|
|
@online{2019icd10cmcms_,
|
|
|
title = {2019 {{ICD-10-CM}} | {{CMS}}},
|
|
|
url = {https://www.cms.gov/Medicare/Coding/ICD10/2019-ICD-10-CM},
|
|
|
urldate = {2023-04-09},
|
|
|
file = {/home/will/Zotero/storage/S5ISTWEL/2019-ICD-10-CM.html}
|
|
|
}
|
|
|
|
|
|
@online{2023icd10cmcms_,
|
|
|
title = {2023 {{ICD-10-CM}} | {{CMS}}},
|
|
|
url = {https://www.cms.gov/medicare/icd-10/2023-icd-10-cm},
|
|
|
urldate = {2023-04-09}
|
|
|
}
|
|
|
|
|
|
@online{2023icd10pcscms_,
|
|
|
title = {2023 {{ICD-10-PCS}} | {{CMS}}},
|
|
|
url = {https://www.cms.gov/medicare/icd-10/2023-icd-10-pcs},
|
|
|
urldate = {2023-04-09},
|
|
|
file = {/home/will/Zotero/storage/4NLQJQT6/2023-icd-10-pcs.html}
|
|
|
}
|
|
|
|
|
|
@online{20240416planworkplansworknotebooks_,
|
|
|
title = {2024-04-16 {{Plan}} - {{Work Plans}} - {{WorkNotebooks}} - {{Collectives}} - {{Nextcloud}}},
|
|
|
url = {https://cloud.thekingfam.com/apps/collectives/WorkNotebooks/Work%20Plans/2024-04-16%20Plan?fileId=44746},
|
|
|
urldate = {2024-04-20},
|
|
|
file = {/home/will/Zotero/storage/SQR8DSHJ/2024-04-16 Plan.html}
|
|
|
}
|
|
|
|
|
|
@article{abrantes-metz_pharmaceuticaldevelopmentphases_2004,
|
|
|
title = {Pharmaceutical {{Development Phases}}: {{A Duration Analysis}}},
|
|
|
shorttitle = {Pharmaceutical {{Development Phases}}},
|
|
|
author = {Abrantes-Metz, Rosa M. and Adams, Christopher and Metz, Albert D.},
|
|
|
date = {2004},
|
|
|
journaltitle = {SSRN Electronic Journal},
|
|
|
shortjournal = {SSRN Journal},
|
|
|
issn = {1556-5068},
|
|
|
doi = {10.2139/ssrn.607941},
|
|
|
url = {http://www.ssrn.com/abstract=607941},
|
|
|
urldate = {2023-01-31},
|
|
|
abstract = {This paper estimates a duration model of late stage drug development in the pharmaceutical industry using publically available data. The paper presents descriptive results on teh estimated relationship between a particular drug's characteristics such as therapy category, route of administration, and originator's size, and that drug's pathway through the three stages of human clinical trials and regulatory review. The results suggest that drugs with longer durations are less likely to succeed, drugs from larger firms are more likely to succeed and faster in the later phase of development, and that durations fell between 1995 and 2002.},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/LANZBC53/Abrantes-Metz et al. - 2004 - Pharmaceutical Development Phases A Duration Anal.pdf}
|
|
|
}
|
|
|
|
|
|
@article{acemoglu_marketsizeinnovation_2004,
|
|
|
title = {{{MARKET SIZE IN INNOVATION}}: {{THEORY AND EVIDENCE FROM THE PHARMACEUTICAL INDUSTRY}}},
|
|
|
author = {Acemoglu, Daron and Linn, Joshua},
|
|
|
date = {2004-08},
|
|
|
journaltitle = {QUARTERLY JOURNAL OF ECONOMICS},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/HYTY3E36/Acemoglu and Linn - MARKET SIZE IN INNOVATION THEORY AND EVIDENCE FRO.pdf}
|
|
|
}
|
|
|
|
|
|
@article{agarwal_whatdrivesinnovation_2022,
|
|
|
title = {What Drives Innovation? {{Lessons}} from {{COVID-19 R}}\&{{D}}},
|
|
|
shorttitle = {What Drives Innovation?},
|
|
|
author = {Agarwal, Ruchir and Gaule, Patrick},
|
|
|
date = {2022-03},
|
|
|
journaltitle = {Journal of Health Economics},
|
|
|
shortjournal = {Journal of Health Economics},
|
|
|
volume = {82},
|
|
|
pages = {102591},
|
|
|
issn = {01676296},
|
|
|
doi = {10.1016/j.jhealeco.2022.102591},
|
|
|
url = {https://linkinghub.elsevier.com/retrieve/pii/S016762962200011X},
|
|
|
urldate = {2023-01-31},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/955FY8K9/Agarwal and Gaule - 2022 - What drives innovation Lessons from COVID-19 R&D.pdf;/home/will/Zotero/storage/IR6EYJXQ/Agarwal and Gaule - 2022 - What drives innovation Lessons from COVID-19 R&D.pdf}
|
|
|
}
|
|
|
|
|
|
@online{anderson_fdadrugapproval_2022,
|
|
|
title = {{{FDA Drug Approval Process}}},
|
|
|
namea = {Anderson, Leigh Ann},
|
|
|
nameatype = {collaborator},
|
|
|
date = {2022-05-28},
|
|
|
url = {https://www.drugs.com/fda-approval-process.html},
|
|
|
urldate = {2023-04-12},
|
|
|
abstract = {It can take up to \$2 billion and 12 to 15 years to get a drug from the test tube to the market. What happens at the FDA to get this drug safely to you?},
|
|
|
langid = {english},
|
|
|
organization = {Drugs.com},
|
|
|
file = {/home/will/Zotero/storage/VTIGXXJB/fda-approval-process.html}
|
|
|
}
|
|
|
|
|
|
@article{anisimov_modellingpredictionadaptive_2007,
|
|
|
title = {Modelling, Prediction and Adaptive Adjustment of Recruitment in Multicentre Trials},
|
|
|
author = {Anisimov, Vladimir V. and Fedorov, Valerii V.},
|
|
|
date = {2007-11-30},
|
|
|
journaltitle = {Statistics in Medicine},
|
|
|
shortjournal = {Statist. Med.},
|
|
|
volume = {26},
|
|
|
number = {27},
|
|
|
pages = {4958--4975},
|
|
|
issn = {02776715, 10970258},
|
|
|
doi = {10.1002/sim.2956},
|
|
|
url = {https://onlinelibrary.wiley.com/doi/10.1002/sim.2956},
|
|
|
urldate = {2023-06-06},
|
|
|
abstract = {This paper is focused on statistical modelling, prediction and adaptive adjustment of patient recruitment in multicentre clinical trials. We consider a recruitment model, where patients arrive at different centres according to Poisson processes, with recruitment rates viewed as a sample from a gamma distribution. A statistical analysis of completed studies is provided and properties of a few types of parameter estimators are investigated analytically and using simulation. The model has been validated using many real completed trials. A statistical technique for predictive recruitment modelling for ongoing trials is developed. It allows the prediction of the remaining recruitment time together with confidence intervals using current enrolment information, and also provision of an adaptive adjustment of recruitment by calculating the number of additional centres required to accomplish a study up to a certain deadline with a pre-specified probability. Results are illustrated for different recruitment scenarios. Copyright q 2007 John Wiley \& Sons, Ltd.},
|
|
|
langid = {english},
|
|
|
keywords = {ClinicalTrials,Enrollment},
|
|
|
file = {/home/will/Zotero/storage/CJJWQZEI/Anisimov and Fedorov - 2007 - Modelling, prediction and adaptive adjustment of r.pdf;/home/will/Zotero/storage/WEU49HLY/Anisimov and Fedorov - 2007 - Modelling, prediction and adaptive adjustment of r.pdf}
|
|
|
}
|
|
|
|
|
|
@misc{applicableclinicaltrialchecklist_,
|
|
|
title = {Applicable {{Clinical Trial Checklist}}},
|
|
|
abstract = {Checklist for Evaluating Whether a Clinical Trial or Study is an Applicable Clinical Trial (ACT)},
|
|
|
organization = {Food and Drug Administration},
|
|
|
file = {/home/will/Zotero/storage/JV8B6QF6/ACT_Checklist.pdf}
|
|
|
}
|
|
|
|
|
|
@book{approveddrugproductstherapeuticequivalence_2022,
|
|
|
title = {{{APPROVED}} {{DRUG}} {{PRODUCTS}} {{WITH THERAPEUTIC}} {{EQUIVALENCE}} {{EVALUATIONS}}},
|
|
|
shorttitle = {{{OrangeBook}}},
|
|
|
date = {2022},
|
|
|
edition = {42},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/ITRKHFI8/_.pdf}
|
|
|
}
|
|
|
|
|
|
@article{avalos-pacheco_validationpredictiveanalyses_2023,
|
|
|
title = {Validation of {{Predictive Analyses}} for {{Interim Decisions}} in {{Clinical Trials}}},
|
|
|
author = {Avalos-Pacheco, Alejandra and Ventz, Steffen and Arfè, Andrea and Alexander, Brian M. and Rahman, Rifaquat and Wen, Patrick Y. and Trippa, Lorenzo},
|
|
|
date = {2023-02},
|
|
|
journaltitle = {JCO precision oncology},
|
|
|
shortjournal = {JCO Precis Oncol},
|
|
|
volume = {7},
|
|
|
eprint = {36848613},
|
|
|
eprinttype = {pmid},
|
|
|
pages = {e2200606},
|
|
|
issn = {2473-4284},
|
|
|
doi = {10.1200/PO.22.00606},
|
|
|
abstract = {PURPOSE: Adaptive clinical trials use algorithms to predict, during the study, patient outcomes and final study results. These predictions trigger interim decisions, such as early discontinuation of the trial, and can change the course of the study. Poor selection of the Prediction Analyses and Interim Decisions (PAID) plan in an adaptive clinical trial can have negative consequences, including the risk of exposing patients to ineffective or toxic treatments. METHODS: We present an approach that leverages data sets from completed trials to evaluate and compare candidate PAIDs using interpretable validation metrics. The goal is to determine whether and how to incorporate predictions into major interim decisions in a clinical trial. Candidate PAIDs can differ in several aspects, such as the prediction models used, timing of interim analyses, and potential use of external data sets. To illustrate our approach, we considered a randomized clinical trial in glioblastoma. The study design includes interim futility analyses on the basis of the predictive probability that the final analysis, at the completion of the study, will provide significant evidence of treatment effects. We examined various PAIDs with different levels of complexity to investigate if the use of biomarkers, external data, or novel algorithms improved interim decisions in the glioblastoma clinical trial. RESULTS: Validation analyses on the basis of completed trials and electronic health records support the selection of algorithms, predictive models, and other aspects of PAIDs for use in adaptive clinical trials. By contrast, PAID evaluations on the basis of arbitrarily defined ad hoc simulation scenarios, which are not tailored to previous clinical data and experience, tend to overvalue complex prediction procedures and produce poor estimates of trial operating characteristics such as power and the number of enrolled patients. CONCLUSION: Validation analyses on the basis of completed trials and real world data support the selection of predictive models, interim analysis rules, and other aspects of PAIDs in future clinical trials.},
|
|
|
langid = {english},
|
|
|
keywords = {Computer Simulation,Electronic Health Records,Glioblastoma,Humans,Randomized Controlled Trials as Topic,Research Design}
|
|
|
}
|
|
|
|
|
|
@article{bieganek_predictionclinicaltrial_2022,
|
|
|
title = {Prediction of Clinical Trial Enrollment Rates},
|
|
|
author = {Bieganek, Cameron and Aliferis, Constantin and Ma, Sisi},
|
|
|
editor = {V E, Sathishkumar},
|
|
|
date = {2022-02-24},
|
|
|
journaltitle = {PLOS ONE},
|
|
|
shortjournal = {PLoS ONE},
|
|
|
volume = {17},
|
|
|
number = {2},
|
|
|
pages = {e0263193},
|
|
|
issn = {1932-6203},
|
|
|
doi = {10.1371/journal.pone.0263193},
|
|
|
url = {https://dx.plos.org/10.1371/journal.pone.0263193},
|
|
|
urldate = {2023-05-02},
|
|
|
abstract = {Clinical trials represent a critical milestone of translational and clinical sciences. However, poor recruitment to clinical trials has been a long standing problem affecting institutions all over the world. One way to reduce the cost incurred by insufficient enrollment is to minimize initiating trials that are most likely to fall short of their enrollment goal. Hence, the ability to predict which proposed trials will meet enrollment goals prior to the start of the trial is highly beneficial. In the current study, we leveraged a data set extracted from ClinicalTrials.gov that consists of 46,724 U.S. based clinical trials from 1990 to 2020. We constructed 4,636 candidate predictors based on data collected by ClinicalTrials.gov and external sources for enrollment rate prediction using various state-of-the-art machine learning methods. Taking advantage of a nested time series cross-validation design, our models resulted in good predictive performance that is generalizable to future data and stable over time. Moreover, information content analysis revealed the study design related features to be the most informative feature type regarding enrollment. Compared to the performance of models built with all features, the performance of models built with study design related features is only marginally worse ( AUC = 0.78 ± 0.03 vs. AUC = 0.76 ± 0.02). The results presented can form the basis for data-driven decision support systems to assess whether proposed clinical trials would likely meet their enrollment goal.},
|
|
|
langid = {english},
|
|
|
keywords = {ClinicalTrials,Enrollment},
|
|
|
file = {/home/will/Zotero/storage/EY66CWRM/Bieganek et al. - 2022 - Prediction of clinical trial enrollment rates.pdf;/home/will/Zotero/storage/IMIATZY6/Bieganek et al. - 2022 - Prediction of clinical trial enrollment rates.pdf}
|
|
|
}
|
|
|
|
|
|
@article{bodenreider_usingsnomedct_,
|
|
|
title = {Using {{SNOMED CT}} with the {{UMLS}}},
|
|
|
author = {Bodenreider, Dr Olivier and Fung, Dr Kin Wah and Willis, Janice H},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/NKYGF5IQ/Bodenreider et al. - Using SNOMED CT with the UMLS.pdf}
|
|
|
}
|
|
|
|
|
|
@article{budish_firmsunderinvestlongterm_2015,
|
|
|
title = {Do {{Firms Underinvest}} in {{Long-Term Research}}? {{Evidence}} from {{Cancer Clinical Trials}}},
|
|
|
shorttitle = {Do {{Firms Underinvest}} in {{Long-Term Research}}?},
|
|
|
author = {Budish, Eric and Roin, Benjamin N. and Williams, Heidi},
|
|
|
date = {2015-07-01},
|
|
|
journaltitle = {American Economic Review},
|
|
|
shortjournal = {American Economic Review},
|
|
|
volume = {105},
|
|
|
number = {7},
|
|
|
pages = {2044--2085},
|
|
|
issn = {0002-8282},
|
|
|
doi = {10.1257/aer.20131176},
|
|
|
url = {https://pubs.aeaweb.org/doi/10.1257/aer.20131176},
|
|
|
urldate = {2023-04-29},
|
|
|
abstract = {We investigate whether private research investments are distorted away from long-term projects. Our theoretical model highlights two potential sources of this distortion: short-termism and the fixed patent term. Our empirical context is cancer research, where clinical trials—and hence, project durations—are shorter for late-stage cancer treatments relative to early-stage treatments or cancer prevention. Using newly constructed data, we document several sources of evidence that together show private research investments are distorted away from long-term projects. The value of life-years at stake appears large. We analyze three potential policy responses: surrogate (non-mortality) clinical-trial endpoints, targeted R\&D subsidies, and patent design. (JEL D92, G31, I11, L65, O31, O34)},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/CH65AXK5/Budish et al. - 2015 - Do Firms Underinvest in Long-Term Research Eviden.pdf;/home/will/Zotero/storage/ECEFIDNV/Budish et al. - 2015 - Do Firms Underinvest in Long-Term Research Eviden.pdf}
|
|
|
}
|
|
|
|
|
|
@article{carter_applicationstochasticprocesses_2004,
|
|
|
title = {Application of Stochastic Processes to Participant Recruitment in Clinical Trials},
|
|
|
author = {Carter, Rickey Edward},
|
|
|
date = {2004-10},
|
|
|
journaltitle = {Controlled Clinical Trials},
|
|
|
shortjournal = {Controlled Clinical Trials},
|
|
|
volume = {25},
|
|
|
number = {5},
|
|
|
pages = {429--436},
|
|
|
issn = {01972456},
|
|
|
doi = {10.1016/j.cct.2004.07.002},
|
|
|
url = {https://linkinghub.elsevier.com/retrieve/pii/S0197245604000522},
|
|
|
urldate = {2023-04-27},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/UHSFPWF8/Carter - 2004 - Application of stochastic processes to participant.pdf}
|
|
|
}
|
|
|
|
|
|
@article{cerda_endogenousinnovationspharmaceutical_2007,
|
|
|
title = {Endogenous Innovations in the Pharmaceutical Industry},
|
|
|
author = {Cerda, Rodrigo A.},
|
|
|
date = {2007-06-27},
|
|
|
journaltitle = {Journal of Evolutionary Economics},
|
|
|
shortjournal = {J Evol Econ},
|
|
|
volume = {17},
|
|
|
number = {4},
|
|
|
pages = {473--515},
|
|
|
issn = {0936-9937, 1432-1386},
|
|
|
doi = {10.1007/s00191-007-0059-3},
|
|
|
url = {http://link.springer.com/10.1007/s00191-007-0059-3},
|
|
|
urldate = {2024-09-06},
|
|
|
abstract = {This paper addresses the creation of new products in the US pharmaceutical sector, during the second half of the 20th century. We indicate that the continuous increases in population, and thus in the market size of this sector, play a fundamental role in explaining the large creation of new drugs during that period. We also argue that population and market size can be endogenously determined through the impact of drugs over the mortality rate. Hence, these two effects reinforce each other, producing decrements in the mortality rate and increments in the stock of drugs over time. We obtained the set of new molecular entities approved by the FDA during the second half of the 20th century and we decomposed the data in a panel of 15 therapeutic categories over time. Using this data, we tested our hypotheses using different econometric methods. The results support the hypothesis and are consistent across methods.},
|
|
|
langid = {english},
|
|
|
keywords = {Instrumental Variables},
|
|
|
file = {/home/will/Zotero/storage/7ZHN8LZ8/Cerda - 2007 - Endogenous innovations in the pharmaceutical industry.pdf}
|
|
|
}
|
|
|
|
|
|
@article{chow_doesenrollmentcancer_2013,
|
|
|
title = {Does {{Enrollment}} in {{Cancer Trials Improve Survival}}?},
|
|
|
author = {Chow, Christopher J. and Habermann, Elizabeth B. and Abraham, Anasooya and Zhu, Yanrong and Vickers, Selwyn M. and Rothenberger, David A. and Al-Refaie, Waddah B.},
|
|
|
date = {2013-04},
|
|
|
journaltitle = {Journal of the American College of Surgeons},
|
|
|
volume = {216},
|
|
|
number = {4},
|
|
|
pages = {774--780},
|
|
|
issn = {1072-7515},
|
|
|
doi = {10.1016/j.jamcollsurg.2012.12.036},
|
|
|
url = {https://journals.lww.com/00019464-201304000-00052},
|
|
|
urldate = {2023-05-03},
|
|
|
langid = {english},
|
|
|
keywords = {Medical},
|
|
|
file = {/home/will/Zotero/storage/JEGZMBN8/Chow et al. - 2013 - Does Enrollment in Cancer Trials Improve Survival.pdf}
|
|
|
}
|
|
|
|
|
|
@online{clinicaltrialsgov_clinicaltrialsgovclinicaltrialsgov_,
|
|
|
title = {About {{ClinicalTrials}}.Gov | {{ClinicalTrials}}.Gov},
|
|
|
author = {{Clinical Trials Gov}},
|
|
|
publisher = {CT},
|
|
|
url = {https://clinicaltrials.gov/about-site/about-ctg},
|
|
|
urldate = {2024-04-19},
|
|
|
file = {/home/will/Zotero/storage/LL7IM8G5/about-ctg.html}
|
|
|
}
|
|
|
|
|
|
@online{clinicaltrialsgov_whyshouldregister_,
|
|
|
title = {Why {{Should I Register}} and {{Submit Results}}? - {{ClinicalTrials}}.Gov},
|
|
|
shorttitle = {Why {{Should I Register}} and {{Submit Results}}?},
|
|
|
author = {{Clinical Trials Gov}},
|
|
|
url = {https://classic.clinicaltrials.gov/ct2/manage-recs/background},
|
|
|
urldate = {2024-04-19},
|
|
|
langid = {english},
|
|
|
keywords = {ClinicalTrials,FederalRegulations},
|
|
|
file = {/home/will/Zotero/storage/69PRARU2/background.html}
|
|
|
}
|
|
|
|
|
|
@online{commissioner_drugdevelopmentprocess_2020,
|
|
|
title = {The {{Drug Development Process}}},
|
|
|
author = {family=Commissioner, given=Office, prefix=of the, useprefix=false},
|
|
|
year = {Thu, 02/20/2020 - 17:28},
|
|
|
publisher = {FDA},
|
|
|
url = {https://www.fda.gov/patients/learn-about-drug-and-device-approvals/drug-development-process},
|
|
|
urldate = {2024-11-05},
|
|
|
abstract = {The Drug Development Process},
|
|
|
langid = {english},
|
|
|
organization = {FDA},
|
|
|
file = {/home/will/Zotero/storage/GYKFHTIT/drug-development-process.html}
|
|
|
}
|
|
|
|
|
|
@article{commissioner_milestonesusfood_2023,
|
|
|
title = {Milestones in {{U}}.{{S}}. {{Food}} and {{Drug Law}}},
|
|
|
author = {family=Commissioner, given=Office, prefix=of the, useprefix=false},
|
|
|
year = {Mon, 01/30/2023 - 11:14},
|
|
|
journaltitle = {FDA},
|
|
|
publisher = {FDA},
|
|
|
url = {https://www.fda.gov/about-fda/fda-history/milestones-us-food-and-drug-law},
|
|
|
urldate = {2024-11-14},
|
|
|
abstract = {Displays important dates that contributed to the History of FDA},
|
|
|
langid = {english},
|
|
|
keywords = {History FDA},
|
|
|
file = {/home/will/Zotero/storage/FQ24Z5MC/milestones-us-food-and-drug-law.html}
|
|
|
}
|
|
|
|
|
|
@dataset{commissioner_nsde_2024,
|
|
|
title = {{{NSDE}}},
|
|
|
shorttitle = {{{NSDE Dataset}}},
|
|
|
author = {{Commissioner}},
|
|
|
year = {Mon, 08/05/2024 - 11:36},
|
|
|
publisher = {FDA},
|
|
|
url = {https://www.fda.gov/industry/structured-product-labeling-resources/nsde},
|
|
|
urldate = {2024-11-14},
|
|
|
abstract = {This web page is to provide for the NSDE file.},
|
|
|
langid = {english},
|
|
|
keywords = {DataSource},
|
|
|
file = {/home/will/Zotero/storage/NKKL7S4Q/nsde.html}
|
|
|
}
|
|
|
|
|
|
@article{commissioner_part1906food_2019,
|
|
|
title = {Part {{I}}: {{The}} 1906 {{Food}} and {{Drugs Act}} and {{Its Enforcement}}},
|
|
|
shorttitle = {Part {{I}}},
|
|
|
author = {family=Commissioner, given=Office, prefix=of the, useprefix=false},
|
|
|
year = {Wed, 04/24/2019 - 13:50},
|
|
|
journaltitle = {FDA},
|
|
|
publisher = {FDA},
|
|
|
url = {https://www.fda.gov/about-fda/changes-science-law-and-regulatory-authorities/part-i-1906-food-and-drugs-act-and-its-enforcement},
|
|
|
urldate = {2024-11-14},
|
|
|
abstract = {Continuing information on the History of FDA which includes the securing of the 1906 Food and Drugs Act.},
|
|
|
langid = {english},
|
|
|
keywords = {History FDA},
|
|
|
file = {/home/will/Zotero/storage/GAZ5LNB8/part-i-1906-food-and-drugs-act-and-its-enforcement.html}
|
|
|
}
|
|
|
|
|
|
@article{commissioner_partii1938_2019,
|
|
|
title = {Part {{II}}: 1938, {{Food}}, {{Drug}}, {{Cosmetic Act}}},
|
|
|
shorttitle = {Part {{II}}},
|
|
|
author = {family=Commissioner, given=Office, prefix=of the, useprefix=false},
|
|
|
year = {Fri, 03/15/2019 - 16:25},
|
|
|
journaltitle = {FDA},
|
|
|
publisher = {FDA},
|
|
|
url = {https://www.fda.gov/about-fda/changes-science-law-and-regulatory-authorities/part-ii-1938-food-drug-cosmetic-act},
|
|
|
urldate = {2024-11-14},
|
|
|
abstract = {Information about securing the 1938 Food, Drug, and Cosmetic Act},
|
|
|
langid = {english},
|
|
|
keywords = {History FDA},
|
|
|
file = {/home/will/Zotero/storage/H68VQ7US/part-ii-1938-food-drug-cosmetic-act.html}
|
|
|
}
|
|
|
|
|
|
@online{commissioner_understandingunapproveduse_2019,
|
|
|
title = {Understanding {{Unapproved Use}} of {{Approved Drugs}} "{{Off Label}}"},
|
|
|
author = {family=Commissioner, given=Office, prefix=of the, useprefix=false},
|
|
|
year = {Thu, 04/18/2019 - 00:30},
|
|
|
publisher = {FDA},
|
|
|
url = {https://www.fda.gov/patients/learn-about-expanded-access-and-other-treatment-options/understanding-unapproved-use-approved-drugs-label},
|
|
|
urldate = {2023-04-10},
|
|
|
abstract = {Understanding Unapproved Use of Approved Drugs "Off Label"},
|
|
|
langid = {english},
|
|
|
organization = {FDA},
|
|
|
file = {/home/will/Zotero/storage/VAKSGTAP/understanding-unapproved-use-approved-drugs-label.html}
|
|
|
}
|
|
|
|
|
|
@dataset{ctti_aact_2022,
|
|
|
title = {Aggregate {{Analysis}} of {{ClinicalTrials}}.Gov ({{AACT}}) {{Database}}},
|
|
|
author = {{Clinical Trials Transformation Initiative (CTTI}},
|
|
|
date = {2022},
|
|
|
location = {https://aact.ctti-clinicaltrials.org}
|
|
|
}
|
|
|
|
|
|
@article{deng_bayesianmodelingprediction_2017,
|
|
|
title = {Bayesian Modeling and Prediction of Accrual in Multi-Regional Clinical Trials},
|
|
|
author = {Deng, Yi and Zhang, Xiaoxi and Long, Qi},
|
|
|
date = {2017-04},
|
|
|
journaltitle = {Statistical Methods in Medical Research},
|
|
|
shortjournal = {Stat Methods Med Res},
|
|
|
volume = {26},
|
|
|
number = {2},
|
|
|
pages = {752--765},
|
|
|
issn = {0962-2802, 1477-0334},
|
|
|
doi = {10.1177/0962280214557581},
|
|
|
url = {http://journals.sagepub.com/doi/10.1177/0962280214557581},
|
|
|
urldate = {2023-04-27},
|
|
|
abstract = {In multi-regional trials, the underlying overall and region-specific accrual rates often do not hold constant over time and different regions could have different start-up times, which combined with initial jump in accrual within each region often leads to a discontinuous overall accrual rate, and these issues associated with multi-regional trials have not been adequately investigated. In this paper, we clarify the implication of the multi-regional nature on modeling and prediction of accrual in clinical trials and investigate a Bayesian approach for accrual modeling and prediction, which models region-specific accrual using a nonhomogeneous Poisson process and allows the underlying Poisson rate in each region to vary over time. The proposed approach can accommodate staggered start-up times and different initial accrual rates across regions/centers. Our numerical studies show that the proposed method improves accuracy and precision of accrual prediction compared to existing methods including the nonhomogeneous Poisson process model that does not model region-specific accrual.},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/MN63YC4H/Deng et al. - 2017 - Bayesian modeling and prediction of accrual in mul.pdf}
|
|
|
}
|
|
|
|
|
|
@article{dimasi_trendsrisksassociated_2010,
|
|
|
title = {Trends in {{Risks Associated With New Drug Development}}: {{Success Rates}} for {{Investigational Drugs}}},
|
|
|
shorttitle = {Trends in {{Risks Associated With New Drug Development}}},
|
|
|
author = {DiMasi, J A and Feldman, L and Seckler, A and Wilson, A},
|
|
|
date = {2010-03},
|
|
|
journaltitle = {Clinical Pharmacology \& Therapeutics},
|
|
|
shortjournal = {Clin Pharmacol Ther},
|
|
|
volume = {87},
|
|
|
number = {3},
|
|
|
pages = {272--277},
|
|
|
issn = {0009-9236, 1532-6535},
|
|
|
doi = {10.1038/clpt.2009.295},
|
|
|
url = {http://doi.wiley.com/10.1038/clpt.2009.295},
|
|
|
urldate = {2024-09-04},
|
|
|
keywords = {To Read},
|
|
|
file = {/home/will/Zotero/storage/H8WG9QXM/DiMasi et al. - 2010 - Trends in Risks Associated With New Drug Development Success Rates for Investigational Drugs.pdf}
|
|
|
}
|
|
|
|
|
|
@article{dimasi_valueimprovingproductivity_2002,
|
|
|
title = {The {{Value}} of {{Improving}} the {{Productivity}} of the {{Drug Development Process}}: {{Faster Times}} and {{Better Decisions}}},
|
|
|
shorttitle = {The {{Value}} of {{Improving}} the {{Productivity}} of the {{Drug Development Process}}},
|
|
|
author = {DiMasi, Joseph A.},
|
|
|
date = {2002},
|
|
|
journaltitle = {PharmacoEconomics},
|
|
|
shortjournal = {PharmacoEconomics},
|
|
|
volume = {20},
|
|
|
pages = {1--10},
|
|
|
issn = {1170-7690},
|
|
|
doi = {10.2165/00019053-200220003-00001},
|
|
|
url = {http://link.springer.com/10.2165/00019053-200220003-00001},
|
|
|
urldate = {2024-10-11},
|
|
|
issue = {Supplement 3},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/A58VSSFF/00019053-200220003-00001.pdf}
|
|
|
}
|
|
|
|
|
|
@article{downing_clinicaltrialevidence_2014,
|
|
|
title = {Clinical {{Trial Evidence Supporting FDA Approval}} of {{Novel Therapeutic Agents}}, 2005-2012},
|
|
|
author = {Downing, Nicholas S. and Aminawung, Jenerius A. and Shah, Nilay D. and Krumholz, Harlan M. and Ross, Joseph S.},
|
|
|
date = {2014-01-22},
|
|
|
journaltitle = {JAMA},
|
|
|
shortjournal = {JAMA},
|
|
|
volume = {311},
|
|
|
number = {4},
|
|
|
pages = {368},
|
|
|
issn = {0098-7484},
|
|
|
doi = {10.1001/jama.2013.282034},
|
|
|
url = {http://jama.jamanetwork.com/article.aspx?doi=10.1001/jama.2013.282034},
|
|
|
urldate = {2023-05-03},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/BIF9BFB2/Downing et al. - 2014 - Clinical Trial Evidence Supporting FDA Approval of.pdf}
|
|
|
}
|
|
|
|
|
|
@article{dranove_doesconsumerdemand_2022,
|
|
|
title = {Does Consumer Demand Pull Scientifically Novel Drug Innovation?},
|
|
|
author = {Dranove, David and Garthwaite, Craig and Hermosilla, Manuel},
|
|
|
date = {2022-09},
|
|
|
journaltitle = {The RAND Journal of Economics},
|
|
|
shortjournal = {The RAND J of Economics},
|
|
|
volume = {53},
|
|
|
number = {3},
|
|
|
pages = {590--638},
|
|
|
issn = {0741-6261, 1756-2171},
|
|
|
doi = {10.1111/1756-2171.12422},
|
|
|
url = {https://onlinelibrary.wiley.com/doi/10.1111/1756-2171.12422},
|
|
|
urldate = {2023-01-31},
|
|
|
abstract = {Prior literature shows that stronger consumer demand leads to increased pharmaceutical R\&D. However, how strong these “demand-pull” effects are for more scientifically novel drug innovation remains unknown. We address this question using comprehensive clinical trial data that include precise characterizations of the scientific approaches used in tested molecules. We characterize scientific novelty as the number of times each approach has been used in the past. Exploiting exogenous demand variation introduced by the introduction of Medicare Part D, we find strong evidence that demand-pull effects are markedly skewed in favor of non-novel or “follow-on” drug R\&D.},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/TKAM758Q/The RAND J of Economics - 2022 - Dranove - Does consumer demand pull scientifically novel drug innovation.pdf}
|
|
|
}
|
|
|
|
|
|
@online{drugdevelopmentfailurehowglp1_,
|
|
|
title = {Drug {{Development Failure}}: How {{GLP-1}} Development Was Abandoned in 1990 | {{Hacker News}}},
|
|
|
url = {https://news.ycombinator.com/item?id=41402418},
|
|
|
urldate = {2024-09-18},
|
|
|
keywords = {To Process},
|
|
|
file = {/home/will/Zotero/storage/ID8UNMA4/item.html}
|
|
|
}
|
|
|
|
|
|
@article{dubois_marketsizepharmaceutical_2015,
|
|
|
title = {Market Size and Pharmaceutical Innovation},
|
|
|
author = {Dubois, Pierre and De Mouzon, Olivier and Scott‐Morton, Fiona and Seabright, Paul},
|
|
|
date = {2015-10},
|
|
|
journaltitle = {The RAND Journal of Economics},
|
|
|
shortjournal = {The RAND J of Economics},
|
|
|
volume = {46},
|
|
|
number = {4},
|
|
|
pages = {844--871},
|
|
|
issn = {0741-6261, 1756-2171},
|
|
|
doi = {10.1111/1756-2171.12113},
|
|
|
url = {https://onlinelibrary.wiley.com/doi/10.1111/1756-2171.12113},
|
|
|
urldate = {2024-09-06},
|
|
|
abstract = {This article quantifies the relationship between market size and innovation in the pharmaceutical industry using improved, and newer, methods and data. We find significant elasticities of innovation to expected market size with a point estimate under our preferred specification of 0.23. This suggests that, on average, \$ 2.5 billion is required in additional revenue to support the invention of one new chemical entity. This magnitude is plausible given recent accounting estimates of the cost of innovation of \$ 800 million to \$ 1 billion per drug, and marginal costs of manufacture and distribution near 50\%.},
|
|
|
langid = {english},
|
|
|
keywords = {To Process},
|
|
|
file = {/home/will/Zotero/storage/IG37VET3/The RAND J of Economics - 2015 - Dubois - Market size and pharmaceutical innovation.pdf;/home/will/Zotero/storage/PH5VBVAW/Dubois et al. - 2015 - Market size and pharmaceutical innovation.pdf}
|
|
|
}
|
|
|
|
|
|
@legislation{federalregister_clinicaltrialsregistration_2016,
|
|
|
title = {Clinical {{Trials Registration}} and {{Results Information Submission}}},
|
|
|
author = {{Federal Register}},
|
|
|
date = {2016-09-21},
|
|
|
journaltitle = {81 FR 64982},
|
|
|
pages = {64982},
|
|
|
publisher = {81FR},
|
|
|
url = {https://www.federalregister.gov/documents/2016/09/21/2016-22129/clinical-trials-registration-and-results-information-submission},
|
|
|
urldate = {2024-04-19},
|
|
|
abstract = {This final rule details the requirements for submitting registration and summary results information, including adverse event information, for specified clinical trials of drug products (including biological products) and device products and for pediatric postmarket surveillances of a device...},
|
|
|
langid = {english},
|
|
|
keywords = {FederalRegulations},
|
|
|
file = {/home/will/Zotero/storage/R6RRX2MV/clinical-trials-registration-and-results-information-submission.html}
|
|
|
}
|
|
|
|
|
|
@article{flier_drugdevelopmentfailure_2024,
|
|
|
title = {Drug {{Development Failure}}: How {{GLP-1}} Development Was Abandoned in 1990},
|
|
|
shorttitle = {Drug {{Development Failure}}},
|
|
|
author = {Flier, Jeffrey},
|
|
|
date = {2024-08},
|
|
|
journaltitle = {Perspectives in Biology and Medicine},
|
|
|
shortjournal = {pbm},
|
|
|
issn = {1529-8795},
|
|
|
doi = {10.1353/pbm.0.a936036},
|
|
|
url = {https://muse.jhu.edu/article/936036},
|
|
|
urldate = {2024-08-30},
|
|
|
abstract = {Many factors determine whether and when a class of therapeutic agents will be successfully developed and brought to market, and historians of science, entrepreneurs, drug developers, and clinicians should be interested in accounts of both successes and failures. Successes induce many participants and observers to document them, whereas failed efforts are often lost to history, in part because involved parties are typically unmotivated to document their failures. The GLP-1 class of drugs for diabetes and obesity have emerged over the past decade as clinical and financial blockbusters, perhaps soon becoming the highest single source of revenue for the pharmaceutical industry (Berk 2023). In that context, it is instructive to tell the story of the first commercial effort to develop this class of drugs for metabolic disease, and how, despite remarkable early success, the work was abandoned in 1990. Told by a key participant in the effort, this story documents history that would otherwise be lost and suggests a number of lessons about drug development that remain relevant today.},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/27RH8CJ6/Flier - 2024 - Drug Development Failure how GLP-1 development was abandoned in 1990.pdf}
|
|
|
}
|
|
|
|
|
|
@online{frequentlyaskedquestionsclinicaltrialsgov_,
|
|
|
type = {Government},
|
|
|
title = {Frequently {{Asked Questions}} - {{ClinicalTrials}}.Gov},
|
|
|
author = {{U.S. National Library of Medicine}},
|
|
|
url = {https://clinicaltrials.gov/ct2/manage-recs/faq#board},
|
|
|
urldate = {2023-04-08},
|
|
|
langid = {english},
|
|
|
organization = {ClinicalTrials.gov},
|
|
|
file = {/home/will/Zotero/storage/GNBZDX5B/faq.html}
|
|
|
}
|
|
|
|
|
|
@article{gelman_multilevelhierarchicalmodeling_2006,
|
|
|
title = {Multilevel ({{Hierarchical}}) {{Modeling}}: {{What It Can}} and {{Cannot Do}}},
|
|
|
shorttitle = {Multilevel ({{Hierarchical}}) {{Modeling}}},
|
|
|
author = {Gelman, Andrew},
|
|
|
date = {2006-08},
|
|
|
journaltitle = {Technometrics},
|
|
|
shortjournal = {Technometrics},
|
|
|
volume = {48},
|
|
|
number = {3},
|
|
|
pages = {432--435},
|
|
|
issn = {0040-1706, 1537-2723},
|
|
|
doi = {10.1198/004017005000000661},
|
|
|
url = {http://www.tandfonline.com/doi/abs/10.1198/004017005000000661},
|
|
|
urldate = {2023-08-02},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/C7JLP9A9/Gelman - 2006 - Multilevel (Hierarchical) Modeling What It Can an.pdf}
|
|
|
}
|
|
|
|
|
|
@report{globalburdenofdiseasecollaborativenetwork_globalburdendisease_2020,
|
|
|
title = {Global {{Burden}} of {{Disease Study}} 2019 ({{GBD}} 2019) {{Cause Hierarchy}}},
|
|
|
author = {Global Burden of Disease Collaborative Network},
|
|
|
date = {2020},
|
|
|
institution = {{nstitute for Health Metrics and Evaluation (IHME)}},
|
|
|
location = {Seattle, United States of America},
|
|
|
abstract = {Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Cause Hierarchy Seattle, United States of America: Institute for Health Metrics and Evaluation (IHME), 2020.}
|
|
|
}
|
|
|
|
|
|
@report{globalburdenofdiseasecollaborativenetwork_globalburdendisease_2020a,
|
|
|
title = {Global {{Burden}} of {{Disease Study}} 2019 ({{GBD}} 2019) {{Cause List Mapped}} to {{ICD Codes}}},
|
|
|
author = {Global Burden of Disease Collaborative Network},
|
|
|
date = {2020},
|
|
|
institution = {{nstitute for Health Metrics and Evaluation (IHME)}},
|
|
|
location = {Seattle, United States of America},
|
|
|
abstract = {Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Cause List Mapped to ICD Codes. Seattle, United States of America: Institute for Health Metrics and Evaluation (IHME), 2020.},
|
|
|
file = {/home/will/Zotero/storage/6P4A94JR/IHME_GBD_2019_NONFATAL_CAUSE_ICD_CODE_MAP_Y2020M10D15.XLSX;/home/will/Zotero/storage/CVXNRJRD/Global Burden of Disease Study 2019 (GBD 2019) Cau.PDF;/home/will/Zotero/storage/TBSL7VCZ/IHME_GBD_2019_COD_CAUSE_ICD_CODE_MAP_Y2020M10D15.XLSX;/home/will/Zotero/storage/VELUT993/IHME_GBD_2019_COD_CAUSE_ICD_CODE_MAP_Y2020M10D15.XLSX;/home/will/Zotero/storage/YDTPP4YP/IHME_GBD_2019_NONFATAL_CAUSE_ICD_CODE_MAP_Y2020M10D15.XLSX}
|
|
|
}
|
|
|
|
|
|
@misc{globalburdentdiseasestudy2019_2022,
|
|
|
title = {Global {{Burdent}} of {{Disease Study}} 2019 ({{GBD}} 2019) {{Results}}},
|
|
|
date = {2022},
|
|
|
url = {https://vizhub.healthdata.org/gbd-results/},
|
|
|
organization = {Seattle, United States: Institute for Health Metrics and Evaluation (IHME), University of Washington}
|
|
|
}
|
|
|
|
|
|
@incollection{goldman_intellectualpropertyinformation_2011,
|
|
|
title = {Intellectual {{Property}}, {{Information Technology}}, {{Biomedical Research}}, and {{Marketing}} of {{Patented Products}}},
|
|
|
booktitle = {Handbook of {{Health Economics}}},
|
|
|
author = {Goldman, Dana and Lakdawalla, Darius},
|
|
|
date = {2011},
|
|
|
volume = {2},
|
|
|
pages = {825--872},
|
|
|
publisher = {Elsevier},
|
|
|
doi = {10.1016/B978-0-444-53592-4.00013-X},
|
|
|
url = {https://linkinghub.elsevier.com/retrieve/pii/B978044453592400013X},
|
|
|
urldate = {2023-01-31},
|
|
|
abstract = {Intellectual property rights are viewed as essential to medical innovation, but very often involve social costs due to patent monopolies and other inefficiencies. We review the positive theory of innovation in health care, as it relates to the determination of innovation demand and supply. The positive theory is related to a host of competing normative models of intellectual property, including patent races, cumulative or sequential innovation, and the implications of health insurance. We also discuss how intellectual property can be used to solve a variety of production externalities that afflict health care, including network externalities, underprovision of marketing, and inefficient provision of diagnostic information. Finally, we discuss novel approaches to protecting intellectual property, including rewards, innovation subsidies, and publicly provided health insurance.},
|
|
|
isbn = {978-0-444-53592-4},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/G7YI26JA/Goldman and Lakdawalla - 2011 - Intellectual Property, Information Technology, Bio.pdf;/home/will/Zotero/storage/QMZTAPML/Goldman and Lakdawalla - 2011 - Intellectual Property, Information Technology, Bio.pdf}
|
|
|
}
|
|
|
|
|
|
@article{gupta_oneproductmany_2020,
|
|
|
title = {One Product, Many Patents: {{Imperfect}} Intellectual Property Rights in the Pharmaceutical Industry},
|
|
|
shorttitle = {One Product, Many Patents},
|
|
|
author = {Gupta, Charu},
|
|
|
date = {2020},
|
|
|
journaltitle = {SSRN Electronic Journal},
|
|
|
shortjournal = {SSRN Journal},
|
|
|
issn = {1556-5068},
|
|
|
doi = {10.2139/ssrn.3748158},
|
|
|
url = {https://www.ssrn.com/abstract=3748158},
|
|
|
urldate = {2023-01-31},
|
|
|
abstract = {Economists’ standard notion of intellectual property rights considers a single patent per product, with a clearly defined scope, certain enforcement, and a fixed term of monopoly protection. Yet common across industries are “imperfect” intellectual property rights: More than one patent may cover a single product, with the scope and enforcement of each uncertain, contributing to an indeterminate period of monopoly protection. Using data on the pharmaceutical industry, I systematically document the presence of imperfect intellectual property rights at the product level and provide the first evidence on the extent to which they impact competition. In a sample of novel drugs, I show that roughly 70 percent of drugs are covered by multiple intellectual property rights. I offer evidence on two mechanisms by which the accumulation of such rights for a single drug may delay generic entry: by introducing a binding later patent expiration and by increasing uncertainty in the scope and enforceability of remaining patents. In an instrumental variables analysis, I determine that the accumulation of patents for a single drug product delays generic entry by over 4 years per drug (amounting to more than 30 percent of mean monopoly life), well beyond the expiration of the drug’s initial molecule patent. This research suggests large consequences for consumer welfare in terms of drug pricing and new molecule development and offers an important nuance for future work on optimal patent policy and innovation—that intellectual property rights are less rigid than we typically assume.},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/RPH7E2VB/Gupta-ImperfectIP-v20231113.pdf;/home/will/Zotero/storage/TLXPJ5LJ/Gupta - 2020 - One product, many patents Imperfect intellectual .pdf;/home/will/Zotero/storage/XINYUULK/Gupta-ImperfectIP-v20230128.pdf}
|
|
|
}
|
|
|
|
|
|
@article{hay_clinicaldevelopmentsuccess_2014,
|
|
|
title = {Clinical Development Success Rates for Investigational Drugs},
|
|
|
author = {Hay, Michael and Thomas, David W and Craighead, John L and Economides, Celia and Rosenthal, Jesse},
|
|
|
date = {2014-01},
|
|
|
journaltitle = {Nature Biotechnology},
|
|
|
shortjournal = {Nat Biotechnol},
|
|
|
volume = {32},
|
|
|
number = {1},
|
|
|
pages = {40--51},
|
|
|
issn = {1087-0156, 1546-1696},
|
|
|
doi = {10.1038/nbt.2786},
|
|
|
url = {https://www.nature.com/articles/nbt.2786},
|
|
|
urldate = {2024-09-04},
|
|
|
langid = {english},
|
|
|
keywords = {To Read},
|
|
|
file = {/home/will/Zotero/storage/LDI72VNY/Hay et al. - 2014 - Clinical development success rates for investigational drugs.pdf}
|
|
|
}
|
|
|
|
|
|
@article{heitjan_realtimepredictionclinical_2015,
|
|
|
title = {Real-Time Prediction of Clinical Trial Enrollment and Event Counts: {{A}} Review},
|
|
|
shorttitle = {Real-Time Prediction of Clinical Trial Enrollment and Event Counts},
|
|
|
author = {Heitjan, Daniel F. and Ge, Zhiyun and Ying, Gui-shuang},
|
|
|
date = {2015-11},
|
|
|
journaltitle = {Contemporary Clinical Trials},
|
|
|
shortjournal = {Contemporary Clinical Trials},
|
|
|
volume = {45},
|
|
|
pages = {26--33},
|
|
|
issn = {15517144},
|
|
|
doi = {10.1016/j.cct.2015.07.010},
|
|
|
url = {https://linkinghub.elsevier.com/retrieve/pii/S1551714415300483},
|
|
|
urldate = {2023-05-02},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/7U9LNLKM/Heitjan et al. - 2015 - Real-time prediction of clinical trial enrollment .pdf;/home/will/Zotero/storage/JAN23W5G/Heitjan et al. - 2015 - Real-time prediction of clinical trial enrollment .pdf}
|
|
|
}
|
|
|
|
|
|
@article{hwang_failureinvestigationaldrugs_2016,
|
|
|
title = {Failure of {{Investigational Drugs}} in {{Late-Stage Clinical Development}} and {{Publication}} of {{Trial Results}}},
|
|
|
author = {Hwang, Thomas J. and Carpenter, Daniel and Lauffenburger, Julie C. and Wang, Bo and Franklin, Jessica M. and Kesselheim, Aaron S.},
|
|
|
date = {2016-12-01},
|
|
|
journaltitle = {JAMA Internal Medicine},
|
|
|
shortjournal = {JAMA Intern Med},
|
|
|
volume = {176},
|
|
|
number = {12},
|
|
|
pages = {1826},
|
|
|
issn = {2168-6106},
|
|
|
doi = {10.1001/jamainternmed.2016.6008},
|
|
|
url = {http://archinte.jamanetwork.com/article.aspx?doi=10.1001/jamainternmed.2016.6008},
|
|
|
urldate = {2023-01-31},
|
|
|
abstract = {OBJECTIVE To assess factors associated with regulatory approval or reasons for failure of investigational therapeutics in phase 3 or pivotal trials and rates of publication of trial results. DESIGN, SETTING, AND PARTICIPANTS Using public sources and commercial databases, we identified investigational therapeutics that entered pivotal trials between 1998 and 2008, with follow-up through 2015. Agents were classified by therapeutic area, orphan designation status, fast track designation, novelty of biological pathway, company size, and as a pharmacologic or biologic product. MAIN OUTCOMES AND MEASURES For each product, we identified reasons for failure (efficacy, safety, commercial) and assessed the rates of publication of trial results. We used multivariable logistic regression models to evaluate factors associated with regulatory approval. RESULTS Among 640 novel therapeutics, 344 (54\%) failed in clinical development, 230 (36\%) were approved by the US Food and Drug Administration (FDA), and 66 (10\%) were approved in other countries but not by the FDA. Most products failed due to inadequate efficacy (n = 195; 57\%), while 59 (17\%) failed because of safety concerns and 74 (22\%) failed due to commercial reasons. The pivotal trial results were published in peer-reviewed journals for 138 of the 344 (40\%) failed agents. Of 74 trials for agents that failed for commercial reasons, only 6 (8.1\%) were published. In analyses adjusted for therapeutic area, agent type, firm size, orphan designation, fast-track status, trial year, and novelty of biological pathway, orphan-designated drugs were significantly more likely than nonorphan drugs to be approved (46\% vs 34\%; adjusted odds ratio [aOR], 2.3; 95\% CI, 1.4-3.7). Cancer drugs (27\% vs 39\%; aOR, 0.5; 95\% CI, 0.3-0.9) and agents sponsored by small and medium-size companies (28\% vs 42\%; aOR, 0.4; 95\% CI, 0.3-0.7) were significantly less likely to be approved. CONCLUSIONS AND RELEVANCE Roughly half of investigational drugs entering late-stage clinical development fail during or after pivotal clinical trials, primarily because of concerns about safety, efficacy, or both. Results for the majority of studies of investigational drugs that fail are not published in peer-reviewed journals.},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/9399MLSQ/Hwang et al. - 2016 - Failure of Investigational Drugs in Late-Stage Cli.pdf;/home/will/Zotero/storage/JJC96CPC/Hwang et al. - 2016 - Failure of Investigational Drugs in Late-Stage Cli.pdf}
|
|
|
}
|
|
|
|
|
|
@online{icd10version2019_,
|
|
|
title = {{{ICD-10 Version}}:2019},
|
|
|
url = {https://icd.who.int/browse10/2019/en#/C00},
|
|
|
urldate = {2023-04-10},
|
|
|
file = {/home/will/Zotero/storage/23DGMZ5X/en.html}
|
|
|
}
|
|
|
|
|
|
@misc{interviewadamgeorge_2023,
|
|
|
title = {Interview with {{Adam George}}.},
|
|
|
date = {2023-05-05}
|
|
|
}
|
|
|
|
|
|
@article{jayasundara_riskfailureclinical_2012,
|
|
|
title = {Risk of {{Failure}} of a {{Clinical Drug Trial}} in {{Patients}} with {{Moderate}} to {{Severe Rheumatoid Arthritis}}},
|
|
|
author = {Jayasundara, Kavisha S. and Keystone, Edward C. and Parker, Jayson L.},
|
|
|
date = {2012-11},
|
|
|
journaltitle = {The Journal of Rheumatology},
|
|
|
shortjournal = {J Rheumatol},
|
|
|
volume = {39},
|
|
|
number = {11},
|
|
|
pages = {2066--2070},
|
|
|
issn = {0315-162X, 1499-2752},
|
|
|
doi = {10.3899/jrheum.120005},
|
|
|
url = {http://www.jrheum.org/lookup/doi/10.3899/jrheum.120005},
|
|
|
urldate = {2023-05-03},
|
|
|
abstract = {Objective. We conducted a systematic review to determine the risk of drug failure in clinical testing with patients with moderate to severe rheumatoid arthritis (RA). Methods. Therapies for RA were investigated by reviewing phase I to phase III studies conducted from December 1998 to March 2011. Clinical trial success rates were calculated and compared to industry standards. Trial failures were classified as either commercial or clinical failures. The exclusion criteria for drugs in this study: drugs that were started in phase I studies prior to January 1998 for this indication; or studies that enrolled patients who were methotrexate-naive and/or had failed biologic therapy. Results. A search in clinicaltrials.gov and approved drugs for the indication yielded a total of 69 drugs that met the study criteria. The cumulative success rate was determined to be 16\%, which is equivalent to the industry standard of 16\%. For each phase, the frequency of clinical failures exceeded commercial failures. Clinical studies equally comprised investigations of small molecules and biological agents, but biologics seemed to exhibit a higher success rate overall. Conclusion. Clinical trial risk in RA with the 84\% failure rate reported here is at par with industry performance and phase II success rate seems to be highly predictive of phase III success.},
|
|
|
langid = {english}
|
|
|
}
|
|
|
|
|
|
@article{jayasundara_riskfailureclinical_2012a,
|
|
|
title = {Risk of {{Failure}} of a {{Clinical Drug Trial}} in {{Patients}} with {{Moderate}} to {{Severe Rheumatoid Arthritis}}},
|
|
|
author = {Jayasundara, Kavisha S. and Keystone, Edward C. and Parker, Jayson L.},
|
|
|
date = {2012-11},
|
|
|
journaltitle = {The Journal of Rheumatology},
|
|
|
shortjournal = {J Rheumatol},
|
|
|
volume = {39},
|
|
|
number = {11},
|
|
|
pages = {2066--2070},
|
|
|
issn = {0315-162X, 1499-2752},
|
|
|
doi = {10.3899/jrheum.120005},
|
|
|
url = {http://www.jrheum.org/lookup/doi/10.3899/jrheum.120005},
|
|
|
urldate = {2025-01-18},
|
|
|
abstract = {Methods. Therapies for RA were investigated by reviewing phase I to phase III studies conducted from December 1998 to March 2011. Clinical trial success rates were calculated and compared to industry standards. Trial failures were classified as either commercial or clinical failures. The exclusion criteria for drugs in this study: drugs that were started in phase I studies prior to January 1998 for this indication; or studies that enrolled patients who were methotrexate-naive and/or had failed biologic therapy. Results. A search in clinicaltrials.gov and approved drugs for the indication yielded a total of 69 drugs that met the study criteria. The cumulative success rate was determined to be 16\%, which is equivalent to the industry standard of 16\%. For each phase, the frequency of clinical failures exceeded commercial failures. Clinical studies equally comprised investigations of small molecules and biological agents, but biologics seemed to exhibit a higher success rate overall. Conclusion. Clinical trial risk in RA with the 84\% failure rate reported here is at par with industry performance and phase II success rate seems to be highly predictive of phase III success. (First Release Sept 1 2012; J Rheumatol 2012;39:2066–70; doi:10.3899/jrheum.120005)},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/8ZKILFN3/Jayasundara et al. - 2012 - Risk of Failure of a Clinical Drug Trial in Patients with Moderate to Severe Rheumatoid Arthritis.pdf}
|
|
|
}
|
|
|
|
|
|
@article{jiang_modelingvalidatingbayesian_2015,
|
|
|
title = {Modeling and Validating {{Bayesian}} Accrual Models on Clinical Data and Simulations Using Adaptive Priors},
|
|
|
author = {Jiang, Yu and Simon, Steve and Mayo, Matthew S. and Gajewski, Byron J.},
|
|
|
date = {2015-02-20},
|
|
|
journaltitle = {Statistics in Medicine},
|
|
|
shortjournal = {Statist. Med.},
|
|
|
volume = {34},
|
|
|
number = {4},
|
|
|
pages = {613--629},
|
|
|
issn = {02776715},
|
|
|
doi = {10.1002/sim.6359},
|
|
|
url = {https://onlinelibrary.wiley.com/doi/10.1002/sim.6359},
|
|
|
urldate = {2023-04-27},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/JJAKCXZN/Jiang et al. - 2015 - Modeling and validating Bayesian accrual models on.pdf}
|
|
|
}
|
|
|
|
|
|
@article{kasenda_prevalencecharacteristicspublication_2014,
|
|
|
title = {Prevalence, {{Characteristics}}, and {{Publication}} of {{Discontinued Randomized Trials}}},
|
|
|
author = {Kasenda, Benjamin and Von Elm, Erik and You, John and Blümle, Anette and Tomonaga, Yuki and Saccilotto, Ramon and Amstutz, Alain and Bengough, Theresa and Meerpohl, Joerg J. and Stegert, Mihaela and Tikkinen, Kari A. O. and Neumann, Ignacio and Carrasco-Labra, Alonso and Faulhaber, Markus and Mulla, Sohail M. and Mertz, Dominik and Akl, Elie A. and Bassler, Dirk and Busse, Jason W. and Ferreira-González, Ignacio and Lamontagne, Francois and Nordmann, Alain and Gloy, Viktoria and Raatz, Heike and Moja, Lorenzo and Rosenthal, Rachel and Ebrahim, Shanil and Schandelmaier, Stefan and Xin, Sun and Vandvik, Per O. and Johnston, Bradley C. and Walter, Martin A. and Burnand, Bernard and Schwenkglenks, Matthias and Hemkens, Lars G. and Bucher, Heiner C. and Guyatt, Gordon H. and Briel, Matthias},
|
|
|
date = {2014-03-12},
|
|
|
journaltitle = {JAMA},
|
|
|
shortjournal = {JAMA},
|
|
|
volume = {311},
|
|
|
number = {10},
|
|
|
pages = {1045},
|
|
|
issn = {0098-7484},
|
|
|
doi = {10.1001/jama.2014.1361},
|
|
|
url = {http://jama.jamanetwork.com/article.aspx?doi=10.1001/jama.2014.1361},
|
|
|
urldate = {2023-06-06},
|
|
|
abstract = {OBJECTIVES To determine the prevalence, characteristics, and publication history of discontinued RCTs and to investigate factors associated with RCT discontinuation due to poor recruitment and with nonpublication. DESIGN AND SETTING Retrospective cohort of RCTs based on archived protocols approved by 6 research ethics committees in Switzerland, Germany, and Canada between 2000 and 2003. We recorded trial characteristics and planned recruitment from included protocols. Last follow-up of RCTs was April 27, 2013. Editorial page 1019 Related articles pages 1063 and 1065 Supplemental content at jama.com MAIN OUTCOMES AND MEASURES Completion status, reported reasons for discontinuation, and publication status of RCTs as determined by correspondence with the research ethics committees, literature searches, and investigator surveys. RESULTS After a median follow-up of 11.6 years (range, 8.8-12.6 years), 253 of 1017 included RCTs were discontinued (24.9\% [95\% CI, 22.3\%-27.6\%]). Only 96 of 253 discontinuations (37.9\% [95\% CI, 32.0\%-44.3\%]) were reported to ethics committees. The most frequent reason for discontinuation was poor recruitment (101/1017; 9.9\% [95\% CI, 8.2\%-12.0\%]). In multivariable analysis, industry sponsorship vs investigator sponsorship (8.4\% vs 26.5\%; odds ratio [OR], 0.25 [95\% CI, 0.15-0.43]; P {$<$} .001) and a larger planned sample size in increments of 100 (−0.7\%; OR, 0.96 [95\% CI, 0.92-1.00]; P = .04) were associated with lower rates of discontinuation due to poor recruitment. Discontinued trials were more likely to remain unpublished than completed trials (55.1\% vs 33.6\%; OR, 3.19 [95\% CI, 2.29-4.43]; P {$<$} .001). CONCLUSIONS AND RELEVANCE In this sample of trials based on RCT protocols from 6 research ethics committees, discontinuation was common, with poor recruitment being the most frequently reported reason. Greater efforts are needed to ensure the reporting of trial discontinuation to research ethics committees and the publication of results of discontinued trials.},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/5UN6LNKJ/Kasenda et al. - 2014 - Prevalence, Characteristics, and Publication of Di.pdf;/home/will/Zotero/storage/GREBMWKV/Kasenda et al. - 2014 - Prevalence, Characteristics, and Publication of Di.pdf}
|
|
|
}
|
|
|
|
|
|
@thesis{khmelnitskaya_competitionattritiondrug_2021,
|
|
|
title = {Competition and {{Attrition}} in {{Drug Development}}},
|
|
|
author = {Khmelnitskaya, Ekaterina},
|
|
|
date = {2021-05},
|
|
|
institution = {University of Virginia},
|
|
|
abstract = {With fewer than 10\% of new drugs reaching the market, the drug development process is notorious for its high attrition rate. However, we rarely observe the reason for a drug’s discontinuation. It is known that pharmaceutical firms withdraw drugs after clinical failures, such as when trial results do not demonstrate adequate safety or efficacy according to FDA standards. At the same time, surveys suggest that firms also withdraw drugs for strategic reasons, such as when competition makes it unprofitable to continue development. Disentangling these two sources of attrition is necessary in order to predict the effects a government policy would have on the number of drugs that reach consumers. In this paper, I propose an empirical framework to separately identify the two components of attrition for each disease. To this end, I build a continuous-time dynamic model of the drug development process. In the model, firms take competitors’ R\&D choices into account when they make exit decisions at different stages of the innovation process. To estimate the model, I use rich data on the development histories of experimental drugs, clinical trial outcomes, and disease-specific epidemiological characteristics. I find that, on average, strategic terminations account for 8.4\% of all attrition, and as much as 35\% for some diseases. Using these estimates in counterfactual simulations, I show that without strategic withdrawals, the rate at which new drugs reach consumers would be on average 23\% higher. Large subsidies for clinical trials help realize some of that gain, with better results found for diseases that have a higher share of strategic attrition. However, the overall effect of subsidies on the rate of new drug launches is small. Alternatively, the same effect can be achieved through any minor regulatory adjustment that marginally helps lower the probability of late-stage clinical failures.},
|
|
|
langid = {english},
|
|
|
pagetotal = {55},
|
|
|
file = {/home/will/Zotero/storage/CSRFCIDB/1_Khmelnitskaya_Ekaterina_2021_PHD.pdf;/home/will/Zotero/storage/QBXQ4ZLR/Khmelnitskaya - Competition and Attrition in Drug Development.pdf}
|
|
|
}
|
|
|
|
|
|
@article{krumholz_whathavewe_2007,
|
|
|
title = {What Have We Learnt from {{Vioxx}}?},
|
|
|
author = {Krumholz, Harlan M and Ross, Joseph S and Presler, Amos H and Egilman, David S},
|
|
|
date = {2007-01-20},
|
|
|
journaltitle = {BMJ : British Medical Journal},
|
|
|
shortjournal = {BMJ},
|
|
|
volume = {334},
|
|
|
number = {7585},
|
|
|
eprint = {17235089},
|
|
|
eprinttype = {pmid},
|
|
|
pages = {120--123},
|
|
|
issn = {0959-8138},
|
|
|
doi = {10.1136/bmj.39024.487720.68},
|
|
|
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1779871/},
|
|
|
urldate = {2024-08-30},
|
|
|
abstract = {In October UK patients who had cardiovascular events while taking rofecoxib lost the right to fight Merck in the US for compensation. But researchers and journals can still benefit from this case if they learn from the mistakes, write Harlan Krumholz and colleagues},
|
|
|
pmcid = {PMC1779871},
|
|
|
file = {/home/will/Zotero/storage/6HB3B8F4/Krumholz et al. - 2007 - What have we learnt from Vioxx.pdf}
|
|
|
}
|
|
|
|
|
|
@article{lai_brownianmotionlongterm_2001,
|
|
|
title = {Brownian Motion and Long-Term Clinical Trial Recruitment},
|
|
|
author = {Lai, Dejian and Moyé, Lemuel A. and Davis, Barry R. and Brown, Lisa E. and Sacks, Frank M.},
|
|
|
date = {2001-02},
|
|
|
journaltitle = {Journal of Statistical Planning and Inference},
|
|
|
shortjournal = {Journal of Statistical Planning and Inference},
|
|
|
volume = {93},
|
|
|
number = {1--2},
|
|
|
pages = {239--246},
|
|
|
issn = {03783758},
|
|
|
doi = {10.1016/S0378-3758(00)00203-2},
|
|
|
url = {https://linkinghub.elsevier.com/retrieve/pii/S0378375800002032},
|
|
|
urldate = {2023-04-28},
|
|
|
langid = {english}
|
|
|
}
|
|
|
|
|
|
@article{lan_statisticalmodelingprediction_2019,
|
|
|
title = {Statistical Modeling and Prediction of Clinical Trial Recruitment},
|
|
|
author = {Lan, Yu and Tang, Gong and Heitjan, Daniel F.},
|
|
|
date = {2019-03-15},
|
|
|
journaltitle = {Statistics in Medicine},
|
|
|
shortjournal = {Statistics in Medicine},
|
|
|
volume = {38},
|
|
|
number = {6},
|
|
|
pages = {945--955},
|
|
|
issn = {0277-6715, 1097-0258},
|
|
|
doi = {10.1002/sim.8036},
|
|
|
url = {https://onlinelibrary.wiley.com/doi/10.1002/sim.8036},
|
|
|
urldate = {2023-04-27},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/JNP2GUTQ/Lan et al. - 2019 - Statistical modeling and prediction of clinical tr.pdf}
|
|
|
}
|
|
|
|
|
|
@article{martin_clinicaltrialcycle_2017,
|
|
|
title = {Clinical Trial Cycle Times Continue to Increase despite Industry Efforts},
|
|
|
author = {Martin, Linda and Hutchens, Melissa and Hawkins, Conrad},
|
|
|
date = {2017-03},
|
|
|
journaltitle = {Nature Reviews Drug Discovery},
|
|
|
shortjournal = {Nat Rev Drug Discov},
|
|
|
volume = {16},
|
|
|
number = {3},
|
|
|
pages = {157--157},
|
|
|
issn = {1474-1776, 1474-1784},
|
|
|
doi = {10.1038/nrd.2017.21},
|
|
|
url = {https://www.nature.com/articles/nrd.2017.21},
|
|
|
urldate = {2024-09-04},
|
|
|
langid = {english},
|
|
|
keywords = {To Read},
|
|
|
file = {/home/will/Zotero/storage/V3HIEYZ4/Martin et al. - 2017 - Clinical trial cycle times continue to increase despite industry efforts.pdf}
|
|
|
}
|
|
|
|
|
|
@book{mcelreath_statisticalrethinkingbayesian_2020,
|
|
|
title = {Statistical Rethinking: A {{Bayesian}} Course with Examples in {{R}} and {{Stan}}},
|
|
|
shorttitle = {Statistical Rethinking},
|
|
|
author = {McElreath, Richard},
|
|
|
date = {2020},
|
|
|
series = {{{CRC}} Texts in Statistical Science},
|
|
|
edition = {2},
|
|
|
publisher = {{Taylor and Francis, CRC Press}},
|
|
|
location = {Boca Raton},
|
|
|
abstract = {"Statistical Rethinking: A Bayesian Course with Examples in R and Stan, Second Edition builds knowledge/confidence in statistical modeling. Pushes readers to perform step-by-step calculations (usually automated.) Unique, computational approach ensures readers understand details to make reasonable choices and interpretations in their modeling work"--},
|
|
|
isbn = {978-0-367-13991-9}
|
|
|
}
|
|
|
|
|
|
@online{officepublicaffairsjusticedepartment_2020,
|
|
|
title = {Office of {{Public Affairs}} | {{Justice Department Announces Global Resolution}} of {{Criminal}} and {{Civil Investigations}} with {{Opioid Manufacturer Purdue Pharma}} and {{Civil Settlement}} with {{Members}} of the {{Sackler Family}} | {{United States Department}} of {{Justice}}},
|
|
|
date = {2020-10-21T10:05:07-04:00},
|
|
|
url = {https://www.justice.gov/opa/pr/justice-department-announces-global-resolution-criminal-and-civil-investigations-opioid},
|
|
|
urldate = {2024-08-30},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/IGHDVTVY/justice-department-announces-global-resolution-criminal-and-civil-investigations-opioid.html}
|
|
|
}
|
|
|
|
|
|
@book{pearl_causalitymodelsreasoning_2009,
|
|
|
title = {Causality: Models, Reasoning, and Inference},
|
|
|
shorttitle = {Causality},
|
|
|
author = {Pearl, Judea},
|
|
|
date = {2009},
|
|
|
edition = {2},
|
|
|
publisher = {Cambridge University Press},
|
|
|
location = {Cambridge, U.K. ; New York},
|
|
|
isbn = {978-0-521-89560-6 978-0-521-77362-1},
|
|
|
langid = {english},
|
|
|
pagetotal = {384},
|
|
|
keywords = {Causation,Probabilities},
|
|
|
file = {/home/will/Zotero/storage/8GZJS832/Pearl - 2000 - Causality models, reasoning, and inference.pdf}
|
|
|
}
|
|
|
|
|
|
@article{pearl_introductioncausalinference_2010,
|
|
|
title = {An {{Introduction}} to {{Causal Inference}}},
|
|
|
author = {Pearl, Judea},
|
|
|
date = {2010-01-26},
|
|
|
journaltitle = {The International Journal of Biostatistics},
|
|
|
volume = {6},
|
|
|
number = {2},
|
|
|
issn = {1557-4679},
|
|
|
doi = {10.2202/1557-4679.1203},
|
|
|
url = {https://www.degruyter.com/document/doi/10.2202/1557-4679.1203/html},
|
|
|
urldate = {2023-01-31},
|
|
|
abstract = {This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underlie all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: those about (1) the effects of potential interventions, (2) probabilities of counterfactuals, and (3) direct and indirect effects (also known as “mediation”). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation.},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/B9GB9RLL/Pearl - 2010 - An Introduction to Causal Inference.pdf}
|
|
|
}
|
|
|
|
|
|
@article{research_orangebookpreface_2023,
|
|
|
title = {Orange {{Book Preface}}},
|
|
|
author = {family=Research, given=Center for Drug Evaluation, prefix=and, useprefix=false},
|
|
|
year = {Tue, 01/24/2023 - 11:54},
|
|
|
journaltitle = {FDA},
|
|
|
publisher = {FDA},
|
|
|
url = {https://www.fda.gov/drugs/development-approval-process-drugs/orange-book-preface},
|
|
|
urldate = {2023-04-08},
|
|
|
abstract = {Preface to Orange Book provides info on how the book came to be, relevant terms and codes, user responsibilities and more.},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/Y6J65VD2/orange-book-preface.html}
|
|
|
}
|
|
|
|
|
|
@online{rxnorm_,
|
|
|
type = {Product, Program, and Project Descriptions},
|
|
|
title = {{{RxNorm}}},
|
|
|
author = {{U.S. National Library of Medicine}},
|
|
|
publisher = {U.S. National Library of Medicine},
|
|
|
url = {https://www.nlm.nih.gov/research/umls/rxnorm/index.html},
|
|
|
urldate = {2023-04-08},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/UPPXYYW6/index.html}
|
|
|
}
|
|
|
|
|
|
@article{sicklick_precisiononcologyintentiontreat_2020,
|
|
|
title = {Precision Oncology: The Intention-to-Treat Analysis Fallacy},
|
|
|
shorttitle = {Precision Oncology},
|
|
|
author = {Sicklick, Jason K. and Kato, Shumei and Okamura, Ryosuke and Kurzrock, Razelle},
|
|
|
date = {2020-07},
|
|
|
journaltitle = {European Journal of Cancer},
|
|
|
shortjournal = {European Journal of Cancer},
|
|
|
volume = {133},
|
|
|
pages = {25--28},
|
|
|
issn = {09598049},
|
|
|
doi = {10.1016/j.ejca.2020.04.002},
|
|
|
url = {https://linkinghub.elsevier.com/retrieve/pii/S095980492030191X},
|
|
|
urldate = {2023-05-03},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/3NKV842V/Sicklick et al. - 2020 - Precision oncology the intention-to-treat analysi.pdf;/home/will/Zotero/storage/GRUSMSSF/Sicklick et al. - 2020 - Precision oncology the intention-to-treat analysi.pdf}
|
|
|
}
|
|
|
|
|
|
@article{spies_conceptdevelopmentinteractive_2021,
|
|
|
title = {Concept and Development of an Interactive Tool for Trial Recruitment Planning and Management},
|
|
|
author = {Spies, Ruan and Siegfried, Nandi and Myers, Bronwyn and Grobbelaar, Sara S.},
|
|
|
date = {2021-12},
|
|
|
journaltitle = {Trials},
|
|
|
shortjournal = {Trials},
|
|
|
volume = {22},
|
|
|
number = {1},
|
|
|
pages = {189},
|
|
|
issn = {1745-6215},
|
|
|
doi = {10.1186/s13063-021-05112-z},
|
|
|
url = {https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-021-05112-z},
|
|
|
urldate = {2023-05-03},
|
|
|
abstract = {Abstract Background Predicting and monitoring recruitment in large, complex trials is essential to ensure appropriate resource management and budgeting. In a novel partnership between clinical trial investigators of the South African Medical Research Council and industrial engineers from the Stellenbosch University Health Systems Engineering and Innovation Hub, we developed a trial recruitment tool (TRT). The objective of the tool is to serve as a computerised decisions-support system to aid the planning and management phases of the trial recruitment process. Method The specific requirements of the TRT were determined in several workshops between the partners. A Poisson process simulation model was formulated and incorporated in the TRT to predict the recruitment duration. The assumptions underlying the model were made in consultation with the trial team at the start of the project and were deemed reasonable. Real-world data extracted from a current cluster trial, Project MIND, based in 24 sites in South Africa was used to verify the simulation model and to develop the monitoring component of the TRT. Results The TRT comprises a planning and monitoring component. The planning component generates different trial scenarios for predicted trial recruitment duration based on user inputs, e.g. number of sites, initiation delays. The monitoring component uses and analyses the data retrieved from the trial management information system to generate different levels of information, displayed visually on an interactive, user-friendly dashboard. Users can analyse the results at trial or site level, changing input parameters to see the resultant effect on the duration of trial recruitment. Conclusion This TRT is an easy-to-use tool that assists in the management of the trial recruitment process. The TRT has potential to expedite improved management of clinical trials by providing the appropriate information needed for the planning and monitoring of the trial recruitment phase. This TRT extends prior tools describing historic recruitment only to using historic data to predict future recruitment. The broader project demonstrates the value of collaboration between clinicians and engineers to optimise their respective skillsets.},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/6W686KV5/Spies et al. - 2021 - Concept and development of an interactive tool for.pdf}
|
|
|
}
|
|
|
|
|
|
@online{splinesstan_,
|
|
|
title = {Splines {{In Stan}}},
|
|
|
url = {https://mc-stan.org/users/documentation/case-studies/splines_in_stan.html},
|
|
|
urldate = {2023-04-28},
|
|
|
file = {/home/will/Zotero/storage/SECBCAYX/splines_in_stan.html}
|
|
|
}
|
|
|
|
|
|
@article{st-louis_enrollmentreportingpractices_2018,
|
|
|
title = {Enrollment and Reporting Practices in Pediatric General Surgical Randomized Clinical Trials: {{A}} Systematic Review and Observational Analysis},
|
|
|
shorttitle = {Enrollment and Reporting Practices in Pediatric General Surgical Randomized Clinical Trials},
|
|
|
author = {St-Louis, Etienne and Oosenbrug, Marcus and Landry, Tara and Baird, Robert},
|
|
|
date = {2018-05},
|
|
|
journaltitle = {Journal of Pediatric Surgery},
|
|
|
shortjournal = {Journal of Pediatric Surgery},
|
|
|
volume = {53},
|
|
|
number = {5},
|
|
|
pages = {879--884},
|
|
|
issn = {00223468},
|
|
|
doi = {10.1016/j.jpedsurg.2018.02.009},
|
|
|
url = {https://linkinghub.elsevier.com/retrieve/pii/S0022346818300630},
|
|
|
urldate = {2023-05-03},
|
|
|
langid = {english}
|
|
|
}
|
|
|
|
|
|
@misc{standevelopmentteam_rstaninterfacestan_2023,
|
|
|
title = {{{RStan}}: The {{R}} Interface to {{Stan}}},
|
|
|
author = {{Stan Development Team}},
|
|
|
date = {2023},
|
|
|
url = {https://mc-stan.org/}
|
|
|
}
|
|
|
|
|
|
@misc{standevelopmentteam_stanmodellingusersguide_2022,
|
|
|
title = {Stan {{Modelling usersGuide}} and {{Reference Manual}}},
|
|
|
author = {{Stan Development Team}},
|
|
|
date = {2022},
|
|
|
url = {https://mc-stan.org/}
|
|
|
}
|
|
|
|
|
|
@article{swain-cabriales_enrollmentbreastcancer_2013,
|
|
|
title = {Enrollment onto Breast Cancer Therapeutic Clinical Trials: {{A}} Tertiary Cancer Center Experience},
|
|
|
shorttitle = {Enrollment onto Breast Cancer Therapeutic Clinical Trials},
|
|
|
author = {Swain-Cabriales, Suzanne and Bourdeanu, Laura and Niland, Joyce and Stiller, Tracy and Somlo, George},
|
|
|
date = {2013-08},
|
|
|
journaltitle = {Applied Nursing Research},
|
|
|
shortjournal = {Applied Nursing Research},
|
|
|
volume = {26},
|
|
|
number = {3},
|
|
|
pages = {133--135},
|
|
|
issn = {08971897},
|
|
|
doi = {10.1016/j.apnr.2013.01.003},
|
|
|
url = {https://linkinghub.elsevier.com/retrieve/pii/S0897189713000049},
|
|
|
urldate = {2023-05-03},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/VXTDFDUW/Swain-Cabriales et al. - 2013 - Enrollment onto breast cancer therapeutic clinical.pdf}
|
|
|
}
|
|
|
|
|
|
@article{tozzi_predictingaccrualrate_1996,
|
|
|
title = {Predicting the Accrual Rate in a Vaccine Clinical Trial: An a Posteriori Evaluation of the Feasibility Study},
|
|
|
shorttitle = {Predicting the Accrual Rate in a Vaccine Clinical Trial},
|
|
|
author = {Tozzi, A. E. and Cofi Degli Atti, M. L. and Panei, P. and Anemona, A. and Binkin, N. and Salmaso, S. and Luzi, S. and Greco, D.},
|
|
|
date = {1996-10},
|
|
|
journaltitle = {Revue D'epidemiologie Et De Sante Publique},
|
|
|
shortjournal = {Rev Epidemiol Sante Publique},
|
|
|
volume = {44},
|
|
|
number = {5},
|
|
|
eprint = {8933665},
|
|
|
eprinttype = {pmid},
|
|
|
pages = {387--393},
|
|
|
issn = {0398-7620},
|
|
|
abstract = {To estimate the expected accrual rate in a double blinded controlled randomized trial of the absolute clinical efficacy of three anti-pertussis vaccines in infants, we performed a series of surveys among mothers and physicians in the areas to be considered for participation in the trial. In this paper, we compared the predicted enrollment with the actual enrollment achieved at the end of the recruitment phase of the trial. The predicted enrollment rate was 27\%, while the observed rate was 26\%. Results from the feasibility study were highly predictive of actual enrollment rate. In the local health units (USL) where such assessments were carried out, the accrual rate, was higher than those not participating in the feasibility phase.},
|
|
|
langid = {english},
|
|
|
keywords = {Adult,Double-Blind Method,Feasibility Studies,Female,Health Knowledge Attitudes Practice,Humans,Infant,Male,Mothers,Multicenter Studies as Topic,Patient Acceptance of Health Care,Pertussis Vaccine,Physicians,Predictive Value of Tests,Randomized Controlled Trials as Topic,Reproducibility of Results}
|
|
|
}
|
|
|
|
|
|
@article{urbas_interimrecruitmentprediction_2022,
|
|
|
title = {Interim Recruitment Prediction for Multi-Center Clinical Trials},
|
|
|
author = {Urbas, Szymon and Sherlock, Chris and Metcalfe, Paul},
|
|
|
date = {2022-04-13},
|
|
|
journaltitle = {Biostatistics},
|
|
|
volume = {23},
|
|
|
number = {2},
|
|
|
pages = {485--506},
|
|
|
issn = {1465-4644, 1468-4357},
|
|
|
doi = {10.1093/biostatistics/kxaa036},
|
|
|
url = {https://academic.oup.com/biostatistics/article/23/2/485/5911853},
|
|
|
urldate = {2023-05-03},
|
|
|
abstract = {Summary We introduce a general framework for monitoring, modeling, and predicting the recruitment to multi-center clinical trials. The work is motivated by overly optimistic and narrow prediction intervals produced by existing time-homogeneous recruitment models for multi-center recruitment. We first present two tests for detection of decay in recruitment rates, together with a power study. We then introduce a model based on the inhomogeneous Poisson process with monotonically decaying intensity, motivated by recruitment trends observed in oncology trials. The general form of the model permits adaptation to any parametric curve-shape. A general method for constructing sensible parameter priors is provided and Bayesian model averaging is used for making predictions which account for the uncertainty in both the parameters and the model. The validity of the method and its robustness to misspecification are tested using simulated datasets. The new methodology is then applied to oncology trial data, where we make interim accrual predictions, comparing them to those obtained by existing methods, and indicate where unexpected changes in the accrual pattern occur.},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/85FQMQUQ/Urbas et al. - 2022 - Interim recruitment prediction for multi-center cl.pdf;/home/will/Zotero/storage/988ACIXQ/Urbas et al. - 2022 - Interim recruitment prediction for multi-center cl.pdf}
|
|
|
}
|
|
|
|
|
|
@article{ursu_drugcentralonlinedrug_2017,
|
|
|
title = {{{DrugCentral}}: Online Drug Compendium},
|
|
|
shorttitle = {{{DrugCentral}}},
|
|
|
author = {Ursu, Oleg and Holmes, Jayme and Knockel, Jeffrey and Bologa, Cristian G. and Yang, Jeremy J. and Mathias, Stephen L. and Nelson, Stuart J. and Oprea, Tudor I.},
|
|
|
date = {2017-01-04},
|
|
|
journaltitle = {Nucleic Acids Research},
|
|
|
shortjournal = {Nucleic Acids Res},
|
|
|
volume = {45},
|
|
|
number = {D1},
|
|
|
pages = {D932-D939},
|
|
|
issn = {0305-1048, 1362-4962},
|
|
|
doi = {10.1093/nar/gkw993},
|
|
|
url = {https://academic.oup.com/nar/article-lookup/doi/10.1093/nar/gkw993},
|
|
|
urldate = {2023-04-10},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/7W6THRK6/Ursu et al. - 2017 - DrugCentral online drug compendium.pdf}
|
|
|
}
|
|
|
|
|
|
@misc{uscms_icd-10_cm_2022,
|
|
|
title = {{{ICD-10-CM Official Guidelines}} for {{Coding}} and {{Reporting}}},
|
|
|
author = {{Centers For Medicare and Medicaid}},
|
|
|
date = {2022-04},
|
|
|
file = {/home/will/Zotero/storage/53ZTGLWD/10cmguidelines-FY2022-April-1-update.pdf}
|
|
|
}
|
|
|
|
|
|
@misc{uscms_icd-10_pcs_2022,
|
|
|
title = {2022 {{ICD-10-PCS Official Guidelines}} for {{Coding}} and {{Reporting}}},
|
|
|
author = {{Centers For Medicare and Medicaid}},
|
|
|
date = {2022-04},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/7ADA2YND/2022 ICD-10-PCS Official Guidelines for Coding and Reporting.pdf}
|
|
|
}
|
|
|
|
|
|
@misc{usfda_splfactsheet_2023,
|
|
|
title = {Indexing {{Spl Fact Sheet}}},
|
|
|
author = {{U.S. Food and Drug Administration}},
|
|
|
url = {https://www.fda.gov/media/85645/download},
|
|
|
urldate = {2023-04-08},
|
|
|
organization = {U.S. Food and Drug Administration},
|
|
|
file = {/home/will/Zotero/storage/KAHW2ABD/Indexing-SPL-Fact-Sheet.pdf}
|
|
|
}
|
|
|
|
|
|
@online{usnlm_fdaaa800finalrule,
|
|
|
type = {Government},
|
|
|
title = {{{FDAAA}} 801 and the {{Final Rule}} - {{ClinicalTrials}}.Gov},
|
|
|
author = {{U.S. National Library of Medicine}},
|
|
|
url = {https://clinicaltrials.gov/ct2/manage-recs/fdaaa},
|
|
|
urldate = {2023-04-08},
|
|
|
langid = {english},
|
|
|
organization = {ClinicalTrials.gov},
|
|
|
file = {/home/will/Zotero/storage/V9YVGVK2/fdaaa.html}
|
|
|
}
|
|
|
|
|
|
@online{usnlm_meshhomepage_2023,
|
|
|
type = {Product, Program, and Project Descriptions},
|
|
|
title = {Medical {{Subject Headings}} - {{Home Page}}},
|
|
|
author = {{U.S. National Library of Medicine}},
|
|
|
publisher = {U.S. National Library of Medicine},
|
|
|
url = {https://www.nlm.nih.gov/mesh/meshhome.html},
|
|
|
urldate = {2023-04-09},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/RTW5EPBG/meshhome.html}
|
|
|
}
|
|
|
|
|
|
@online{usnlm_rxnavinabox_2023,
|
|
|
title = {{{RxNav-in-a-Box}} - {{RxNav Applications}}},
|
|
|
author = {{U.S. National Library of Medicine}},
|
|
|
url = {https://lhncbc.nlm.nih.gov/RxNav/applications/RxNav-in-a-Box.html},
|
|
|
urldate = {2023-04-10},
|
|
|
file = {/home/will/Zotero/storage/A9S2NM29/RxNav-in-a-Box.html}
|
|
|
}
|
|
|
|
|
|
@online{usnlm_rxnorm_2023,
|
|
|
type = {Product, Program, and Project Descriptions},
|
|
|
title = {{{RxNorm Overview}}},
|
|
|
author = {{U.S. National Library of Medicine}},
|
|
|
publisher = {U.S. National Library of Medicine},
|
|
|
url = {https://www.nlm.nih.gov/research/umls/rxnorm/overview.html},
|
|
|
urldate = {2023-04-08},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/XI269ZNM/overview.html}
|
|
|
}
|
|
|
|
|
|
@article{vandergronde_addressingchallengehighpriced_2017,
|
|
|
title = {Addressing the Challenge of High-Priced Prescription Drugs in the Era of Precision Medicine: {{A}} Systematic Review of Drug Life Cycles, Therapeutic Drug Markets and Regulatory Frameworks},
|
|
|
shorttitle = {Addressing the Challenge of High-Priced Prescription Drugs in the Era of Precision Medicine},
|
|
|
author = {family=Gronde, given=Toon, prefix=van der, useprefix=true and Uyl-de Groot, Carin A. and Pieters, Toine},
|
|
|
date = {2017-08-16},
|
|
|
journaltitle = {PLoS ONE},
|
|
|
shortjournal = {PLoS One},
|
|
|
volume = {12},
|
|
|
number = {8},
|
|
|
eprint = {28813502},
|
|
|
eprinttype = {pmid},
|
|
|
pages = {e0182613},
|
|
|
issn = {1932-6203},
|
|
|
doi = {10.1371/journal.pone.0182613},
|
|
|
url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5559086/},
|
|
|
urldate = {2023-03-11},
|
|
|
abstract = {Context Recent public outcry has highlighted the rising cost of prescription drugs worldwide, which in several disease areas outpaces other health care expenditures and results in a suboptimal global availability of essential medicines. Method A systematic review of Pubmed, the Financial Times, the New York Times, the Wall Street Journal and the Guardian was performed to identify articles related to the pricing of medicines. Findings Changes in drug life cycles have dramatically affected patent medicine markets, which have long been considered a self-evident and self-sustainable source of income for highly profitable drug companies. Market failure in combination with high merger and acquisition activity in the sector have allowed price increases for even off-patent drugs. With market interventions and the introduction of QALY measures in health care, governments have tried to influence drug prices, but often encounter unintended consequences. Patent reform legislation, reference pricing, outcome-based pricing and incentivizing physicians and pharmacists to prescribe low-cost drugs are among the most promising short-term policy options. Due to the lack of systematic research on the effectiveness of policy measures, an increasing number of ad hoc decisions have been made with counterproductive effects on the availability of essential drugs. Future challenges demand new policies, for which recommendations are offered. Conclusion A fertile ground for high-priced drugs has been created by changes in drug life-cycle dynamics, the unintended effects of patent legislation, government policy measures and orphan drug programs. There is an urgent need for regulatory reform to curtail prices and safeguard equitable access to innovative medicines.},
|
|
|
pmcid = {PMC5559086},
|
|
|
file = {/home/will/Zotero/storage/7Y8KZSMU/van der Gronde et al. - 2017 - Addressing the challenge of high-priced prescripti.pdf}
|
|
|
}
|
|
|
|
|
|
@article{vos_globalburden369_2020,
|
|
|
title = {Global Burden of 369 Diseases and Injuries in 204 Countries and Territories, 1990–2019: A Systematic Analysis for the {{Global Burden}} of {{Disease Study}} 2019},
|
|
|
shorttitle = {Global Burden of 369 Diseases and Injuries in 204 Countries and Territories, 1990–2019},
|
|
|
author = {Vos, Theo and Lim, Stephen S. and Abbafati, Cristiana and Abbas, Kaja M. and Abbasi, Mohammad and Abbasifard, Mitra and Abbasi-Kangevari, Mohsen and Abbastabar, Hedayat and Abd-Allah, Foad and Abdelalim, Ahmed and Abdollahi, Mohammad and Abdollahpour, Ibrahim and Abolhassani, Hassan and Aboyans, Victor and Abrams, Elissa M. and Abreu, Lucas Guimarães and Abrigo, Michael R. M. and Abu-Raddad, Laith Jamal and Abushouk, Abdelrahman I. and Acebedo, Alyssa and Ackerman, Ilana N. and Adabi, Maryam and Adamu, Abdu A. and Adebayo, Oladimeji M. and Adekanmbi, Victor and Adelson, Jaimie D. and Adetokunboh, Olatunji O. and Adham, Davoud and Afshari, Mahdi and Afshin, Ashkan and Agardh, Emilie E. and Agarwal, Gina and Agesa, Kareha M. and Aghaali, Mohammad and Aghamir, Seyed Mohammad Kazem and Agrawal, Anurag and Ahmad, Tauseef and Ahmadi, Alireza and Ahmadi, Mehdi and Ahmadieh, Hamid and Ahmadpour, Ehsan and Akalu, Temesgen Yihunie and Akinyemi, Rufus Olusola and Akinyemiju, Tomi and Akombi, Blessing and Al-Aly, Ziyad and Alam, Khurshid and Alam, Noore and Alam, Samiah and Alam, Tahiya and Alanzi, Turki M. and Albertson, Samuel B. and Alcalde-Rabanal, Jacqueline Elizabeth and Alema, Niguse Meles and Ali, Muhammad and Ali, Saqib and Alicandro, Gianfranco and Alijanzadeh, Mehran and Alinia, Cyrus and Alipour, Vahid and Aljunid, Syed Mohamed and Alla, François and Allebeck, Peter and Almasi-Hashiani, Amir and Alonso, Jordi and Al-Raddadi, Rajaa M. and Altirkawi, Khalid A. and Alvis-Guzman, Nelson and Alvis-Zakzuk, Nelson J. and Amini, Saeed and Amini-Rarani, Mostafa and Aminorroaya, Arya and Amiri, Fatemeh and Amit, Arianna Maever L. and Amugsi, Dickson A. and Amul, Gianna Gayle Herrera and Anderlini, Deanna and Andrei, Catalina Liliana and Andrei, Tudorel and Anjomshoa, Mina and Ansari, Fereshteh and Ansari, Iman and Ansari-Moghaddam, Alireza and Antonio, Carl Abelardo T. and Antony, Catherine M. and Antriyandarti, Ernoiz and Anvari, Davood and Anwer, Razique and Arabloo, Jalal and Arab-Zozani, Morteza and Aravkin, Aleksandr Y. and Ariani, Filippo and Ärnlöv, Johan and Aryal, Krishna K. and Arzani, Afsaneh and Asadi-Aliabadi, Mehran and Asadi-Pooya, Ali A. and Asghari, Babak and Ashbaugh, Charlie and Atnafu, Desta Debalkie and Atre, Sachin R. and Ausloos, Floriane and Ausloos, Marcel and Quintanilla, Beatriz Paulina Ayala and Ayano, Getinet and Ayanore, Martin Amogre and Aynalem, Yared Asmare and Azari, Samad and Azarian, Ghasem and Azene, Zelalem Nigussie and Babaee, Ebrahim and Badawi, Alaa and Bagherzadeh, Mojtaba and Bakhshaei, Mohammad Hossein and Bakhtiari, Ahad and Balakrishnan, Senthilkumar and Balalla, Shivanthi and Balassyano, Shelly and Banach, Maciej and Banik, Palash Chandra and Bannick, Marlena S. and Bante, Agegnehu Bante and Baraki, Adhanom Gebreegziabher and Barboza, Miguel A. and Barker-Collo, Suzanne Lyn and Barthelemy, Celine M. and Barua, Lingkan and Barzegar, Akbar and Basu, Sanjay and Baune, Bernhard T. and Bayati, Mohsen and Bazmandegan, Gholamreza and Bedi, Neeraj and Beghi, Ettore and Béjot, Yannick and Bello, Aminu K. and Bender, Rose G. and Bennett, Derrick A. and Bennitt, Fiona B. and Bensenor, Isabela M. and Benziger, Catherine P. and Berhe, Kidanemaryam and Bernabe, Eduardo and Bertolacci, Gregory J. and Bhageerathy, Reshmi and Bhala, Neeraj and Bhandari, Dinesh and Bhardwaj, Pankaj and Bhattacharyya, Krittika and Bhutta, Zulfiqar A. and Bibi, Sadia and Biehl, Molly H. and Bikbov, Boris and Sayeed, Muhammad Shahdaat Bin and Biondi, Antonio and Birihane, Binyam Minuye and Bisanzio, Donal and Bisignano, Catherine and Biswas, Raaj Kishore and Bohlouli, Somayeh and Bohluli, Mehdi and Bolla, Srinivasa Rao Rao and Boloor, Archith and Boon-Dooley, Alexandra S. and Borges, Guilherme and Borzì, Antonio Maria and Bourne, Rupert and Brady, Oliver J. and Brauer, Michael and Brayne, Carol and Breitborde, Nicholas J. K. and Brenner, Hermann and Briant, Paul Svitil and Briggs, Andrew M. and Briko, Nikolay Ivanovich and Britton, Gabrielle B. and Bryazka, Dana and Buchbinder, Rachelle and Bumgarner, Blair R. and Busse, Reinhard and Butt, Zahid A. and family=Santos, given=Florentino Luciano Caetano, prefix=dos, useprefix=false and Cámera, Luis LA Alberto and Campos-Nonato, Ismael R. and Car, Josip and Cárdenas, Rosario and Carreras, Giulia and Carrero, Juan J. and Carvalho, Felix and Castaldelli-Maia, Joao Mauricio and Castañeda-Orjuela, Carlos A. and Castelpietra, Giulio and Castle, Chris D. and Castro, Franz and Catalá-López, Ferrán and Causey, Kate and Cederroth, Christopher R. and Cercy, Kelly M. and Cerin, Ester and Chandan, Joht Singh and Chang, Alex R. and Charlson, Fiona J. and Chattu, Vijay Kumar and Chaturvedi, Sarika and Chimed-Ochir, Odgerel and Chin, Ken Lee and Cho, Daniel Youngwhan and Christensen, Hanne and Chu, Dinh-Toi and Chung, Michael T. and Cicuttini, Flavia M. and Ciobanu, Liliana G. and Cirillo, Massimo and Collins, Emma L. and Compton, Kelly and Conti, Sara and Cortesi, Paolo Angelo and Costa, Vera Marisa and Cousin, Ewerton and Cowden, Richard G. and Cowie, Benjamin C. and Cromwell, Elizabeth A. and Cross, Di H. and Crowe, Christopher Stephen and Cruz, Jessica A. and Cunningham, Matthew and Dahlawi, Saad M. A. and Damiani, Giovanni and Dandona, Lalit and Dandona, Rakhi and Darwesh, Aso Mohammad and Daryani, Ahmad and Das, Jai K. and Gupta, Rajat Das and family=Neves, given=José, prefix=das, useprefix=false and Dávila-Cervantes, Claudio Alberto and Davletov, Kairat and Leo, Diego De and Dean, Frances E. and DeCleene, Nicole K. and Deen, Amanda and Degenhardt, Louisa and Dellavalle, Robert Paul and Demeke, Feleke Mekonnen and Demsie, Desalegn Getnet and Denova-Gutiérrez, Edgar and Dereje, Nebiyu Dereje and Dervenis, Nikolaos and Desai, Rupak and Desalew, Assefa and Dessie, Getenet Ayalew and Dharmaratne, Samath Dhamminda and Dhungana, Govinda Prasad and Dianatinasab, Mostafa and Diaz, Daniel and Forooshani, Zahra Sadat Dibaji and Dingels, Zachary V. and Dirac, M. Ashworth and Djalalinia, Shirin and Do, Hoa Thi and Dokova, Klara and Dorostkar, Fariba and Doshi, Chirag P. and Doshmangir, Leila and Douiri, Abdel and Doxey, Matthew C. and Driscoll, Tim Robert and Dunachie, Susanna J. and Duncan, Bruce B. and Duraes, Andre Rodrigues and Eagan, Arielle Wilder and Kalan, Mohammad Ebrahimi and Edvardsson, David and Ehrlich, Joshua R. and Nahas, Nevine El and Sayed, Iman El and Tantawi, Maha El and Elbarazi, Iffat and Elgendy, Islam Y. and Elhabashy, Hala Rashad and El-Jaafary, Shaimaa I. and Elyazar, Iqbal RF and Emamian, Mohammad Hassan and Emmons-Bell, Sophia and Erskine, Holly E. and Eshrati, Babak and Eskandarieh, Sharareh and Esmaeilnejad, Saman and Esmaeilzadeh, Firooz and Esteghamati, Alireza and Estep, Kara and Etemadi, Arash and Etisso, Atkilt Esaiyas and Farahmand, Mohammad and Faraj, Anwar and Fareed, Mohammad and Faridnia, Roghiyeh and Farinha, Carla Sofia e Sá and Farioli, Andrea and Faro, Andre and Faruque, Mithila and Farzadfar, Farshad and Fattahi, Nazir and Fazlzadeh, Mehdi and Feigin, Valery L. and Feldman, Rachel and Fereshtehnejad, Seyed-Mohammad and Fernandes, Eduarda and Ferrari, Alize J. and Ferreira, Manuela L. and Filip, Irina and Fischer, Florian and Fisher, James L. and Fitzgerald, Ryan and Flohr, Carsten and Flor, Luisa Sorio and Foigt, Nataliya A. and Folayan, Morenike Oluwatoyin and Force, Lisa M. and Fornari, Carla and Foroutan, Masoud and Fox, Jack T. and Freitas, Marisa and Fu, Weijia and Fukumoto, Takeshi and Furtado, João M. and Gad, Mohamed M. and Gakidou, Emmanuela and Galles, Natalie C. and Gallus, Silvano and Gamkrelidze, Amiran and Garcia-Basteiro, Alberto L. and Gardner, William M. and Geberemariyam, Biniyam Sahiledengle and Gebrehiwot, Abiyu Mekonnen and Gebremedhin, Ketema Bizuwork and Gebreslassie, Assefa Ayalew Ayalew Ayalew and Hayoon, Anna Gershberg and Gething, Peter W. and Ghadimi, Maryam and Ghadiri, Keyghobad and Ghafourifard, Mansour and Ghajar, Alireza and Ghamari, Farhad and Ghashghaee, Ahmad and Ghiasvand, Hesam and Ghith, Nermin and Gholamian, Asadollah and Gilani, Syed Amir and Gill, Paramjit Singh and Gitimoghaddam, Mojgan and Giussani, Giorgia and Goli, Srinivas and Gomez, Ricardo Santiago and Gopalani, Sameer Vali and Gorini, Giuseppe and Gorman, Taren M. and Gottlich, Harrison Chase and Goudarzi, Houman and Goulart, Alessandra C. and Goulart, Bárbara Niegia Garcia and Grada, Ayman and Grivna, Michal and Grosso, Giuseppe and Gubari, Mohammed Ibrahim Mohialdeen and Gugnani, Harish Chander and Guimaraes, Andre Luiz Sena and Guimarães, Rafael Alves and Guled, Rashid Abdi and Guo, Gaorui and Guo, Yuming and Gupta, Rajeev and Haagsma, Juanita A. and Haddock, Beatrix and Hafezi-Nejad, Nima and Hafiz, Abdul and Hagins, Hailey and Haile, Lydia M. and Hall, Brian J. and Halvaei, Iman and Hamadeh, Randah R. and Abdullah, Kanaan Hamagharib and Hamilton, Erin B. and Han, Chieh and Han, Hannah and Hankey, Graeme J. and Haro, Josep Maria and Harvey, James D. and Hasaballah, Ahmed I. and Hasanzadeh, Amir and Hashemian, Maryam and Hassanipour, Soheil and Hassankhani, Hadi and Havmoeller, Rasmus J. and Hay, Roderick J. and Hay, Simon I. and Hayat, Khezar and Heidari, Behnam and Heidari, Golnaz and Heidari-Soureshjani, Reza and Hendrie, Delia and Henrikson, Hannah J. and Henry, Nathaniel J. and Herteliu, Claudiu and Heydarpour, Fatemeh and Hird, Thomas R. and Hoek, Hans W. and Hole, Michael K. and Holla, Ramesh and Hoogar, Praveen and Hosgood, H. Dean and Hosseinzadeh, Mehdi and Hostiuc, Mihaela and Hostiuc, Sorin and Househ, Mowafa and Hoy, Damian G. and Hsairi, Mohamed and Hsieh, Vivian Chia-rong and Hu, Guoqing and Huda, Tanvir M. and Hugo, Fernando N. and Huynh, Chantal K. and Hwang, Bing-Fang and Iannucci, Vincent C. and Ibitoye, Segun Emmanuel and Ikuta, Kevin S. and Ilesanmi, Olayinka Stephen and Ilic, Irena M. and Ilic, Milena D. and Inbaraj, Leeberk Raja and Ippolito, Helen and Irvani, Seyed Sina Naghibi and Islam, M. Mofizul and Islam, MdMohaimenul and Islam, Sheikh Mohammed Shariful and Islami, Farhad and Iso, Hiroyasu and Ivers, Rebecca Q. and Iwu, Chidozie C. D. and Iyamu, Ihoghosa Osamuyi and Jaafari, Jalil and Jacobsen, Kathryn H. and Jadidi-Niaragh, Farhad and Jafari, Hussain and Jafarinia, Morteza and Jahagirdar, Deepa and Jahani, Mohammad Ali and Jahanmehr, Nader and Jakovljevic, Mihajlo and Jalali, Amir and Jalilian, Farzad and James, Spencer L. and Janjani, Hosna and Janodia, Manthan Dilipkumar and Jayatilleke, Achala Upendra and Jeemon, Panniyammakal and Jenabi, Ensiyeh and Jha, Ravi Prakash and Jha, Vivekanand and Ji, John S. and Jia, Peng and John, Oommen and John-Akinola, Yetunde O. and Johnson, Catherine Owens and Johnson, Sarah Charlotte and Jonas, Jost B. and Joo, Tamas and Joshi, Ankur and Jozwiak, Jacek Jerzy and Jürisson, Mikk and Kabir, Ali and Kabir, Zubair and Kalani, Hamed and Kalani, Rizwan and Kalankesh, Leila R. and Kalhor, Rohollah and Kamiab, Zahra and Kanchan, Tanuj and Matin, Behzad Karami and Karch, André and Karim, Mohd Anisul and Karimi, Salah Eddin and Kassa, Getachew Mullu and Kassebaum, Nicholas J. and Katikireddi, Srinivasa Vittal and Kawakami, Norito and Kayode, Gbenga A. and Keddie, Suzanne H. and Keller, Cathleen and Kereselidze, Maia and Khafaie, Morteza Abdullatif and Khalid, Nauman and Khan, Maseer and Khatab, Khaled and Khater, Mona M. and Khatib, Mahalaqua Nazli and Khayamzadeh, Maryam and Khodayari, Mohammad Taghi and Khundkar, Roba and Kianipour, Neda and Kieling, Christian and Kim, Daniel and Kim, Young-Eun and Kim, Yun Jin and Kimokoti, Ruth W. and Kisa, Adnan and Kisa, Sezer and Kissimova-Skarbek, Katarzyna and Kivimäki, Mika and Kneib, Cameron J. and Knudsen, Ann Kristin Skrindo and Kocarnik, Jonathan M. and Kolola, Tufa and Kopec, Jacek A. and Kosen, Soewarta and Koul, Parvaiz A. and Koyanagi, Ai and Kravchenko, Michael A. and Krishan, Kewal and Krohn, Kris J. and Defo, Barthelemy Kuate and Bicer, Burcu Kucuk and Kumar, G. Anil and Kumar, Manasi and Kumar, Pushpendra and Kumar, Vivek and Kumaresh, Girikumar and Kurmi, Om P. and Kusuma, Dian and Kyu, Hmwe Hmwe and Vecchia, Carlo La and Lacey, Ben and Lal, Dharmesh Kumar and Lalloo, Ratilal and Lam, Jennifer O. and Lami, Faris Hasan and Landires, Iván and Lang, Justin J. and Lansingh, Van Charles and Larson, Samantha Leigh and Larsson, Anders O. and Lasrado, Savita and Lassi, Zohra S. and Lau, Kathryn Mei-Ming and Lavados, Pablo M. and Lazarus, Jeffrey V. and Ledesma, Jorge R. and Lee, Paul H. and Lee, Shaun Wen Huey and LeGrand, Kate E. and Leigh, James and Leonardi, Matilde and Lescinsky, Haley and Leung, Janni and Levi, Miriam and Lewington, Sarah and Li, Shanshan and Lim, Lee-Ling and Lin, Christine and Lin, Ro-Ting and Linehan, Christine and Linn, Shai and Liu, Hung-Chun and Liu, Shiwei and Liu, Zichen and Looker, Katharine J. and Lopez, Alan D. and Lopukhov, Platon D. and Lorkowski, Stefan and Lotufo, Paulo A. and Lucas, Tim C. D. and Lugo, Alessandra and Lunevicius, Raimundas and Lyons, Ronan A. and Ma, Jianing and MacLachlan, Jennifer H. and Maddison, Emilie R. and Maddison, Ralph and Madotto, Fabiana and Mahasha, Phetole Walter and Mai, Hue Thi and Majeed, Azeem and Maled, Venkatesh and Maleki, Shokofeh and Malekzadeh, Reza and Malta, Deborah Carvalho and Mamun, Abdullah A. and Manafi, Amir and Manafi, Navid and Manguerra, Helena and Mansouri, Borhan and Mansournia, Mohammad Ali and Herrera, Ana M. Mantilla and Maravilla, Joemer C. and Marks, Ashley and Martins-Melo, Francisco Rogerlândio and Martopullo, Ira and Masoumi, Seyedeh Zahra and Massano, João and Massenburg, Benjamin Ballard and Mathur, Manu Raj and Maulik, Pallab K. and McAlinden, Colm and McGrath, John J. and McKee, Martin and Mehndiratta, Man Mohan and Mehri, Fereshteh and Mehta, Kala M. and Meitei, Wahengbam Bigyananda and Memiah, Peter T. N. and Mendoza, Walter and Menezes, Ritesh G. and Mengesha, Endalkachew Worku and Mengesha, Meresa Berwo and Mereke, Alibek and Meretoja, Atte and Meretoja, Tuomo J. and Mestrovic, Tomislav and Miazgowski, Bartosz and Miazgowski, Tomasz and Michalek, Irmina Maria and Mihretie, Kebadnew Mulatu and Miller, Ted R. and Mills, Edward J. and Mirica, Andreea and Mirrakhimov, Erkin M. and Mirzaei, Hamed and Mirzaei, Maryam and Mirzaei-Alavijeh, Mehdi and Misganaw, Awoke Temesgen and Mithra, Prasanna and Moazen, Babak and Moghadaszadeh, Masoud and Mohamadi, Efat and Mohammad, Dara K. and Mohammad, Yousef and Mezerji, Naser Mohammad Gholi and Mohammadian-Hafshejani, Abdollah and Mohammadifard, Noushin and Mohammadpourhodki, Reza and Mohammed, Shafiu and Mokdad, Ali H. and Molokhia, Mariam and Momen, Natalie C. and Monasta, Lorenzo and Mondello, Stefania and Mooney, Meghan D. and Moosazadeh, Mahmood and Moradi, Ghobad and Moradi, Masoud and Moradi-Lakeh, Maziar and Moradzadeh, Rahmatollah and Moraga, Paula and Morales, Linda and Morawska, Lidia and Velásquez, Ilais Moreno and Morgado-da-Costa, Joana and Morrison, Shane Douglas and Mosser, Jonathan F. and Mouodi, Simin and Mousavi, Seyyed Meysam and Khaneghah, Amin Mousavi and Mueller, Ulrich Otto and Munro, Sandra B. and Muriithi, Moses K. and Musa, Kamarul Imran and Muthupandian, Saravanan and Naderi, Mehdi and Nagarajan, Ahamarshan Jayaraman and Nagel, Gabriele and Naghshtabrizi, Behshad and Nair, Sanjeev and Nandi, Anita K. and Nangia, Vinay and Nansseu, Jobert Richie and Nayak, Vinod C. and Nazari, Javad and Negoi, Ionut and Negoi, Ruxandra Irina and Netsere, Henok Biresaw Netsere and Ngunjiri, Josephine W. and Nguyen, Cuong Tat and Nguyen, Jason and Nguyen, Michele and Nguyen, Minh and Nichols, Emma and Nigatu, Dabere and Nigatu, Yeshambel T. and Nikbakhsh, Rajan and Nixon, Molly R. and Nnaji, Chukwudi A. and Nomura, Shuhei and Norrving, Bo and Noubiap, Jean Jacques and Nowak, Christoph and Nunez-Samudio, Virginia and Oţoiu, Adrian and Oancea, Bogdan and Odell, Christopher M. and Ogbo, Felix Akpojene and Oh, In-Hwan and Okunga, Emmanuel Wandera and Oladnabi, Morteza and Olagunju, Andrew T. and Olusanya, Bolajoko Olubukunola and Olusanya, Jacob Olusegun and Oluwasanu, Mojisola Morenike and Bali, Ahmed Omar and Omer, Muktar Omer and Ong, Kanyin L. and Onwujekwe, Obinna E. and Orji, Aislyn U. and Orpana, Heather M. and Ortiz, Alberto and Ostroff, Samuel M. and Otstavnov, Nikita and Otstavnov, Stanislav S. and Øverland, Simon and Owolabi, Mayowa O. and A, Mahesh P. and Padubidri, Jagadish Rao and Pakhare, Abhijit P. and Palladino, Raffaele and Pana, Adrian and Panda-Jonas, Songhomitra and Pandey, Anamika and Park, Eun-Kee and Parmar, Priya G. Kumari and Pasupula, Deepak Kumar and Patel, Sangram Kishor and Paternina-Caicedo, Angel J. and Pathak, Ashish and Pathak, Mona and Patten, Scott B. and Patton, George C. and Paudel, Deepak and Toroudi, Hamidreza Pazoki and Peden, Amy E. and Pennini, Alyssa and Pepito, Veincent Christian Filipino and Peprah, Emmanuel K. and Pereira, Alexandre and Pereira, David M. and Perico, Norberto and Pham, Hai Quang and Phillips, Michael R. and Pigott, David M. and Pilgrim, Thomas and Pilz, Tessa M. and Pirsaheb, Meghdad and Plana-Ripoll, Oleguer and Plass, Dietrich and Pokhrel, Khem Narayan and Polibin, Roman V. and Polinder, Suzanne and Polkinghorne, Kevan R. and Postma, Maarten J. and Pourjafar, Hadi and Pourmalek, Farshad and Kalhori, Reza Pourmirza and Pourshams, Akram and Poznańska, Anna and Prada, Sergio I. and Prakash, V. and Pribadi, Dimas Ria Angga and Pupillo, Elisabetta and Syed, Zahiruddin Quazi and Rabiee, Mohammad and Rabiee, Navid and Radfar, Amir and Rafiee, Ata and Rafiei, Alireza and Raggi, Alberto and Rahimi-Movaghar, Afarin and Rahman, Muhammad Aziz and Rajabpour-Sanati, Ali and Rajati, Fatemeh and Ramezanzadeh, Kiana and Ranabhat, Chhabi Lal and Rao, Puja C. and Rao, Sowmya J. and Rasella, Davide and Rastogi, Prateek and Rathi, Priya and Rawaf, David Laith and Rawaf, Salman and Rawal, Lal and Razo, Christian and Redford, Sofia Boston and Reiner, Robert C. and Reinig, Nickolas and Reitsma, Marissa Bettay and Remuzzi, Giuseppe and Renjith, Vishnu and Renzaho, Andre M. N. and Resnikoff, Serge and Rezaei, Nima and family=Rezai, given=Mohammad, prefix=sadegh, useprefix=false and Rezapour, Aziz and Rhinehart, Phoebe-Anne and Riahi, Seyed Mohammad and Ribeiro, Antonio Luiz P. and Ribeiro, Daniel Cury and Ribeiro, Daniela and Rickard, Jennifer and Roberts, Nicholas L. S. and Roberts, Shaun and Robinson, Stephen R. and Roever, Leonardo and Rolfe, Sam and Ronfani, Luca and Roshandel, Gholamreza and Roth, Gregory A. and Rubagotti, Enrico and Rumisha, Susan Fred and Sabour, Siamak and Sachdev, Perminder S. and Saddik, Basema and Sadeghi, Ehsan and Sadeghi, Masoumeh and Saeidi, Shahram and Safi, Sare and Safiri, Saeid and Sagar, Rajesh and Sahebkar, Amirhossein and Sahraian, Mohammad Ali and Sajadi, S. Mohammad and Salahshoor, Mohammad Reza and Salamati, Payman and Zahabi, Saleh Salehi and Salem, Hosni and Salem, Marwa R. Rashad and Salimzadeh, Hamideh and Salomon, Joshua A. and Salz, Inbal and Samad, Zainab and Samy, Abdallah M. and Sanabria, Juan and Santomauro, Damian Francesco and Santos, Itamar S. and Santos, João Vasco and Santric-Milicevic, Milena M. and Saraswathy, Sivan Yegnanarayana Iyer and Sarmiento-Suárez, Rodrigo and Sarrafzadegan, Nizal and Sartorius, Benn and Sarveazad, Arash and Sathian, Brijesh and Sathish, Thirunavukkarasu and Sattin, Davide and Sbarra, Alyssa N. and Schaeffer, Lauren E. and Schiavolin, Silvia and Schmidt, Maria Inês and Schutte, Aletta Elisabeth and Schwebel, David C. and Schwendicke, Falk and Senbeta, Anbissa Muleta and Senthilkumaran, Subramanian and Sepanlou, Sadaf G. and Shackelford, Katya Anne and Shadid, Jamileh and Shahabi, Saeed and Shaheen, Amira A. and Shaikh, Masood Ali and Shalash, Ali S. and Shams-Beyranvand, Mehran and Shamsizadeh, Morteza and Shannawaz, Mohammed and Sharafi, Kiomars and Sharara, Fablina and Sheena, Brittney S. and Sheikhtaheri, Abbas and Shetty, Ranjitha S. and Shibuya, Kenji and Shiferaw, Wondimeneh Shibabaw and Shigematsu, Mika and Shin, Jae Il and Shiri, Rahman and Shirkoohi, Reza and Shrime, Mark G. and Shuval, Kerem and Siabani, Soraya and Sigfusdottir, Inga Dora and Sigurvinsdottir, Rannveig and Silva, João Pedro and Simpson, Kyle E. and Singh, Ambrish and Singh, Jasvinder A. and Skiadaresi, Eirini and Skou, Søren T. and Skryabin, Valentin Yurievich and Sobngwi, Eugene and Sokhan, Anton and Soltani, Shahin and Sorensen, Reed J. D. and Soriano, Joan B. and Sorrie, Muluken Bekele and Soyiri, Ireneous N. and Sreeramareddy, Chandrashekhar T. and Stanaway, Jeffrey D. and Stark, Benjamin A. and Ştefan, Simona Cătălina and Stein, Caroline and Steiner, Caitlyn and Steiner, Timothy J. and Stokes, Mark A. and Stovner, Lars Jacob and Stubbs, Jacob L. and Sudaryanto, Agus and Sufiyan, Mu'awiyyah Babale and Sulo, Gerhard and Sultan, Iyad and Sykes, Bryan L. and Sylte, Dillon O. and Szócska, Miklós and Tabarés-Seisdedos, Rafael and Tabb, Karen M. and Tadakamadla, Santosh Kumar and Taherkhani, Amir and Tajdini, Masih and Takahashi, Ken and Taveira, Nuno and Teagle, Whitney L. and Teame, Hirut and Tehrani-Banihashemi, Arash and Teklehaimanot, Berhane Fseha and Terrason, Sonyah and Tessema, Zemenu Tadesse and Thankappan, Kavumpurathu Raman and Thomson, Azalea M. and Tohidinik, Hamid Reza and Tonelli, Marcello and Topor-Madry, Roman and Torre, Anna E. and Touvier, Mathilde and Tovani-Palone, Marcos Roberto Roberto and Tran, Bach Xuan and Travillian, Ravensara and Troeger, Christopher E. and Truelsen, Thomas Clement and Tsai, Alexander C. and Tsatsakis, Aristidis and Car, Lorainne Tudor and Tyrovolas, Stefanos and Uddin, Riaz and Ullah, Saif and Undurraga, Eduardo A. and Unnikrishnan, Bhaskaran and Vacante, Marco and Vakilian, Alireza and Valdez, Pascual R. and Varughese, Santosh and Vasankari, Tommi Juhani and Vasseghian, Yasser and Venketasubramanian, Narayanaswamy and Violante, Francesco S. and Vlassov, Vasily and Vollset, Stein Emil and Vongpradith, Avina and Vukovic, Ana and Vukovic, Rade and Waheed, Yasir and Walters, Madgalene K. and Wang, Jiayu and Wang, Yafeng and Wang, Yuan-Pang and Ward, Joseph L. and Watson, Alexandrea and Wei, Jingkai and Weintraub, Robert G. and Weiss, Daniel J. and Weiss, Jordan and Westerman, Ronny and Whisnant, Joanna L. and Whiteford, Harvey A. and Wiangkham, Taweewat and Wiens, Kirsten E. and Wijeratne, Tissa and Wilner, Lauren B. and Wilson, Shadrach and Wojtyniak, Bogdan and Wolfe, Charles D. A. and Wool, Eve E. and Wu, Ai-Min and Hanson, Sarah Wulf and Wunrow, Han Yong and Xu, Gelin and Xu, Rixing and Yadgir, Simon and Jabbari, Seyed Hossein Yahyazadeh and Yamagishi, Kazumasa and Yaminfirooz, Mousa and Yano, Yuichiro and Yaya, Sanni and Yazdi-Feyzabadi, Vahid and Yearwood, Jamal A. and Yeheyis, Tomas Y. and Yeshitila, Yordanos Gizachew and Yip, Paul and Yonemoto, Naohiro and Yoon, Seok-Jun and Lebni, Javad Yoosefi and Younis, Mustafa Z. and Younker, Theodore Patrick and Yousefi, Zabihollah and Yousefifard, Mahmoud and Yousefinezhadi, Taraneh and Yousuf, Abdilahi Yousuf and Yu, Chuanhua and Yusefzadeh, Hasan and Moghadam, Telma Zahirian and Zaki, Leila and Zaman, Sojib Bin and Zamani, Mohammad and Zamanian, Maryam and Zandian, Hamed and Zangeneh, Alireza and Zastrozhin, Mikhail Sergeevich and Zewdie, Kaleab Alemayehu and Zhang, Yunquan and Zhang, Zhi-Jiang and Zhao, Jeff T. and Zhao, Yingxi and Zheng, Peng and Zhou, Maigeng and Ziapour, Arash and Zimsen, Stephanie R. M. and Naghavi, Mohsen and Murray, Christopher J. L.},
|
|
|
date = {2020-10-17},
|
|
|
journaltitle = {The Lancet},
|
|
|
shortjournal = {The Lancet},
|
|
|
volume = {396},
|
|
|
number = {10258},
|
|
|
eprint = {33069326},
|
|
|
eprinttype = {pmid},
|
|
|
pages = {1204--1222},
|
|
|
publisher = {Elsevier},
|
|
|
issn = {0140-6736, 1474-547X},
|
|
|
doi = {10.1016/S0140-6736(20)30925-9},
|
|
|
url = {https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30925-9/fulltext},
|
|
|
urldate = {2023-03-14},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/PL3HFMXA/Vos et al. - 2020 - Global burden of 369 diseases and injuries in 204 .pdf}
|
|
|
}
|
|
|
|
|
|
@article{wang_realtimemonitoring_2022,
|
|
|
title = {Real Time Monitoring and Prediction of Time to Endpoint Maturation in Clinical Trials},
|
|
|
author = {Wang, Li and Liu, Yang and Chen, Xiaotian and Pulkstenis, Erik},
|
|
|
date = {2022-08-15},
|
|
|
journaltitle = {Statistics in Medicine},
|
|
|
shortjournal = {Statistics in Medicine},
|
|
|
volume = {41},
|
|
|
number = {18},
|
|
|
pages = {3596--3611},
|
|
|
issn = {0277-6715, 1097-0258},
|
|
|
doi = {10.1002/sim.9436},
|
|
|
url = {https://onlinelibrary.wiley.com/doi/10.1002/sim.9436},
|
|
|
urldate = {2023-05-03},
|
|
|
langid = {english}
|
|
|
}
|
|
|
|
|
|
@article{waring_analysisattritiondrug_2015,
|
|
|
title = {An Analysis of the Attrition of Drug Candidates from Four Major Pharmaceutical Companies},
|
|
|
author = {Waring, Michael J. and Arrowsmith, John and Leach, Andrew R. and Leeson, Paul D. and Mandrell, Sam and Owen, Robert M. and Pairaudeau, Garry and Pennie, William D. and Pickett, Stephen D. and Wang, Jibo and Wallace, Owen and Weir, Alex},
|
|
|
date = {2015-07},
|
|
|
journaltitle = {Nature Reviews Drug Discovery},
|
|
|
shortjournal = {Nat Rev Drug Discov},
|
|
|
volume = {14},
|
|
|
number = {7},
|
|
|
pages = {475--486},
|
|
|
issn = {1474-1776, 1474-1784},
|
|
|
doi = {10.1038/nrd4609},
|
|
|
url = {http://www.nature.com/articles/nrd4609},
|
|
|
urldate = {2023-01-31},
|
|
|
abstract = {The pharmaceutical industry remains under huge pressure to address the high attrition rates in drug development. Attempts to reduce the number of efficacy- and safety-related failures by analysing possible links to the physicochemical properties of small-molecule drug candidates have been inconclusive because of the limited size of data sets from individual companies. Here, we describe the compilation and analysis of combined data on the attrition of drug candidates from AstraZeneca, Eli Lilly and Company, GlaxoSmithKline and Pfizer. The analysis reaffirms that control of physicochemical properties during compound optimization is beneficial in identifying compounds of candidate drug quality and indicates for the first time a link between the physicochemical properties of compounds and clinical failure due to safety issues. The results also suggest that further control of physicochemical properties is unlikely to have a significant effect on attrition rates and that additional work is required to address safety-related failures. Further cross-company collaborations will be crucial to future progress in this area.},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/HD3WAS2A/Waring et al. - 2015 - An analysis of the attrition of drug candidates fr.pdf;/home/will/Zotero/storage/SJIM9P79/Waring et al. - 2015 - An analysis of the attrition of drug candidates fr.pdf}
|
|
|
}
|
|
|
|
|
|
@online{who_icd-10_2023,
|
|
|
title = {International {{Classification}} of {{Diseases}} ({{ICD}})},
|
|
|
author = {{World Health Organization}},
|
|
|
url = {https://www.who.int/standards/classifications/classification-of-diseases},
|
|
|
urldate = {2023-04-09},
|
|
|
abstract = {International Classification of Diseases (ICD) Revision},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/4Y3F35AR/classification-of-diseases.html}
|
|
|
}
|
|
|
|
|
|
@article{wong_estimationclinicaltrial_2019,
|
|
|
title = {Estimation of Clinical Trial Success Rates and Related Parameters},
|
|
|
author = {Wong, Chi Heem and Siah, Kien Wei and Lo, Andrew W},
|
|
|
date = {2019-04-01},
|
|
|
journaltitle = {Biostatistics},
|
|
|
volume = {20},
|
|
|
number = {2},
|
|
|
pages = {273--286},
|
|
|
issn = {1465-4644, 1468-4357},
|
|
|
doi = {10.1093/biostatistics/kxx069},
|
|
|
url = {https://academic.oup.com/biostatistics/article/20/2/273/4817524},
|
|
|
urldate = {2024-05-25},
|
|
|
abstract = {Previous estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases. Using a sample of 406 038 entries of clinical trial data for over 21 143 compounds from January 1, 2000 to October 31, 2015, we estimate aggregate clinical trial success rates and durations. We also compute disaggregated estimates across several trial features including disease type, clinical phase, industry or academic sponsor, biomarker presence, lead indication status, and time. In several cases, our results differ significantly in detail from widely cited statistics. For example, oncology has a 3.4\% success rate in our sample vs. 5.1\% in prior studies. However, after declining to 1.7\% in 2012, this rate has improved to 2.5\% and 8.3\% in 2014 and 2015, respectively. In addition, trials that use biomarkers in patient-selection have higher overall success probabilities than trials without biomarkers.},
|
|
|
langid = {english},
|
|
|
keywords = {To Process,To Read},
|
|
|
file = {/home/will/Zotero/storage/YBKSGRWT/Wong et al. - 2019 - Estimation of clinical trial success rates and rel.pdf}
|
|
|
}
|
|
|
|
|
|
@online{worldhealthorganization_icd10version2019_,
|
|
|
title = {{{ICD-10 Version}}:2019},
|
|
|
author = {{World Health Organization}},
|
|
|
url = {https://icd.who.int/browse10/2019/en},
|
|
|
urldate = {2025-01-20},
|
|
|
organization = {ICD-10 Version:2019},
|
|
|
keywords = {ICD-10},
|
|
|
file = {/home/will/Zotero/storage/5T8CPB6H/en.html}
|
|
|
}
|
|
|
|
|
|
@article{zhang_jointmonitoringprediction_2012,
|
|
|
title = {Joint Monitoring and Prediction of Accrual and Event Times in Clinical Trials: {{Prediction}} of Accrual and Event Times in Clinical Trials},
|
|
|
shorttitle = {Joint Monitoring and Prediction of Accrual and Event Times in Clinical Trials},
|
|
|
author = {Zhang, Xiaoxi and Long, Qi},
|
|
|
date = {2012-11},
|
|
|
journaltitle = {Biometrical Journal},
|
|
|
shortjournal = {Biom. J.},
|
|
|
volume = {54},
|
|
|
number = {6},
|
|
|
pages = {735--749},
|
|
|
issn = {03233847},
|
|
|
doi = {10.1002/bimj.201100180},
|
|
|
url = {https://onlinelibrary.wiley.com/doi/10.1002/bimj.201100180},
|
|
|
urldate = {2023-04-27},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/XVW43JDP/Zhang and Long - 2012 - Joint monitoring and prediction of accrual and eve.pdf;/home/will/Zotero/storage/ZK6QNHST/Zhang and Long - 2012 - Joint monitoring and prediction of accrual and eve.pdf}
|
|
|
}
|
|
|
|
|
|
@article{zhang_modelingpredictionsubject_2012,
|
|
|
title = {Modeling and Prediction of Subject Accrual and Event Times in Clinical Trials: A Systematic Review},
|
|
|
shorttitle = {Modeling and Prediction of Subject Accrual and Event Times in Clinical Trials},
|
|
|
author = {Zhang, Xiaoxi and Long, Qi},
|
|
|
date = {2012-12},
|
|
|
journaltitle = {Clinical Trials},
|
|
|
shortjournal = {Clinical Trials},
|
|
|
volume = {9},
|
|
|
number = {6},
|
|
|
pages = {681--688},
|
|
|
issn = {1740-7745, 1740-7753},
|
|
|
doi = {10.1177/1740774512447996},
|
|
|
url = {http://journals.sagepub.com/doi/10.1177/1740774512447996},
|
|
|
urldate = {2023-04-27},
|
|
|
abstract = {Background Modeling and prediction of subject accrual and event times in clinical trials has been a topic of considerable interest for important practical reasons. It has implications not only at the initial planning stage of a trial but also on its ongoing monitoring. Purpose To provide a systematic view of the recent research in the field of modeling and prediction of subject accrual and event times in clinical trials. Methods Two classes of methods for modeling and prediction of subject accrual are reviewed, namely, one that uses the Brownian motion and the other uses the Poisson process. Extensions of the accrual models in multicenter clinical trials are also discussed. Trials with survival endpoints require proper joint modeling of subject accrual and event/lost-to-follow-up (LTFU) times, the latter of which can be modeled either parametrically (e.g., exponential and Weibull) or nonparametrically. Results Flexible stochastic models are better suited when modeling real trials that does not follow constant underlying enrollment rate. The accrual model generally improves as center-specific information is accounted for in multicenter trials. The choice between parametric and nonparametric event models can depend on confidence on the underlying event rates. Limitations All methods reviewed in event modeling assume noninformative censoring, which cannot be tested. Conclusions We recommend using proper stochastic accrual models, in combination with flexible event time models when applicable, for modeling and prediction of subject enrollment and event times in clinical trials.},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/L6L5R3UH/Zhang and Long - 2012 - Modeling and prediction of subject accrual and eve.pdf;/home/will/Zotero/storage/LRAUPM73/1740774512447996.rflw.epub;/home/will/Zotero/storage/XHHH3BSC/1740774512447996.rflw.epub}
|
|
|
}
|
|
|
|
|
|
@article{zhang_simplerobustmodel_2022,
|
|
|
title = {A Simple and Robust Model for Enrollment Projection in Clinical Trials},
|
|
|
author = {Zhang, Xiaoxi and Huang, Bo},
|
|
|
date = {2022-12},
|
|
|
journaltitle = {Contemporary Clinical Trials},
|
|
|
shortjournal = {Contemporary Clinical Trials},
|
|
|
volume = {123},
|
|
|
pages = {106999},
|
|
|
issn = {15517144},
|
|
|
doi = {10.1016/j.cct.2022.106999},
|
|
|
url = {https://linkinghub.elsevier.com/retrieve/pii/S1551714422003251},
|
|
|
urldate = {2023-05-03},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/E77EK2MA/Zhang and Huang - 2022 - A simple and robust model for enrollment projectio.pdf;/home/will/Zotero/storage/UAPA9EW4/Zhang and Huang - 2022 - A simple and robust model for enrollment projectio.pdf}
|
|
|
}
|
|
|
|
|
|
@article{zhang_stochasticmodelingprediction_2010,
|
|
|
title = {Stochastic Modeling and Prediction for Accrual in Clinical Trials},
|
|
|
author = {Zhang, Xiaoxi and Long, Qi},
|
|
|
date = {2010},
|
|
|
journaltitle = {Statistics in Medicine},
|
|
|
shortjournal = {Statist. Med.},
|
|
|
pages = {n/a-n/a},
|
|
|
issn = {02776715, 10970258},
|
|
|
doi = {10.1002/sim.3847},
|
|
|
url = {https://onlinelibrary.wiley.com/doi/10.1002/sim.3847},
|
|
|
urldate = {2023-04-27},
|
|
|
langid = {english},
|
|
|
file = {/home/will/Zotero/storage/2ZLB3KSW/Zhang and Long - 2010 - Stochastic modeling and prediction for accrual in .pdf;/home/will/Zotero/storage/VIB8DMAI/Zhang and Long - 2010 - Stochastic modeling and prediction for accrual in .pdf}
|
|
|
}
|