Merge branch 'rewrite_section'
Will King
1 year ago
commit
52a88bcd61
@ -1 +1 @@
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Subproject commit c25565274403e454f03bdb2d7f72cf108a0db213
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Subproject commit d25f5c2a0e672c361937e8c3b490a575714b8ec1
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@ -0,0 +1,84 @@
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||||
// This tab is where I manage main from.
|
||||
// it opens up Main.txt for my JMP, opens the pdf in okular (in a floating tab), and then get's ready to build the pdf.
|
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|
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|
||||
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|
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|
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|
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|
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|
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|
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@ -0,0 +1,84 @@
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|
||||
tab name="Main and Compile" cwd="~/research/phd_deliverables/jmp/Latex/Paper" hide_floating_panes=true focus=true {
|
||||
// This tab is where I manage main from.
|
||||
// it opens up Main.txt for my JMP, opens the pdf in okular (in a floating tab), and then get's ready to build the pdf.
|
||||
pane size=1 borderless=true {
|
||||
plugin location="tab-bar"
|
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// here is where I run okular from, it is auto hidden
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focus true
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@ -0,0 +1,18 @@
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NEXT STEPS IN WRITING
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- insert a description of the general approach I use:
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- predicting, based on snapshots, the likelihood of termination.
|
||||
- this needs to go between the description of the snapshots and the
|
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causal inference introduction.
|
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- Then I can use what I've written about the graph, and follow up with more information about the data.
|
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|
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Overall this would look like
|
||||
|
||||
- [x] Introduction of the question and general issues of confoundedness.
|
||||
- [x] Clinical Trials Data Sources
|
||||
- [x] Explain basic econometric modelling approach
|
||||
- [ ] Then explain the graph, nodes, and confoundedness in more detail
|
||||
- [ ] Then go over the rest of the data.
|
||||
- [ ] Finally
|
||||
- Discuss the number of datapoints.
|
||||
- review major challenges to causal identification. (no enrollment model small data size)
|
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@ -0,0 +1,34 @@
|
||||
Outlining for jmp
|
||||
<intro>
|
||||
Introduction and problem statement
|
||||
*Explain what I am doing:*
|
||||
</intro>
|
||||
<literature
|
||||
Describe what has been done
|
||||
- measuring failure rates & impact
|
||||
Introduce different types of failure
|
||||
- Scientific
|
||||
- Strategic
|
||||
- Operational
|
||||
Efforts to measure failures
|
||||
Medbio story to illuistrate failure modes.
|
||||
Operational and strategic failures undermine scientific process of discovery
|
||||
*My effort is to separate...*: place my work in context
|
||||
Introduce clinical trials' progressions, stages, and statuses.
|
||||
</literature>
|
||||
<causal model>
|
||||
Derive causal model
|
||||
</causal model>
|
||||
<data>
|
||||
Summarize data sources
|
||||
</data>
|
||||
<econometrics>
|
||||
Introduce econometric model
|
||||
</econmetrics>
|
||||
<results>
|
||||
Discuss econometric results
|
||||
</results>
|
||||
Conclusion
|
||||
Appendicies
|
||||
- in-depth data source info
|
||||
- More econometric results
|
||||
@ -0,0 +1,58 @@
|
||||
In 19xx the United States Food and Drug Administration (FDA) was created to "QUOTE".
|
||||
As of Sept 2022 \todo{Check Date} they have approved 6,602 currently-marketed compounds with Structured Product Labels (SPL)
|
||||
and 10,983 previously-marketed SPLs.
|
||||
%from nsde table. Get number of unique application_nubmers_or_citations with most recent end date as null.
|
||||
In 2007, they began requiring that drug developers register and publish clinical trials on \url{https://clinicaltrials.gov}.
|
||||
This provides a public mechanism where clinical trial sponsors are responsible to explain
|
||||
what they are trying to acheive and how it will be measured, as well as provide the public the ability to
|
||||
search and find trials that they might enroll in.
|
||||
Data such as this has become part of multiple datasets
|
||||
(e.g. the Cortellis Investigational Drugs dataset or the AACT dataset from the Clinical Trials Transformation Intiative)
|
||||
used to evaluate what drugs might be entering the market soon.
|
||||
This brings up a question: can we use this public data on clinical trials to describe what effects their success or failure?
|
||||
In this work, I use updates to records on \url{https://ClinicalTrials.gov} to disentangle
|
||||
the effect of participant enrollment and drugs on the market affect the success or failure of clinical trials.
|
||||
|
||||
%Describe how clinical trials fit into the drug development landscape and how they proceed
|
||||
Clinical trials are a required part of drug development.
|
||||
Not only does the FDA require that a series of clinical trials demonstrate sufficient safety and efficacy of
|
||||
a novel pharmaceutical compound or device, producers of derivative medicines may be required to ensure that
|
||||
their generic small molecule compound -- such as ibuprofen or levothyroxine -- matches the
|
||||
performance of the originiator drug if delivery or dosage is changed.
|
||||
For large molecule generics (termed biosimilars) such as Adalimumab
|
||||
(Brand name Humira, with biosimilars Abrilada, Amjevita, Cyltezo, Hadlima, Hulio,
|
||||
Hyrimoz, Idacio, Simlandi, Yuflyma, and Yusimry),
|
||||
the biosimilars are required to prove they have similar efficacy and safety to the
|
||||
reference drug.
|
||||
|
||||
When registering a clinical trial,
|
||||
the investigators are required to
|
||||
% discuss how these are registered and what data is published.
|
||||
% Include image and discuss stages
|
||||
% Discuss challenges faced
|
||||
|
||||
% Introduce my work
|
||||
|
||||
In the world of drug development, these trials are classified into different phases of development.
|
||||
Pre-clinical studies may include
|
||||
Phase I trials are the first attempt to evaluate safety and efficacy in humans, and usually \todo{Describe trial phases, get citation}
|
||||
Phase II trials typically \todo{}
|
||||
A Phase III trial is the final trial befor approval by the FDA
|
||||
Phase IV trials are used after approval to ensure safety and efficacy in the general populace ....
|
||||
|
||||
In the economics literature, most of the focus has been on evaluating how drug candidates transition between
|
||||
different phases and then on to approval.
|
||||
|
||||
% Now begin introducing work by Chris Adams
|
||||
% Lead into lit review
|
||||
|
||||
|
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% Causality
|
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% Data
|
||||
|
||||
% Economic Model
|
||||
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% Results
|
||||
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||||
% Conclusion
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@ -0,0 +1,42 @@
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How do I begin work on stuff
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- next step is causal story. key points include
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||||
- we are trying to separate strategic and operational concerns. (why is this a difficult problem?)
|
||||
- we can't trust what we are told
|
||||
- terminations could be due to safety, strategic, or operational concerns.
|
||||
- explaining confounding between
|
||||
- population/market and enrollment.
|
||||
-population/market and market conditions.
|
||||
- market conditions and enrollment.
|
||||
- describe other confounders
|
||||
- safety and effectiveness
|
||||
- duration <--> enrollment/termination
|
||||
- Condition
|
||||
- Decision to procede with Phase III trial
|
||||
- How do I handle this?
|
||||
- Introduce Do-Calculus
|
||||
- DAG model
|
||||
- What do I need to control for, in some form or other?
|
||||
CURRENTLY HERE:
|
||||
- Introduce Data
|
||||
- Clinical Trial Progression
|
||||
- AACT gives us information on
|
||||
- terminated/completed status
|
||||
- compound-indication pairs
|
||||
- MeSH/RxNorm links
|
||||
- Snapshots
|
||||
- Market Conditions
|
||||
- can't directly measure alternate treatments/standards of care.
|
||||
- Can get measures of USP - formulary alternatives
|
||||
- Can get number of generics or brand names with same drug.
|
||||
- Population Sizes
|
||||
- IHME Global Burden of Disease dataset. Best measure of impact of a given disease category.
|
||||
- DALY's
|
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- How much data do I have?
|
||||
- Econometric model
|
||||
- for a given state, what is the probability it will terminate?
|
||||
- more accurately for my dist-diff analysis: for a given state, what is the distribution of the probabilities it will terminate?
|
||||
- basic bernoulli-logistic model, linear in parameters.
|
||||
- What are the specific things I am looking at?
|
||||
- number of competing treatments.
|
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- delaying close of enrollment.
|
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@ -0,0 +1,318 @@
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\documentclass[../Main.tex]{subfiles}
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\graphicspath{{\subfix{Assets/img/}}}
|
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\begin{document}
|
||||
|
||||
In 1938 President Franklin D Rosevelt signed the Food, Drug, and Cosmetic Act,
|
||||
granting the Food and Drug Administration (FDA) authority to require
|
||||
pre-market approval of pharmaceuticals.
|
||||
\cite{commissioner_MilestonesUS_2023}.
|
||||
As of Sept 2022 \todo{Check Date} they have approved 6,602 currently-marketed
|
||||
compounds with Structured Product Labels (SPLs)
|
||||
and 10,983 previously-marketed SPLs
|
||||
\cite{commissioner_NSDE_2024}.
|
||||
%from nsde table. Get number of unique application_nubmers_or_citations with most recent end date as null.
|
||||
In 1999, they began requiring that drug developers register and
|
||||
publish clinical trials on \url{https://clinicaltrials.gov}.
|
||||
This provides a public mechanism where clinical trial sponsors are
|
||||
responsible to explain what they are trying to acheive and how it will be
|
||||
measured, as well as provide the public the ability to search and find trials
|
||||
that they might enroll in.
|
||||
Multiple derived datasets such as the Cortellis Investigational Drugs dataset
|
||||
or the AACT dataset from the Clinical Trials Transformation Intiative
|
||||
integrate these data.
|
||||
This brings up a question:
|
||||
Can we use this public data on clinical trials to identify what effects the
|
||||
success or failure of trials?
|
||||
In this work, I use updates to records on
|
||||
\url{https://ClinicalTrials.gov}
|
||||
to do exactly that, disentangle the effect of participant enrollment
|
||||
and competing drugs on the market affect the success or failure of
|
||||
clinical trials.
|
||||
|
||||
%Describe how clinical trials fit into the drug development landscape and how they proceed
|
||||
Clinical trials are a required part of drug development.
|
||||
Not only does the FDA require that a series of clinical trials demonstrate sufficient safety and efficacy of
|
||||
a novel pharmaceutical compound or device, producers of derivative medicines may be required to ensure that
|
||||
their generic small molecule compound -- such as ibuprofen or levothyroxine -- matches the
|
||||
performance of the originiator drug if delivery or dosage is changed.
|
||||
For large molecule generics (termed biosimilars) such as Adalimumab
|
||||
(Brand name Humira, with biosimilars Abrilada, Amjevita, Cyltezo, Hadlima, Hulio,
|
||||
Hyrimoz, Idacio, Simlandi, Yuflyma, and Yusimry),
|
||||
the biosimilars are required to prove they have similar efficacy and safety to the
|
||||
reference drug.
|
||||
|
||||
When registering these clinical trials
|
||||
% discuss how these are registered and what data is published.
|
||||
% Include image and discuss stages
|
||||
% Discuss challenges faced
|
||||
|
||||
% Introduce my work
|
||||
|
||||
In the world of drug development, these trials are classified into different
|
||||
phases of development.
|
||||
\cite{FDADrugApprovalProcess_2022}
|
||||
provide an overview of this process
|
||||
\cite{commissioner_DrugDevelopment_2020}
|
||||
while describes the actual details.
|
||||
Pre-clinical studies primarily establish toxicity and potential dosing levels
|
||||
\cite{commissioner_DrugDevelopment_2020}.
|
||||
Phase I trials are the first attempt to evaluate safety and efficacy in humans.
|
||||
Participants typically are heathy individuals, and they measure how the drug
|
||||
affects healthy bodies, potential side effects, and adjust dosing levels.
|
||||
Sample sizes are often less than 100 participants.
|
||||
\cite{commissioner_DrugDevelopment_2020}.
|
||||
Phase II trials typically involve a few hundred participants and is where
|
||||
investigators will dial in dosing, research methods, and safety.
|
||||
\cite{commissioner_DrugDevelopment_2020}.
|
||||
A Phase III trial is the final trial befor approval by the FDA, and is where
|
||||
the investigator must demonstrate safety and efficacy with a large number of
|
||||
participants, usually on the order of hundreds or thousands.
|
||||
\cite{commissioner_DrugDevelopment_2020}.
|
||||
Occassionally, a trial will be a multiphase trial, covering aspects of either
|
||||
Phases I and II or Phases II and III.
|
||||
|
||||
|
||||
After a successful Phase III trial, the sponsor will decide whether or not
|
||||
to submit an application for approval from the FDA.
|
||||
Before filing this application, the developer must have completed
|
||||
"two large, controlled clinical trials."
|
||||
\cite{commissioner_DrugDevelopment_2020}.
|
||||
Phase IV trials are used after the drug has recieved marketing approval to
|
||||
validate safety and efficacy in the general populace.
|
||||
Throughout this whole process, the FDA is available to assist in decisionmaking
|
||||
regarding topics such as study design, document review, and whether or not
|
||||
they should terminate the trial.
|
||||
The FDA also reserves the right to place a hold on the clinical trial for
|
||||
safety or other operational concerns, although this is rare.
|
||||
\cite{commissioner_DrugDevelopment_2020}.
|
||||
|
||||
In the economics literature, most of the focus has been on evaluating how
|
||||
drug candidates transition between different phases and their probability
|
||||
of final approval.
|
||||
% Lead into lit review
|
||||
% Abrantes-Metz, Adams, Metz (2004)
|
||||
\cite{abrantes-metz_pharmaceutical_2004},
|
||||
described the relationship between
|
||||
various drug characteristics and how the drug progressed through clinical trials.
|
||||
% This descriptive estimate was notable for using a
|
||||
% mixed state proportional hazard model and estimating the impact of
|
||||
% observed characteristics in each of the three phases.
|
||||
They found that as Phase I and II trials last longer,
|
||||
the rate of failure increases.
|
||||
In contrast, Phase 3 trials generally have a higher rate of
|
||||
success than failure after 91 months.
|
||||
This may be due to the fact that the purpose of Phases I and II are different
|
||||
from the purpose of Phase III.
|
||||
|
||||
Continuing on this theme,
|
||||
%DiMasi FeldmanSeckler Wilson 2009
|
||||
\cite{dimasi_TrendsRisks_2010} examine the completion rate of clinical drug
|
||||
develompent and find that for the 50 largest drug producers,
|
||||
approximately 19\% of their drugs under development between 1993 and 2004
|
||||
successfully moved from Phase I to recieving an New Drug Application (NDA)
|
||||
or Biologics License Application (BLA).
|
||||
They note a couple of changes in how drugs are developed over the years they
|
||||
study, most notably that
|
||||
drugs began to fail earlier in their development cycle in the
|
||||
latter half of the time they studied.
|
||||
They note that this may reduce the cost of new drugs by eliminating late
|
||||
and costly failures in the development pipeline.
|
||||
|
||||
Earlier work by
|
||||
\authorcite{dimasi_ValueImproving_2002}
|
||||
used data on 68 investigational drugs from 10 firms to simulate how reducing
|
||||
time in development reduces the costs of developing drugs.
|
||||
He estimates that reducing Phase III of clinical trials by one year would
|
||||
reduce total costs by about 8.9\% and that moving 5\% of clinical trial failures
|
||||
from phase III to Phase II would reduce out of pocket costs by 5.6\%.
|
||||
|
||||
Like much of the work in this field, the focus of the the work by
|
||||
\citeauthor{dimasi_ValueImproving_2002}
|
||||
and
|
||||
\citeauthor{dimasi_TrendsRisks_2010}
|
||||
tends to be on the drug development pipeline, i.e. the progression between
|
||||
phases and towards marketing approval.
|
||||
A key contribution to this drug development literature is the work by
|
||||
\authorcite{khmelnitskaya_CompetitionAttrition_2021}
|
||||
on a causal identification strategy
|
||||
to disentangle strategic exits from exits due to clinical failures
|
||||
in the drug development pipeline.
|
||||
She found that overall 8.4\% of all pipeline exits are due to strategic
|
||||
terminations and that the rate of new drug production would be about 23\%
|
||||
higher if those strategic terminatations were elimintated.
|
||||
|
||||
The work that is closest to mine is the work by
|
||||
\authorcite{hwang_FailureInvestigational_2016}
|
||||
who investigated causes for which late stage (Phase III)
|
||||
clinical trials fail -- with a focus on trials in the USA,
|
||||
Europe, Japan, Canada, and Australia.
|
||||
They identified 640 novel therapies and then studied each therapy's
|
||||
development history, as outlined in commercial datasets.
|
||||
They found that for late stage trials that did not go on to recieve approval,
|
||||
57\% failed on efficacy grounds, 17\% failed on safety grounds, and 22\% failed
|
||||
on commercial or other grounds.
|
||||
|
||||
% Begin Discussing what I do. Then introduce
|
||||
Unlike the majority of the literature, I focus on the progress of
|
||||
individual clinical trials, not on the drug development pipeline.
|
||||
In both
|
||||
\authorcite{khmelnitskaya_CompetitionAttrition_2021}
|
||||
and
|
||||
\authorcite{hwang_FailureInvestigational_2016}
|
||||
the authors describe failures due to safety, efficacy, or strategic concerns.
|
||||
There is another category of concerns that arise for individual clinical trials,
|
||||
that of operational failures.
|
||||
Operational failures can arise when a trial struggles to recruit participants,
|
||||
the principle investigator or other key member leaves for another opportunity,
|
||||
or other studies prove that the trial requires a protocol change.
|
||||
|
||||
% In a personal review of 199 randomly selected clinical trials from the AACT
|
||||
% database, the
|
||||
% \begin{table}
|
||||
% \caption{}\label{tab:}
|
||||
% \begin{center}
|
||||
% \begin{tabular}[c]{|l|l|}
|
||||
% \hline
|
||||
% Reason & Percentage Mentioned \\
|
||||
% \hline
|
||||
% Safety or Efficacy & 14.5\% \\
|
||||
% Funding Problems & 9.1\% \\
|
||||
% Enrollment Issues & 31\% \\
|
||||
% \hline
|
||||
% \end{tabular}
|
||||
% \end{center}
|
||||
% \end{table}
|
||||
|
||||
|
||||
|
||||
This paper proposes the first model to separate the causal effects of
|
||||
market conditions (a strategic concern) from the effects of
|
||||
participant enrollment (an operational concern) on Phase III Clinical trials.
|
||||
This will allow me to answer the questions:
|
||||
\begin{itemize}
|
||||
\item What is the marginal effect on trial completion of an additional
|
||||
generic drug on the market?
|
||||
\item What is the marginal effect on trial completion of a delay in
|
||||
closing enrollment?
|
||||
\end{itemize}
|
||||
To undderstand how I do this, we'll cover some background information on
|
||||
clinical trials in section \ref{SEC:ClinicalTrials},
|
||||
explain the data in section \ref{SEC:DataSources},
|
||||
and then examine causal identification and econometric model in sections
|
||||
\ref{SEC:CausalIdentificationAndModel}.
|
||||
Finally I'll review the results and conclusion in sections
|
||||
\ref{SEC:Results}
|
||||
and
|
||||
\ref{SEC:Conclusion}
|
||||
respectively.
|
||||
|
||||
% \subsection{Market incentives and drug development}
|
||||
% %%%%%%%%% What do we know about drug development incentives?
|
||||
%
|
||||
% \cite{dranove_DoesConsumer_2022} use the implementation of Medicare part D
|
||||
% to examine whether the production of novel or follow up drugs increases during
|
||||
% the following 15 years.
|
||||
% They find that when Medicare part D was implemented -- increasing senior
|
||||
% citizens' ability to pay for drugs -- there was a (delayed) increase
|
||||
% in drug development, with effects concentrated among compounds that were least
|
||||
% innovative according to their classification of innovations.
|
||||
% They suggest that this is due to financial risk management, as novel
|
||||
% pharmaceuticals have a higher probability of failure compared to the less novel
|
||||
% follow up development.
|
||||
% This is what leads risk-adverse companies to prefer follow up development.
|
||||
%
|
||||
%
|
||||
% % Acemoglu and Linn
|
||||
% % - Market size in innovation
|
||||
% % - Exogenous demographic trends has a large impact on the entry of non-generic drugs and new molecular entitites.
|
||||
% On the side of market analysis,
|
||||
% \citeauthor{acemoglu_market_2004}
|
||||
% (\citeyear{acemoglu_market_2004})
|
||||
% used exogenous deomographics changes to show that the
|
||||
% entry of novel compounds is highly driven by the underlying aged population.
|
||||
% They estimate that a 1\% increase in applicable demographics increase the
|
||||
% entry of new drugs by 6\%, mostly concentrated among generics.
|
||||
% Among non-generics, a 1\% increase in potential market size
|
||||
% (as measured by demographic groups) leads to a 4\% increase in novel therapies.
|
||||
%
|
||||
% % Gupta
|
||||
% % - Inperfect intellectual property rights in the pharmaceutical industry
|
||||
% \cite{gupta_OneProduct_2020} discovered that uncertainty around which patents
|
||||
% might apply to a novel drug causes a delay in the entry of generics after
|
||||
% the primary patent has expired.
|
||||
% She found that this delay in delivery is around 3 years.
|
||||
%
|
||||
% % Agarwal and Gaule 2022
|
||||
% % - Retrospective on impact from COVID-19 pandemic
|
||||
% % Not in this version
|
||||
%
|
||||
% \subsection{Understanding Failures in Drug Development}
|
||||
%
|
||||
% % DISCUSS: Different types of failures
|
||||
% There are myriad of reasons that a drug candidate may not make it to market,
|
||||
% regardless of it's novelty or known safety.
|
||||
% In this work, I focus on the failure of individual clinical trials, but the
|
||||
% categories of failure apply to the individual trials as well as the entire
|
||||
% drug development pipeline.
|
||||
% They generally fall into one of the following categories:
|
||||
% \begin{itemize}
|
||||
% \item Scientific Failure: When there are issues regarding
|
||||
% safety and efficacy that must be addressed.
|
||||
% The preeminient question is:
|
||||
% ``Will the drug work for patients?''
|
||||
% %E.Khm, Gupta, etc.
|
||||
% \item Strategic Failure: When the sponsors stop development because of
|
||||
% profitability
|
||||
% %Whether or not the drug will be profitiable, or align with
|
||||
% %the drug developer's future Research \& Development directions i.e.
|
||||
% ``Will producing the drug be beneficial to the
|
||||
% company in the long term?''
|
||||
% %E.Khm, Gupta, GLP-1s, etc.
|
||||
% \item Operational concerns are answers to:
|
||||
% %Whether or not the developer can successfully conduct
|
||||
% %operations to meet scientific or strategic goals, i.e.
|
||||
% ``What has prevented the the company from being able to
|
||||
% finance, develop, produce, and market the drug?''
|
||||
% \end{itemize}
|
||||
% It is likely that a drug fails to complete the development cycle due to some
|
||||
% combination of these factors.
|
||||
%
|
||||
%
|
||||
% %USE MetaBio/CalBio GLP-1 story to illuistrate these different factors.
|
||||
% \cite{flier_DrugDevelopment_2024} documents the case of MetaBio, a company
|
||||
% he was involved in founding that was in the first stages of
|
||||
% developing a GLP-1 based drug for diabetes or obesety before being shut down
|
||||
% in .
|
||||
% MetaBio was a wholy owned subsidiary of CalBio, a metabolic drug development
|
||||
% firm, that recieved a \$30 million -- 5 year investment from Pfizer to
|
||||
% persue development of GLP-1 based therapies.
|
||||
% At the time it was shut down, it faced a few challenges:
|
||||
% \begin{itemize}
|
||||
% \item The compound had a short half life and they were seeking methods to
|
||||
% improve it's effectiveness; a scientific failure.
|
||||
% \item Pfizer imposed a requirement that it be delivered though a route
|
||||
% other than injection (the known delivery mechanism); a strategic failure.
|
||||
% \item When Pfizer pulled the plug, CalBio closed MetaBio because they
|
||||
% could not find other funding sources; an operational failure.
|
||||
% \end{itemize}
|
||||
%
|
||||
% The author states in his conclusion:
|
||||
% \begin{displayquote}
|
||||
% Despite every possibility of success,
|
||||
% MetaBio went down because there were mistaken ideas about what was
|
||||
% possible and what was not in the realm of metabolic therapeutics, and
|
||||
% because proper corporate structure and adequate capital are always
|
||||
% issues when attempting to survive predictable setbacks.
|
||||
% \end{displayquote}
|
||||
%
|
||||
% From this we see that there was a cascade of issues leading to the failure to
|
||||
% develop this novel drug.
|
||||
%
|
||||
%
|
||||
% % I don't think I need to include modelling enrollment here.
|
||||
% % If it is applicable, it can show up in those sections later.
|
||||
%
|
||||
%
|
||||
|
||||
\end{document}
|
||||
@ -0,0 +1,93 @@
|
||||
\documentclass[../Main.tex]{subfiles}
|
||||
\graphicspath{{\subfix{Assets/img/}}}
|
||||
|
||||
\begin{document}
|
||||
|
||||
% Clinical Trials Background Outline
|
||||
% - ClinicalTrials.gov
|
||||
% - Clincial trial progression
|
||||
% -
|
||||
% -
|
||||
% -
|
||||
% -
|
||||
% -
|
||||
% -
|
||||
% -
|
||||
|
||||
To understand how my administrative clinical trial data is obtained
|
||||
and what it can be used for,
|
||||
let's take a look at how trial investigators record data on
|
||||
\url{ClinicalTrials.gov} operate.
|
||||
Figure \ref{Fig:Stages} illuistrates the process I describe below.
|
||||
During the Pre-Trial period the trial investigators will design the trial,
|
||||
choose primary and secondary objectives,
|
||||
and decide on how many participants they need to enroll.
|
||||
Once they have decided on these details, they post the trial to \url{ClinicalTrials.com}
|
||||
and decide on a date to begin enrolling trial participants.
|
||||
If the investigators decide to not continue with the trial before enrolling any participants,
|
||||
the trial is marked as ``Withdrawn''.
|
||||
On the other hand, if they begin enrolling participants, there are two methods to do so.
|
||||
The first is to enter a general ``Recruiting'' state, where patients attempt to enroll.
|
||||
The second is to enter an "Enrollment by invitation only" state.
|
||||
After a trial has enrolled their participants, they wil typically move to an
|
||||
"Active, not recruiting" state to inform potential participants that they are
|
||||
not recruiting.
|
||||
Finally, when the investigators have obtained enough data to achieve their primary
|
||||
objective, the clinical trial will be closed, and marked as ``Completed'' in
|
||||
\url{ClinicalTrials.gov}
|
||||
If the trial is closed before achieving the primary objective, the trial is
|
||||
marked as ``Terminated'' on
|
||||
\url{ClinicalTrials.gov}.
|
||||
|
||||
|
||||
\begin{figure}%[H] %use [H] to fix the figure here.
|
||||
\includegraphics[width=\textwidth]{../assets/img/ClinicalTrialStagesAndStatuses}
|
||||
\par \small
|
||||
Diamonds represent decision points while
|
||||
Squares represent states of the clinical trial and Rhombuses represend data obtained by the trial.
|
||||
\caption[Clinical Trial Stages and Progression]{Clinical Trial Stages and Progression}
|
||||
\label{Fig:Stages}
|
||||
\end{figure}
|
||||
|
||||
Note the information we obtain about the trial from the final status:
|
||||
``Withdrawn'', ``Terminated'', or ``Completed''.
|
||||
Although \cite{khm} describes a clinical failure due to safety or efficacy as a
|
||||
\textit{scientific} failure, it is better described as a compound failure.
|
||||
Discovering that a compound doesn't work as hoped is not a failure but the whole
|
||||
purpose of the clinical trials process.
|
||||
On the other hand, when a trial terminates early due to reasons
|
||||
other than safety or efficacy concerns, the trial operator does not learn
|
||||
if the drug is effective or safe.
|
||||
This is a true failure in that we did not learn if the drug was effective or not.
|
||||
Unfortunately, although termination documentation typically includes a
|
||||
description of a reason for the clinical trial termination, this doesn't necessarily
|
||||
list all the reasons contributing to the trial termination and may not exist for a given trial.
|
||||
|
||||
As a trial goes through the different stages of recruitment, the investigators
|
||||
update the records on ClinicalTrials.gov.
|
||||
Even though there are only a few times that investigators are required
|
||||
to update this information, it tends to be updated somewhat regularly as it is
|
||||
a way to communicate with potential enrollees.
|
||||
When a trial is first posted, it tends to include information
|
||||
such as planned enrollment,
|
||||
planned end dates,
|
||||
the sites at which it is being conducted,
|
||||
the diseases that it is investigating,
|
||||
the drugs or other treatments that will be used,
|
||||
the experimental arms that will be used,
|
||||
and who is sponsoring the trial.
|
||||
As enrollment is opened and closed and sites are added or removed,
|
||||
investigators will update the status and information
|
||||
to help doctors and potential participants understand whether they should apply.
|
||||
|
||||
|
||||
|
||||
% -
|
||||
% -
|
||||
% -
|
||||
% -
|
||||
% -
|
||||
% -
|
||||
|
||||
|
||||
\end{document}
|
||||
@ -0,0 +1,54 @@
|
||||
--get a list of the most recent activations that exist for a given application.
|
||||
create temp table nsde_activations as
|
||||
select
|
||||
application_number_or_citation,
|
||||
count(distinct package_ndc) as package_count,
|
||||
max(marketing_start_date) as most_recent_start,
|
||||
max(marketing_end_date) as most_recent_end,
|
||||
max(inactivation_date) as most_recent_inactivation,
|
||||
max(reactivation_date) as most_recent_reactivation
|
||||
from spl.nsde
|
||||
group by application_number_or_citation
|
||||
;
|
||||
|
||||
select count(*) from nsde_activations
|
||||
where most_recent_end is null
|
||||
;
|
||||
/*
|
||||
count
|
||||
-----
|
||||
6602
|
||||
*/
|
||||
|
||||
|
||||
select count(*) from nsde_activations
|
||||
where most_recent_end is NOT null
|
||||
;
|
||||
/*
|
||||
count
|
||||
-----
|
||||
10983
|
||||
*/
|
||||
|
||||
/*
|
||||
So, the current number of marketed compounds is how many NDA or ANDA (ANADA?) compounds there are.
|
||||
|
||||
*/
|
||||
|
||||
-- get count of drugs that you can select by first 3 letters
|
||||
select
|
||||
left(application_number_or_citation, 3) as first_3,
|
||||
count(*) as row_count
|
||||
from nsde_activations
|
||||
group by first_3
|
||||
;
|
||||
|
||||
|
||||
|
||||
select
|
||||
left(application_number_or_citation, 3) as first_3,
|
||||
count(*) as row_count
|
||||
from nsde_activations
|
||||
where first_3 in ()
|
||||
group by first_3
|
||||
;
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 179 KiB |
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Reference in New Issue