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98 lines
4.2 KiB
TeX
98 lines
4.2 KiB
TeX
\documentclass[../Main.tex]{subfiles}
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\graphicspath{{\subfix{Assets/img/}}}
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\begin{document}
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As noted above, there are various issues with the analysis as completed so far.
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Below I discuss various issues and ways to address them that I believe will improve the analysis.
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\subsection{Increasing number of observations}
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The most important step is to increase the number of observations available.
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Currently this requires matching trials to ICD-10 codes by hand.
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Improvements in Large-Language-Models may make this data more accessible, or
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the data may be available in a commercial dataset.
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\subsection{Enrollment Modelling}
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One of the original goals of this project was to examine the impact that
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enrollment struggles have on the probability of trial termination.
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Unfortunately, this requires a model of clinical trial enrollment, and this
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data is missing from my dataset.
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In most cases the trial sponsor reports the anticipated enrollment value
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while the trial is still recruiting and only updates the actual enrollment
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after the trial has ended.
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Some trials do publish an incremental record of their enrollment numbers,
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but this is rare.
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Due to the bayesian model used, it would be possible to
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include a model of the missing data
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\cite{mcelreath_statisticalrethinkingbayesian_2020}.
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which would
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allow me to estimate the direct effect of slow enrollment
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on clinical trial termination rates.
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There has been substantial work on forecasting
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multi-site enrollment rates and durations by
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\cite{
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tozzi_predictingaccrualrate_1996,
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carter_applicationstochasticprocesses_2004,
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anisimov_modellingpredictionadaptive_2007,
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zhang_stochasticmodelingprediction_2010,
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zhang_jointmonitoringprediction_2012,
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zhang_modelingpredictionsubject_2012,
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heitjan_realtimepredictionclinical_2015,
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jiang_modelingvalidatingbayesian_2015,
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deng_bayesianmodelingprediction_2017,
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lan_statisticalmodelingprediction_2019,
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zhang_simplerobustmodel_2022,
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urbas_interimrecruitmentprediction_2022,
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bieganek_predictionclinicaltrial_2022,
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avalos-pacheco_validationpredictiveanalyses_2023,
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}
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but choosing between the various single and multi-site models presented is
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difficult without a dataset to validate the results on.
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\subsection{Improving Population Estimates}
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The Global Burden of Disease dataset contains the best estimates of disease
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population sizes that I have found so far.
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Unfortunately, for some conditions it can be relatively imprecise due to
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its focus on providing data geared towards public health policy.
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For example, GBD contains categories for both
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drug resistant and drug suceptible tuberculosis, but maps those to the same
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ICD-10 code.
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In contrast, there is no category for non-age related macular degeneration.
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Thus not every trial has a good match with the estimate of the population of
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interest.
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Finding a way to focus on trials that have good disease population estimates
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would improve the efficiency of the analysis.
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\subsection{Improving Measures of Market Conditions}
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% Deficiency: cannot measure effect of market conditions because of endogenetiy of population and market conditions (fatal diseases)
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In addition to the fact that many diseases may be treated by non-pharmaceutical
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means (e.g. diet, physical therapy, medical devices, etc),
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off-label prescription of pharmaceuticals is legal at the federal level
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\cite{commissioner_understandingunapproveduse_2019}.
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These two facts both complicate measuring competing treatments,
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a key part of market conditions.
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One way to address non-pharmaceutical treatments is to concentrate on domains
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that are primarily treated by pharmaceuticals.
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Another way to address this would be to focus the analysis on just a few specific
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diseases, for which a history of treatment options can be compiled.
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This second approach may also allow the researcher to distinguish the direction
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of causality between population size and number of drugs on the market;
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for example, drugs to treat a chronic, non-fatal disease will probably not
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affect the market size much in the short to medium term.
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This allows the effect of market conditions to be isolated from
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the effects of the population.
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% Alternative approaches
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% - diseases with constant kill rates? population effect should be relatively constant?
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\end{document}
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