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74 lines
3.6 KiB
TeX
74 lines
3.6 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 the
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data is just not in 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 up to date record of their enrollment numbers, but this
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is rare.
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If a bayesian model of multisite enrollment can be developed for the disease categories
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in question, then it will be possible to impute this missing data probabalistically,
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which will allow me to estimate the direct effect of slow enrollment
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\cite{mcelreath_statistical_2020}.
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This does not exist yet, although some work on multi-site enrollment forecasting has
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been done by \cite{CHECK ZOTERO NOTES FOR CITATIONS}
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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.
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In contrast, there is no category for non-age related macular degeneration.
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One resulting concern is that for a given ICD-10 code, the applicable GBD population
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estimates may act as an estimate of the upper bound of population size
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(\cite{global_burden_of_disease_collective_network_global_2020}).
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The dataset contains various measures of disease severity, so it may be
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worth investigating how to incorporate some of those measures.
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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, off-label prescription of pharmaceuticals is legal at the federal level
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(\cite{commissioner_understanding_2019}).
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These two facts both complicate measuring 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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