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As noted above, there are various issues with the analysis as completed so far.
Below I discuss various issues and ways to address them that I believe will improve the analysis.

\subsection{Increasing number of observations}

The most important step is to increase the number of observations available.
Currently this requires matching trials to ICD-10 codes by hand.
Improvements in Large-Language-Models may make this data more accessible, or
the data may be available in a commercial dataset.


\subsection{Enrollment Modelling}

One of the original goals of this project was to examine the impact that 
enrollment struggles have on the probability of trial termination. 
Unfortunately, this requires a model of clinical trial enrollment, and this
data is missing from my dataset.
In most cases the trial sponsor reports the anticipated enrollment value
while the trial is still recruiting and only updates the actual enrollment
after the trial has ended.
Some trials do publish an incremental record of their enrollment numbers, 
but this is rare. 
Due to the bayesian model used, it would be possible to 
include a model of the missing data 
\cite{mcelreath_statisticalrethinkingbayesian_2020}.
which would
allow me to estimate the direct effect of slow enrollment 
on clinical trial termination rates

There has been substantial work on forecasting
multi-site enrollment rates and durations by
\cite{
    tozzi_predictingaccrualrate_1996,
    carter_applicationstochasticprocesses_2004,
    anisimov_modellingpredictionadaptive_2007,
    zhang_stochasticmodelingprediction_2010,
    zhang_jointmonitoringprediction_2012,
    zhang_modelingpredictionsubject_2012,
    heitjan_realtimepredictionclinical_2015,
    jiang_modelingvalidatingbayesian_2015,
    deng_bayesianmodelingprediction_2017,
    lan_statisticalmodelingprediction_2019,
    zhang_simplerobustmodel_2022,
    urbas_interimrecruitmentprediction_2022,
    bieganek_predictionclinicaltrial_2022,
    avalos-pacheco_validationpredictiveanalyses_2023,
    }
but choosing between the various single and multi-site models presented is 
difficult without a dataset to validate the results on. 



\subsection{Improving Population Estimates}

The Global Burden of Disease dataset contains the best estimates of disease
population sizes that I have found so far. 
Unfortunately, for some conditions it can be relatively imprecise due to 
its focus on providing data geared towards public health policy.
For example, GBD contains categories for both
drug resistant and drug suceptible tuberculosis, but maps those to the same 
ICD-10 code.
In contrast, there is no category for non-age related macular degeneration.
Thus not every trial has a good match with the estimate of the population of
interest.

\subsection{Improving Measures of Market Conditions}

% Deficiency: cannot measure effect of market conditions because of endogenetiy of population and market conditions (fatal diseases)

In addition to the fact that many diseases may be treated by non-pharmaceutical 
means (e.g. diet, physical therapy, medical devices, etc), 
off-label prescription of pharmaceuticals is legal at the federal level 
\cite{commissioner_understandingunapproveduse_2019}.
These two facts both complicate measuring competing treatments, 
a key part of market conditions.
One way to address non-pharmaceutical treatments is to concentrate on domains
that are primarily treated by pharmaceuticals.
Another way to address this would be to focus the analysis on just a few specific
diseases, for which a history of treatment options can be compiled.
This second approach may also allow the researcher to distinguish the direction
of causality between population size and number of drugs on the market; 
for example, drugs to treat a chronic, non-fatal disease will probably not 
affect the market size much in the short to medium term.
This allows the effect of market conditions to be isolated from
the effects of the population.

% Alternative approaches 
% - diseases with constant kill rates? population effect should be relatively constant?

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