\documentclass[../Main.tex]{subfiles}
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\begin{document}
% hook - what makes drugs expensive? Mention high failure rate
% describe current research 
% - Examine mechanisms by which clinical trials fail.
% - Mention data
% - Results
How to best address the high cost of pharmaceuticals is a crucial health 
and fiscal policy question that has been debated for 
decades.
Due to the complicated legal and competitive landscape, unintended consequences
are common 
\cite{vandergronde_addressingchallengehighpriced_2017}.
One essential step to introduce a novel pharmaceutical - or even
to begin selling a generic compound - is to establish that the drug as packaged and sold will
have acceptable safety and efficacy profiles.
When evaluating these compounds in a clinical trial, multiple outcomes are possible:
\begin{enumerate}
    \item The compound demonstrates sufficient safety and efficacy, and proceeds in the appoval process. 
        \label{Item:EndSuccess}
    \item The compound fails to demonstrate sufficient safety and efficacy, and the approval process halts. 
        \label{Item:EndFail}
    \item The trial is terminated before it can acheive one of the first two 
        outcomes, for reasons unrelated to safety and efficacy concerns. 
        \label{Item:Terminate}
\end{enumerate}


\begin{table}
    \caption{Potential States of Knowledge from a clinical trial}\label{tab:StatesOfKnowledge}
    \begin{center}
        \begin{tabular}{p{0.15\textwidth} p{0.2\textwidth}||p{0.25\textwidth}|p{0.25\textwidth}|}
        \cline{3-4}
            \multicolumn{2}{c|}{Drug-Indication Match}  & safe and efficacious & not safe or not efficatious \\
        \hline
        \hline
            \multirow{2}{0.15\textwidth}{Operations} & Success & Known good & Known bad \\
        \cline{2-4}
            & Failure & \multicolumn{2}{c|}{Unkown} \\
        \cline{2-4}
        \end{tabular}
    \end{center}
\end{table}


\begin{table}
    \caption{Clinical Trial end states}\label{tab:ClinicalTrialEndStates}
    \begin{center}
        \begin{tabular}{p{0.15\textwidth} p{0.2\textwidth}||p{0.25\textwidth}|p{0.25\textwidth}|}
        \cline{3-4}
            \multicolumn{2}{c|}{Drug-Indication Match}  & safe and efficacious & not safe or not efficatious \\
        \hline
        \hline
            \multirow{2}{0.15\textwidth}{Operations} & Success & Completion & Completion or Termination \\
        \cline{2-4}
            & Failure & \multicolumn{2}{c|}{Termination} \\
        \cline{2-4}
        \end{tabular}
    \end{center}
\end{table}

While it is known that pharmaceutical companies withdraw some drugs from
their development pipeline due to commercialization concerns 
(
\cite{khmelnitskaya_competition_2021} 
and 
\cite{van_der_gronde_addressing_2017}
), there are likely unseen
effects that might affect the overall drug pipleline.
One of these is the concern that when there are already approved therapies on 
the market, patients might be loath to enroll in clinical trials,
causing the trial to fail for reasons unrelated to the scientific or
commercial viability of the therapy.


To adequately guide public policy it is crucial that robust, causally-identified
statistical models are available to describe the interaction between
various players within the space.

This work endeavors to estimate the change in probability of successful completion
of a clinical trial due to the existence of alternative drugs on the market. 
In particular, it seeks to establish whether such an impact is mediated
by enrollment patterns or is caused more directly.


The paper proceeds as follows: a brief literature review in \cref{SEC:LiteratureReview}, 
a description of the caual model in \cref{SEC:CausalIdentification},
followed by a description of the data (\cref{SEC:Data}) and the 
econometric model (\cref{SEC:EconometricModel}).
Preliminary results are presented in \cref{SEC:Results} and a discussion
of proposed improvements is included in \cref{SEC:Improvements}.




\end{document}