began rewriting introduction

2 years ago · 411be99bc9
parent 2fc6f08086
commit 411be99bc9
2 changed files with 107 additions and 90 deletions
--- a/Latex/Paper/sections/01.1_introduction.tex
+++ b/Latex/Paper/sections/01.1_introduction.tex
@ -0,0 +1,98 @@
 \documentclass[../Main.tex]{subfiles}
 \graphicspath{{\subfix{Assets/img/}}}
 \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}
--- a/Latex/Paper/sections/01_introduction.tex
+++ b/Latex/Paper/sections/01_introduction.tex
@ -2,97 +2,16 @@
 \graphicspath{{\subfix{Assets/img/}}}
 \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}.
 Developing new, effective pharmaceutical compounds is a fundamentally difficult task.
 Starting with challenges identifying promising treatment targets and potential compounds, to ensuring the drug can be properly delivered within the body, the scientific work that needs to go well is massive.
 The regulatory and market conditions in which they exist add to this difficulty. 
 For example, regulations are designed to reduce the number of drugs released to market
 with significan issues, such as in the case of VIOXX \cite{krumholz_whathavewe_2007}
 or the Perdue Pharma scandal \cite{officepublicaffairsjusticedepartment_2020}.
 These regulations, such as clinical trial standards \todo{add citation to clinical trials here}, 
 increase the costs of developing new drugs, adding to the business conserns already present, including 
 competitors already in the market or close to entering and the overall demand to address a given condition.
 \end{document}