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In 1938, President Franklin D. 
Roosevelt signed the Food, Drug, and Cosmetic Act, establishing the Food and
Drug Administration's (FDA) authority to require pre-market approval of
pharmaceuticals [Com14]. 
This created a regulatory framework where pharmaceutical companies must
demonstrate safety and efficacy through clinical trials before bringing drugs
to market. 
The costs of these trials - both in time and money - form a significant barrier
to entry in pharmaceutical markets. 
Understanding what causes clinical trials to fail is therefore crucial to 
predict the impact of policies, intended or unintended.

Existing research has examined how drugs progress through development
pipelines, but we know relatively little about the relative contribution of different
challenges to the early termination of clinical trials. 
%HWANG et al do discuss a few different reasons
When a trial terminates early due to operational challenges rather than safety
or efficacy concerns, potentially effective treatments may be delayed or
abandoned entirely.
%Example of GLP-1s

This paper provides the first empirical framework to separate 
market-driven and safety/efficacy based terminations from 
one form of operational failure 
-- enrollment challenges -- 
in Phase III clinical trials. 
Using a novel dataset constructed from administrative data registered on 
ClinicalTrials.gov, I exploit variation in enrollment timing and market
conditions to identify how extending the enrollment period 
affects trial completion. 
Specifically, I answer the question:
\textit{
    ``How does the probability of trial termination change 
    when the enrollment period is extended?''
}
This approach differs from previous work that focuses for the most part 
on the drug development
pipeline and progression between clinical trial phases.




To understand how I do this, we'll cover some background information on 
clinical trials, the current literature, 
and the administrative data I collected in section 
\ref{SEC:ClinicalTrials}.
Then I'll
explain the approach to causal identification and how the data collected
matches those results,
\ref{SEC:CausalAndData}. 
Then we'll cover the econometric model 
(section \ref{SEC:EconometricModel}) 
and results (section \ref{SEC:Results}). 
Finally, we acknowledge deficiencies in the analysis and potential improvements
in section 
\ref{SEC:Improvements},
then end with my thoughts in the conclusion \ref{SEC:Conclusion}

% \subsection{Market incentives and drug development}
% %%%%%%%%% What do we know about drug development incentives?
%
% \cite{dranove_DoesConsumer_2022} use the implementation of Medicare part D 
% to examine whether the production of novel or follow up drugs increases during 
% the following 15 years. 
% They find that when Medicare part D was implemented -- increasing senior 
% citizens' ability to pay for drugs -- there was a (delayed) increase 
% in drug development, with effects concentrated among compounds that were least
% innovative according to their classification of innovations.
% They suggest that this is due to financial risk management, as novel 
% pharmaceuticals have a higher probability of failure compared to the less novel
% follow up development. 
% This is what leads risk-adverse companies to prefer follow up development.
%
%
% % Acemoglu and Linn
% % - Market size in innovation
% % - Exogenous demographic trends has a large impact on the entry of non-generic drugs and new molecular entitites.
% On the side of market analysis, 
% \citeauthor{acemoglu_market_2004} 
% (\citeyear{acemoglu_market_2004})
% used exogenous deomographics changes to show that the
% entry of novel compounds is highly driven by the underlying aged population.
% They estimate that a 1\% increase in applicable demographics increase the
% entry of new drugs by 6\%, mostly concentrated among generics.
% Among non-generics, a 1\% increase in potential market size 
% (as measured by demographic groups) leads to a 4\% increase in novel therapies.
%
% % Gupta
% % - Inperfect intellectual property rights in the pharmaceutical industry
% \cite{gupta_OneProduct_2020} discovered that uncertainty around which patents
% might apply to a novel drug causes a delay in the entry of generics after 
% the primary patent has expired. 
% She found that this delay in delivery is around 3 years. 
%
% % Agarwal and Gaule 2022
% % - Retrospective on impact from COVID-19 pandemic
% % Not in this version
%
% \subsection{Understanding Failures in Drug Development}
%
% % DISCUSS: Different types of failures
% There are myriad of reasons that a drug candidate may not make it to market, 
% regardless of it's novelty or known safety.
% In this work, I focus on the failure of individual clinical trials, but the 
% categories of failure apply to the individual trials as well as the entire
% drug development pipeline.
% They generally fall into one of the following categories:
% \begin{itemize}
%     \item Scientific  Failure: When there are issues regarding 
%         safety and efficacy that must be addressed. 
%         The preeminient question is: 
%         ``Will the drug work for patients?''
%         %E.Khm, Gupta, etc.
%     \item Strategic Failure: When the sponsors stop development because of 
%         profitability
%         %Whether or not the drug will be profitiable, or align with
%         %the drug developer's future Research \& Development directions i.e.
%         ``Will producing the drug be beneficial to the 
%         company in the long term?''
%         %E.Khm, Gupta, GLP-1s, etc.
%     \item Operational concerns are answers to: 
%         %Whether or not the developer can successfully conduct
%         %operations to meet scientific or strategic goals, i.e. 
%         ``What has prevented the the company from being able to 
%         finance, develop, produce, and market the drug?''
% \end{itemize}
% It is likely that a drug fails to complete the development cycle due to some
% combination of these factors. 
%
%
% %USE MetaBio/CalBio GLP-1 story to illuistrate these different factors.
% \cite{flier_DrugDevelopment_2024} documents the case of MetaBio, a company
% he was involved in founding that was in the first stages of
% developing a GLP-1 based drug for diabetes or obesety before being shut down
% in . 
% MetaBio was a wholy owned subsidiary of CalBio, a metabolic drug development 
% firm, that recieved a \$30 million -- 5 year investment from Pfizer to 
% persue development of GLP-1 based therapies. 
% At the time it was shut down, it faced a few challenges:
% \begin{itemize}
%     \item The compound had a short half life and they were seeking methods to 
%         improve it's effectiveness; a scientific failure.
%     \item Pfizer imposed a requirement that it be delivered though a route
%         other than injection (the known delivery mechanism); a strategic failure. 
%     \item When Pfizer pulled the plug, CalBio closed MetaBio because they 
%         could not find other funding sources; an operational failure.
% \end{itemize}
%
% The author states in his conclusion:
% \begin{displayquote}
%     Despite every possibility of success, 
%     MetaBio went down because there were mistaken ideas about what was 
%     possible and what was not in the realm of metabolic therapeutics, and 
%     because proper corporate structure and adequate capital are always 
%     issues when attempting to survive predictable setbacks.
% \end{displayquote} 
%
% From this we see that there was a cascade of issues leading to the failure to 
% develop this novel drug. 
%
%
% % I don't think I need to include modelling enrollment here. 
% % If it is applicable, it can show up in those sections later.
%
%

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