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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.



% In 1938 President Franklin D Rosevelt signed the Food, Drug, and Cosmetic Act,
% granting the Food and Drug Administration (FDA) authority to require 
% pre-market approval of pharmaceuticals. 
% \cite{commissioner_milestonesusfood_2023}
% As of Sept 2022 \todo{Check Date} they have approved 6,602 currently-marketed 
% compounds with Structured Product Labels (SPLs) 
% and 10,983 previously-marketed SPLs
% \cite{commissioner_nsde_2024},
% %from nsde table. Get number of unique application_nubmers_or_citations with most recent end date as null.
% In 1999, they began requiring that drug developers register and 
% publish clinical trials on \url{https://clinicaltrials.gov}.
% This provides a public mechanism where clinical trial sponsors are 
% responsible to explain what they are trying to acheive and how it will be 
% measured, as well as provide the public the ability to search and find trials 
% that they might enroll in.
% Multiple derived datasets such as the Cortellis Investigational Drugs dataset 
% or the AACT dataset from the Clinical Trials Transformation Intiative
% integrate these data. 
% This brings up a question: 
% Can we use this public data on clinical trials to identify what effects the 
% success or failure of trials?
% In this work, I use updates to records on 
% \url{https://ClinicalTrials.gov} 
% to do exactly that, disentangle the effect of participant enrollment 
% and competing drugs on the market affect the success or failure of 
% clinical trials.

\subsection{Background}

%Describe how clinical trials fit into the drug development landscape and how they proceed
Clinical trials are a required part of drug development.
Not only does the FDA require that a series of clinical trials demonstrate sufficient safety and efficacy of
a novel pharmaceutical compound or device, producers of derivative medicines may be required to ensure that
their generic small molecule compound -- such as ibuprofen or levothyroxine -- matches the
performance of the originator drug if delivery or dosage is changed.
For large molecule generics (termed biosimilars) such as Adalimumab
(Brand name Humira, with biosimilars Abrilada, Amjevita, Cyltezo, Hadlima, Hulio,
Hyrimoz, Idacio, Simlandi, Yuflyma, and Yusimry),
the biosimilars are required to prove they have similar efficacy and safety to the
reference drug.

%TODO? Decide whether to include this or not
%When registering these clinical trials
% discuss how these are registered and what data is published.
% Include image and discuss stages
% Discuss challenges faced

% Introduce my work

In the world of drug development, these trials are classified into different 
phases of development\footnote{
\cite{anderson_fdadrugapproval_2022}
provide an overview of this process
while
\cite{commissioner_drugdevelopmentprocess_2020}
describes the process in detail.}.
Pre-clinical studies primarily establish toxicity and potential dosing levels.
% \cite{commissioner_drugdevelopmentprocess_2020}.
Phase I trials are the first attempt to evaluate safety and efficacy in humans. 
Participants typically are healthy individuals, and they measure how the drug 
affects healthy bodies, potential side effects, and adjust dosing levels. 
Sample sizes are often less than 100 participants. 
% \cite{commissioner_drugdevelopmentprocess_2020}.
Phase II trials typically involve a few hundred participants and is where 
investigators will dial in dosing, research methods, and safety.
% \cite{commissioner_drugdevelopmentprocess_2020}.
A Phase III trial is the final trial before approval by the FDA, and is where 
the investigator must demonstrate safety and efficacy with a large number of 
participants, usually on the order of hundreds or thousands.
% \cite{commissioner_drugdevelopmentprocess_2020}.
Occasionally, a trial will be a multi-phase trial, covering aspects of either
Phases I and II or Phases II and III. 
After a successful Phase III trial, the sponsor will decide whether or not 
to submit an application for approval from the FDA. 
Before filing this application, the developer must have completed 
``two large, controlled clinical trials.''
% \cite{commissioner_drugdevelopmentprocess_2020}.
Phase IV trials are used after the drug has received marketing approval to 
validate safety and efficacy in the general populace.
Throughout this whole process, the FDA is available to assist in decision-making
regarding topics such as study design, document review, and whether
they should terminate the trial. 
The FDA also reserves the right to place a hold on the clinical trial for 
safety or other operational concerns, although this is rare. 
\cite{commissioner_drugdevelopmentprocess_2020}.


In the economics literature, most of the focus has been on describing how 
drug candidates transition between different phases and their probability 
of final approval.
% Lead into lit review
% Abrantes-Metz, Adams, Metz (2004)
\authorcite{abrantes-metz_pharmaceuticaldevelopmentphases_2004}
described the relationship between
various drug characteristics and how the drug progressed through clinical trials.
% This descriptive estimate was notable for using a 
% mixed state proportional hazard model and estimating the impact of 
% observed characteristics in each of the three phases.
They found that as Phase I and II trials last longer, 
the rate of failure increases. 
In contrast, Phase 3 trials generally have a higher rate of 
success than failure after 91 months.
This may be due to the fact that the purpose of Phases I and II are different
from the purpose of Phase III.

Continuing on this theme,
%DiMasi FeldmanSeckler Wilson 2009
\authorcite{dimasi_trendsrisksassociated_2010}
examine the completion rate of clinical drug 
development and find that for the 50 largest drug producers, 
approximately 19\% of their drugs under development between 1993 and 2004
successfully moved from Phase I to receiving an New Drug Application (NDA) 
or Biologics License Application (BLA). 
They note a couple of changes in how drugs are developed over the years they 
study, most notably that
drugs began to fail earlier in their development cycle in the 
latter half of the time they studied. 
They note that this may reduce the cost of new drugs by eliminating late 
and costly failures in the development pipeline.

Earlier work by 
\authorcite{dimasi_valueimprovingproductivity_2002}
used data on 68 investigational drugs from 10 firms to simulate how reducing
time in development reduces the costs of developing drugs. 
He estimates that reducing Phase III of clinical trials by one year would 
reduce total costs by about 8.9\% and that moving 5\% of clinical trial failures
from phase III to Phase II would reduce out of pocket costs by 5.6\%. 

A key contribution to this drug development literature is the work by 
\authorcite{khmelnitskaya_competitionattritiondrug_2021}
who created a causal identification strategy
to disentangle strategic exits from exits due to clinical failures 
in the drug development pipeline.
She found that overall 8.4\% of all pipeline exits are due to strategic 
terminations and that the rate of new drug production would be about 23\% 
higher if those strategic terminatations were eliminated.

The work that is closest to mine is the work by 
\authorcite{hwang_failureinvestigationaldrugs_2016}
who investigated causes for which late stage (Phase III)
clinical trials fail -- with a focus on trials in the USA, 
Europe, Japan, Canada, and Australia. 
They identified 640 novel therapies and then studied each therapy's 
development history, as outlined in commercial datasets.
They found that for late stage trials that did not go on to receive approval,
57\% failed on efficacy grounds, 17\% failed on safety grounds, and 22\% failed
on commercial or other grounds.

Unfortunately the work of both 
\authorcite{hwang_failureinvestigationaldrugs_2016}
and
\authorcite{khmelnitskaya_competitionattritiondrug_2021}
ignore a potentially large cause of failures: operational challenges, i.e. when
issues running or funding the trial cause it to fail before achieving its 
primary objective.
In a personal review of 199 randomly selected clinical trials which terminated
before achieving their primary objective,
I found that 
14.5\% cited safety or efficacy concerns, 
9.1\% cited funding problems (an operational concern),
and 
31\% cited enrollment issues (a separate operational concern)\footnote{
Note that these figures differ from 
\authorcite{hwang_failureinvestigationaldrugs_2016}
because I sampled from all stages of trials, not just Phase III trials
focused on drug development.
}.




The main contribution of this work is the model I develop to separate 
the causal effects of 
market conditions (a strategic concern) from the effects of 
participant enrollment (an operational concern) on Phase III Clinical trials. 
This allows me to answer the question posed earlier:
\textit{
    ``How does the probability of trial termination change 
    when the enrollment period is extended?''
}
using administrative data.


To understand how I do this, we'll cover some background information on 
clinical trials and the administrative data I collected in section 
\ref{SEC:ClinicalTrials}, 
explain the approach to causal identification, the required data,
and describe how the data used matches these requirements in section 
\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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