updated lit review and other minor changes

Latex/Paper/Main.tex

@@ -45,10 +45,18 @@
 
 \subfile{sections/01_introduction}
 %---------------------------------------------------------------
-\section{Literature Review}\label{SEC:LiteratureReview}
+%\section{Literature Review}\label{SEC:LiteratureReview}
 %---------------------------------------------------------------
 \subfile{sections/05_LitReview}
 
+The paper proceeds as follows. 
+Then section \ref{SEC:data} covers the data sources and the proposed
+data generating process as well as the causal identification.
+Section \ref{SEC:EconometricModel} describes the econometric model
+used.
+Section \ref{SEC:Results} discusses the results of the analysis.
+\todo{Review this after writing a few mor sections.}
+
 %---------------------------------------------------------------
 \section{Causal Story and Data}\label{SEC:Data}
 %---------------------------------------------------------------

Latex/Paper/sections/01_introduction.tex

@@ -3,34 +3,63 @@
 
 \begin{document}
 
-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.
+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 succeede 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.
+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 concerns 
+already present, including competitors already in the market or close to 
+entering and the overall demand to address a given condition.
 
 %begin discussing failures
 %I am thinking I'll discuss marketing and operational failures
 %I somehow need to step away from the drug development framing and soften it to ... what? drug investigation?
+From these general challenges we can begin to classify failures in drug 
+development into a hierarchy of causes.
+\cite{khmelnitskaya_competitionattritiondrug_2021}
+described two general causes for a drug to exit the drug-development pipline,
+strategic exits and scientific failure.
+\cite{hwang_failure_2016} 
+described failues of Phase III trials in a similar way, 
+ascribing drug development failures to issues with safety, 
+efficacy, or other (buisness) concerns.
 
-While discovering that a drug doesn't work or is unsafe is a scientific failure, other failure modes occur.
-\cite{khmelnitskaya_competitionattritiondrug_2021} explored how to identify business related failures within the drug development pipeline. 
+% The only one most ameniable to being targeted by policy 
+% is those ``other concerns''.
+Although decisions to continue drug development are driven 
+by long term profit analyses,
+pharmaceutical companies face short term operational challenges.
+% As an example, while a drug may have few competitors and 
+% strong evidence of safety, difficulties recruiting trial participants may 
+% prevent the clinical trials process from being completed successfully.
+For example, even with few competitors and strong safety evidence, recruitment difficulties can still derail a drug's clinical trial process.
+\todo{Clean up that hypothetical, it doesn't seem clean}
+Thus being able to isolate the effect of operational challenges from 
+strategic decisions allows us to predict the intended or unintended effects
+of a given policy on clinical trials.
 
-Thoughts so far
-types of failure: 
-- scientific: unsafe or ineffective
-- business: concerns about profitability
-- operational: cannot actually complete the steps required to bring things to market.
+In this work, I propose a model of clinical trial progression that allows 
+me to separate the effects of competing drugs (a strategic concern)
+and struggles recruiting (an operational concern).
+I also use a novel dataset extracted from 
+\url{ClinicalTrials.gov} 
+that tracks individual clinical trials as they progress towards completion
+to estimate the effects of competing drugs and difficulty recruiting.
+Similar to 
+\cite{hwang_failure_2016} 
+I focus on clinical trials in Phase III trials for drug compounds. 
+Not all of these trials will be to test novel compounds, as many 
+are trials to use previously approved compounds for new indications
+or in combination with other treatments.
 
-Things that influence each
-- scientific: phamokynetics, biology. Take as given.
-- business: regulation, competitors, demand levels, patents, etc
-- operational: regulation, finding participants, finding competent PIs etc.
-
-Maybe financial and operational terminology?
 
 \end{document}

Latex/Paper/sections/05_LitReview.tex

@@ -3,71 +3,81 @@
 
 \begin{document}
 
-This paper sits within an intersection of health and industrial organization economics
-that is frequently studied.
-Encouraging a strong supply of novel and generic pharmaceuticals contributes 
-in important ways to both public health and fiscal policy.
-Not only to the pathway to drug approval long, as many as 90\% of compounds
-that begin human trials fail to gain approval 
-(\cite{khmelnitskaya_competition_2021}).
-Complicating this is the complex regulatory and competitive environment in
-which pharmaceutical companies operate. 
-
-%%%%%%%%% Why are drugs so expensive?
-
-% van der Grond, Uyle-de Groot, Pieters 2017
-% - What causes high costs of drugs? 
-% - High level synthesis of discussion regarding causes
-% - Academic and non-academic sources
-
-
 %%%%%%%%%%%%%%%% What do we know about clinical trials?
+\subsection{What do we know about clinical trials and their success rates?}
+
+Most studies of clinical trials attempt to model only those trials 
+which are involved in the drug approval process. 
 
 % Hwang, Carpenter, Lauffenburger, et al (2016)
 % - Why do investigational new drugs fail during late stage trials?
-\citeauthor{hwang_failure_2016} (\citeyear{hwang_failure_2016}) 
+\cite{hwang_failure_2016} 
 investigated causes for which late stage (Phase III)
 clinical trials fail across the USA, Europe, Japan, Canada, and Australia. 
 They found that for late stage trials that did not go on to recieve approval,
 57\% failed on efficacy grounds, 17\% failed on safety grounds, and 22\% failed
 on commercial or other grounds.
-For context, this current work hopes to be able to distinguish some of the 
-mechanisms behind those commercial or other failures.
 
 % Abrantes-Metz, Adams, Metz (2004)
 % - What correlates with successfully passing clinical trials and FDA review?
 % - 
-In \citeyear{abrantes-metz_pharmaceutical_2004}, 
-\citeauthor{abrantes-metz_pharmaceutical_2004} 
+\cite{abrantes-metz_pharmaceutical_2004} 
 described the relationship between
 various drug characteristics and how the drug progressed through clinical trials.
-This non-causal estimate was notable for using a 
-mixed state proportional hazard model and estimating the impact of 
+This descriptive estimate used a 
+mixed state proportional hazard model and estimated the impact of 
 observed characteristics in each of the three phases.
 They found that as trials last longer, the rate of failure increases for
-Phase I \& II trials, while Phase 3 trials generally have a higher rate of 
+Phase I and II trials, while Phase 3 trials generally have a higher rate of 
 success than failure after 91 months.
 
+
+\cite{hay_ClinicalDevelopment_2014} tracks clinical trials based on 
+the number of indications studied. 
+They find that 10.4\% of all novel drug development paths for an indication, 
+studied in a phase I trial, are ultimately approved by the FDA.
+
+\cite{wong_EstimationClinical_2019} 
+constructed a model where they estimated each, which they used to estimate the 
+probability of completing a given phase, conditional on starting a previous phase.
+In doing so, they found that 13.8\% of all drug development programs 
+completed successfully, which is higher than the approximately 10\% rate
+others have found\cite{hay_ClinicalDevelopment_2014}.
+One cause of this may be that they considered that a single drug might 
+be used tested for multiple indications.
+% Large dataset. 
+% they found lower estimates than previous work.
+
 % Ekaterina Khmelnitskaya (2021)
 % - separates scientific from market failure of the clinical drug pipeline
-In her doctoral dissertation, Ekaterina Khmelnitskaya studied the transition of
+%In her doctoral dissertation, Ekaterina Khmelnitskaya
+\cite{khmelnitskaya_CompetitionAttrition_2021} approaches a slightly 
+different problem. 
+She created a multistage model to track the transition of 
 drug candidates between clinical trial phases. 
-Her key contribution was to find ways to disentangle strategic exits from the 
-development pipeline and exits due to clinical failures.
-She found that overall 8.4\% of all pipeline exits are due to strategic 
+Her key contribution was to find ways to disentangle strategic exits where 
+firms remove novel from the development pipeline and 
+exits due to scientific failures 
+(where safety and efficacy did not prove sufficient).
+She estimates 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 elimintated
-(\cite{khmelnitskaya_competition_2021}).
-
-% Waring, Arrosmith, Leach, et al (2015)
-% - Atrition of drug candidates from four major pharma companies
-% - Looked at how phisicochemical properties affected clinical failure due to safety issues
-%not in this version
-
-
-
+higher if those strategic terminatations were elimintated.
 
 %%%%%%%%% What do we know about drug development incentives?
+\subsection{What do we know about drug development incentives?}
+% Introduce section
+% key points
+% - multiple types of drugs (generic and brand named)
+% - These respond differently
+%   -  Dranov et al 2022 - demand pull seems to bias follow up drug development.
+%      - increasing demand doesn't necessarily result in new compounds (check this). Risks.
+%   -  acemoglu and linn 2004 - population size matters.
+% - Note then that separating effects is difficult at the drug development level.
+%   - Population ties into the number of drugs available, and operational (recruitment) concerns
+%   - In general, there are going to be many confounding variables.
+% - 
+% 
+% 
 
 % Dranov, Garthwaite, and Hermosilla (2022)
 % - does the demand-pull theory of R&D explain novel compound development?
@@ -88,10 +98,22 @@ Among non-generics, a 1\% increase in potential market size
 
 % Gupta
 % - Inperfect intellectual property rights in the pharmaceutical industry
-%\cite{GupaPhd2023} 
+\cite{gupta_OneProduct_2020} 
+\todo{Sumarize how intellectual property rights affect things}
+% - link to difference between novel and generics from acemoglu and linn
 
 % Agarwal and Gaule 2022
 % - Retrospective on impact from COVID-19 pandemic
 % Not in this version
 
+\subsection{What do we know about how Clinical Trials proceed?}
+%interview with Adam George
+% - clinical trials are often handled by contractors
+% - they plan sites, start times, etc from beginning.
+% - Running late is normal.
+
+% Results on enrollment projection
+% - nothing really good exists.
+% - no cross validation, only tested on  a few trials.
+
 \end{document}