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%%%%%%%%%%%%%%%% 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. 
For example, 

% Hwang, Carpenter, Lauffenburger, et al (2016)
% - Why do investigational new drugs fail during late stage trials?
\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.

% Abrantes-Metz, Adams, Metz (2004)
% - What correlates with successfully passing clinical trials and FDA review?
% - 
\cite{abrantes-metz_pharmaceutical_2004} 
described the relationship between
various drug characteristics and how the drug progressed through clinical trials.
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 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, for given indication, only 10.4\% of all novel drug development paths
studied in a phase I trial are ultimately approved by the FDA.

\cite{wong_EstimationClinical_2019} 
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. % slightly higherothers have found\cite{hay_ClinicalDevelopment_2014}.
One cause of this may be that they considered that a single drug might 
be tested for multiple indications.


% Ekaterina Khmelnitskaya (2021)
% - separates scientific from market failure of the clinical drug pipeline
%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 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.

%%%%%%%%% What do we know about drug development incentives?
\subsection{What do we know about drug development incentives?}
% Introduce section
% -  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.
\cite{dranove_DoesConsumer_2022} examined whether increased demand for drugs 
will increase the development of novel drugs. 
Using measures of the scientific novelty of drug compounds after the creation
of Medicare part D, they found that most development occurred in the least
novel categories of drugs, in spite of a relatively constant growth in novel 
compounds. 



%   -  acemoglu and linn 2004 - population size matters.
%   - Population ties into the number of drugs available, and operational (recruitment) concerns
%   - In general, there are going to be many confounding variables.
% - 
% - Exogenous demographic trends has a large impact on the entry of non-generic drugs and new molecular entitites.
On the side of market analysis, %TODO:remove when other sections are written up.
\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.


% Cerda 2007 - Endogenous innovations in the pharmaceutical industry
% from abstract %TODO: Read better
% Market size, population, and existence of drugs are endogenous
% from the abstract I get the impresssion that it is:
% - large population -> large market -> more profitable -> more drugs
% - more drugs -> better survivability -> larger market 
% Applicable because: Need to separate population and market effects.
% Does this mess with my results? I don't think so because of the relatively short time in trials. Not enough time to effect population back, but it might have another effect.
\cite{cerda_EndogenousInnovations_2007} 
suggests a two-way, long term relationship between market size and drug 
development.
They suggest that a large population with a condition implies a (relatively) 
larger market, which improves the profitabilty and thus number of drugs with that
condition. 
Then the drugs improve mortality, increasing the relative population.
They do find evidence of the impact of both population and market size
on the creation of new drugs. 


% van der gronde et al 2017 Addressing the challenge of high-price prescription drugs
% Massive number of policies used to try to reduce costs. These will affect production decisions. 
% Some of the unintended consequences of that (in terms of reduced development incentives) include
% - reducing development costs - side effect of lower quality evidence
% - Preference policy (e.g. policies about using generics first etc) - side effect of shorter life cycle for patented (novel) drugs.
% - these are focused on reducing expenditures, i.e. they reduce profit. Some of them feed back into the development process.

\cite{vandergronde_AddressingChallenge_2017}
documents many of the things driving drug development choices. 
\begin{itemize}
    \item Policies that encourage low cost generics shorten the life cycle of
        patented/novel drugs.
    \item Some diseases have lower safety and efficacy standards applied to them 
        compared to similar diseases. These tend to have higher R\&D due to the 
        lower costs involved.
    \item As much of the "low hanging fruit" in drug development has been developed,
        R\&D expenses have been increasing.
\end{itemize}


% Dubois et al 2015 - Market Size and pharmaceutical innovation
% estimate the relationship between marekt size and the innovation in pharmaceuticals
% elasticity of innovation w.r.t. expected market size of 0.23, thus $2.5 billion in 
% market size required to get a new chemical entity.
\cite{dubois_MarketSize_2015}
examined the ``elasticity of innovation'', i.e. the ``additional revenue required
to support the invention of a new chemical entity.''
They found that a marginal drug will require approximately a \$2.5 billon increase
in expected revenue.

% Gupta
% - Inperfect intellectual property rights in the pharmaceutical industry
\cite{gupta_OneProduct_2020} 
describes the impact that imperfect intellectual property rights have in the 
the market for pharmaceuticals. 
She describes how overlapping and ambiguous patent rights increase the time 
to entry of generic drugs by about 3 years.


\subsection{What do we know about how Clinical Trials operations?}
%interview with Adam George
% - clinical trials are often handled by contractors
% - they plan sites, start times, etc from beginning.
% - Running late is normal.
In a personal interview with someone who works for a company that runs clinical
trials, I learned about how clinical trials will typically proceed. 
\todo{Figure out best way to cite this}
\begin{itemize}
    \item Quote a job (one side of company): N, timeline, etc
    \item Allocate resources (sites, doctors, etc) to try to accomplish
    \item Sales vs Operations conflict, leading to lateness/issues delivering, etc.
\end{itemize}

% Bess Stillman - look at difficulties joining oncology trials

% Random sample of Clinicaltrials.gov - how many closed due to operational problems?
% TODO: random sample 171, about 30% mentioned recruitment issues

% Results on enrollment projection
% - nothing really good exists.
% - Multiple models, no comparison.
% - no cross validation, only tested on  a few trials.

% Thus we should look at the effects that operational concerns have.

\end{document}
