It can also be devided into a few different regimes.
% - spike at 0
% - spike at 0
% - the boxplot
% - the boxplot
% - 63% of mass below 0 : find better way to say that
% - 63% of mass below 0 : find better way to say that
@ -146,15 +117,29 @@ The second regime consists of the moderate impact on clinical trials'
probabilities of termination, say values in the interval $[-0.5, 0.5]$
probabilities of termination, say values in the interval $[-0.5, 0.5]$
on the graph.
on the graph.
Most of this probability mass is represents a decrease in the probability of
Most of this probability mass is represents a decrease in the probability of
a termination, some of it rather large decreases.
a termination, some of it rather large.
The third regime consists of the high impact region,
Finally, there exists the high impact region, almost exclusively concentrated
almost exclusively concentrated above increases in the probability of
around increases in the probability of termination at $\delta_p > 0.75$.
termination $\delta_p > 0.75$.
These represent cases where delaying the close of enrollemnt changes a trial
These represent cases where delaying the close of enrollemnt changes a trial
from a case where they were highly likely to complete their primary objectives to
from a case where they were highly likely to complete their primary objectives to
a case where they were likely or almost certain to terminate the trial early.
a case where they were likely or almost certain to terminate the trial early.
% - the high impact regime is strange because it consists of trials that moved from unlikely (<20% chance) of termination to a high chance (>80% chance) of termination. Something like 5% of all trials have a greater than 98 percentage point increase in termination. Not sure what this is doing.
% - the high impact regime is strange because it consists of trials that moved from unlikely (<20% chance) of termination to a high chance (>80% chance) of termination. Something like 5% of all trials have a greater than 98 percentage point increase in termination. Not sure what this is doing.
Based on the boxplot below, there are a couple of things to note.
First, the median effect is a 2.3 percentage point decrease
in the probability of termination.
Second, for a random selction from our trials,
there is a 63\% chance that the impact is to
reduce the probability of a termination.
Third, about 13\% of the probability mass is contained within the interval
[-0.1,0.1].
Finally, the mean effect is measured as a 9.6 percentage point increase in
% Looking at the spike around zero, we find that 13.09% of the probability mass
% Looking at the spike around zero, we find that 13.09% of the probability mass
% is contained within the band from [-1,1].
% is contained within the band from [-1,1].
% Additionally, there was 33.4282738% of the probability above that
% Additionally, there was 33.4282738% of the probability above that
@ -190,20 +175,7 @@ tend to have a similar results:
Again, note the high mass near zero, the general decrease in the probability
Again, note the high mass near zero, the general decrease in the probability
of termination, and then the strong upper tails.
of termination, and then the strong upper tails.
Continuing to the $\beta$ parameters in figure
Continuing to the $\beta$ parameters,
\ref{fig:parameters_ANR_by_group},
we can see the estimated distributions
the status: \textbf{Active, not recruiting}.
The prior distributions were centered on zero, but we can see that the
pooled learning has moved the mean
values negative, representing reductions in the probability of termination
across the board.
This decrease in the probability of termination is strongest in the categories of Neoplasms ($n=49$),
Musculoskeletal diseases ($n=17$), and Infections and Parasites ($n=20$), the three categories with the most data.
As this is a comparison against the trial status XXX, we note that YYY.
\todo{The natural comparison I want to make is against the Recruting status. Do I want to redo this so that I can read that directly?It shouldn't affect the $\delta_p$ analysis, but this could probably use it. YES, THIS UPDATE NEEDS TO HAPPEN. The base needs to be ``active not recruiting.''}
Overall, this is consistent with the result that extending a clinical trial's enrollment period will reduce the probability of termination.
\caption{Distribution of parameters associated with ``Active, not recruiting'' status, by ICD-10 Category}
\caption{Distribution of parameters associated with ``Active, not recruiting'' status, by ICD-10 Category}
@ -211,6 +183,15 @@ Overall, this is consistent with the result that extending a clinical trial's en
\end{figure}
\end{figure}
% -
% -
Finally, in figure \ref{fig:parameters_ANR_by_group}, we can see the estimated distributions of the $\beta$ parameter for
the status: \textbf{Active, not recruiting}.
The prior distributions were centered on zero, but we can see that the pooled learning has moved the mean
values negative, representing reductions in the probability of termination across the board.
This decrease in the probability of termination is strongest in the categories of Neoplasms ($n=49$),
Musculoskeletal diseases ($n=17$), and Infections and Parasites ($n=20$), the three categories with the most data.
As this is a comparison against the trial status XXX, we note that
\todo{The natural comparison I want to make is against the Recruting status. Do I want to redo this so that I can read that directly?It shouldn't affect the $\delta_p$ analysis, but this could probably use it. YES, THIS UPDATE NEEDS TO HAPPEN. The base needs to be ``active not recruiting.''}
Overall, this suggests that extending a clinical trial's enrollment period will reduce the probability of termination.
% - Potential Explanations for high impact regime:
% - Potential Explanations for high impact regime:
This leads to the question:
This leads to the question:
@ -220,15 +201,12 @@ The most likely explanations in my mind are that either
some trials are highly suceptable to enrollment struggles or that this is a
some trials are highly suceptable to enrollment struggles or that this is a
modelling artifact.
modelling artifact.
% - Some trials are highly suceptable. This is the face value effect
% - Some trials are highly suceptable. This is the face value effect
The first option -- that some trials are more suceptable to
The first option -- that some categories are more suceptable to
issues with participant enrollment -- should allow us to
issues with participant enrollment -- should allow us to
isolate categories or trials that contribute the most to this effect.
isolate categories or trials that contribute the most to this effect.
This is not what we find when we inspect the categories
In figure
in figure
\ref{fig:pred_dist_dif_delay2}, it appears that most of the trials have
\ref{fig:pred_dist_dif_delay2}.
this high impact regime at $\delta_p > 0.75$.
Instead it appears that most of the categories have this high
impact regime when $\delta_p > 0.75$, although the maximum value
of this regime varies considerably.
Another explanation is that this is a modelling artefact due to priors
Another explanation is that this is a modelling artefact due to priors
with strong tails and the relatively low number of trials in
with strong tails and the relatively low number of trials in
@ -243,9 +221,7 @@ A few things lead me to believe this:
\begin{itemize}
\begin{itemize}
\item The low fractions of E-BFMI suggest that the sampler is struggling
\item The low fractions of E-BFMI suggest that the sampler is struggling
to explore some regions of the posterior.
to explore some regions of the posterior.
According to
According to \cite{standevelopmentteam_RuntimeWarnings_2022} this is