Assessment of a clinical trial’s probability of success
Assuming a drug altered the disease progression rate as shown in
Equation 6, where E was the drug effect and all other parameters
were the same as in Equation 5, the respective longitudinal models were
used to simulate the severity and SoS data for 6000 patients, stratified
to either receive the drug treatment or not, according to the assessment
schedule in the PPMI trial.
\(S_{i\left(t\right)}=S_{i,0}+\text{Slope}_{i}\bullet\left(1-E\right)\bullet time+IOV\)Equation 6
The change from baseline of the simulated data was fitted to a full
model which included a drug effect, or a reduced model which did not
include any drug effect, for treatment durations of 6, 12, 18 and 24
months. Treatment difference was estimated to generate individual
objective function (iOFV) values, which were subject to likelihood ratio
test (p<0.05) per Monte Carlo Mapped Power (MCMP) method,
which has been described in detail elsewhere29, based
on 1000 treatment datasets for a wide range of samples sizes.
A range of potential drug effects – 100 random values from a normal
distribution (mean of 0.3 and variance of 0.0169, which generated
5th-95th quantiles of 0.1-0.5) were
tested. The collective proportion of trials showing a statistically
significant positive drug effect across the entire range was calculated
as the Probability of Success (PoS).15 The PoS was
calculated for both severity and SoS endpoints, based on all 33 items or
only the non-tremor items (see Results section).