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).