The Impact of the Underlying Risk in Control Group and Effect Measures in Non-Inferiority Trials With Time-to-Event Data: A Simulation Study

Background We designed a simulation study to assess how the conclusions of a non-inferiority trial (NIT) will change if the observed risk is different from the expected risk. Methods We simulated Weibull distribution time-to-event data with a true hazard ratio (HR) being equal or close to 1. The empirical margins and sample size of a hypothetical trial were chosen based on a systematic review. Setting the significance level at 5% for the two-sided confidence interval (CI), we examined the statistical power (i.e., the probabilities of the upper limit of the 95% CI falling within the margin) of using two measures at various underlying risk in the control group. Results Using the empirical margins, HRs of 1.2, 1.35 or 1.5, the statistical power is lower than 0.22 when the underlying risk in the control group is less than 10%, but the power increases along with the higher underlying risk. The predicted upper limit of the 95% CI of the difference in two Kaplan-Meier estimators (DTKME) is low when risk is low (< 20%) or high (> 80%), but reaches the highest value when risk is around 50%. When the underlying risk in the control group is lower than 10%, measures of DTKME resulted in much higher power than HR. Conclusions When HR is the effect measure, the probability of concluding non-inferiority will increase as the underlying risk in the control group increases. When DTKME is the effect measure, the probability of concluding non-inferiority will decrease as the underlying risk in the control increases. In this case, the probability of concluding non-inferiority is at a minimum when the control risk reaches about 50%. When the risk in the control arm is less than 10%, the conclusion of an NIT is sensitive to the choice of effect measure.