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

Abstract

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.




J Clin Med Res. 2018;10(5):376-383
doi: https://doi.org/10.14740/jocmr3349e