Joint Statistical Meetings 2018 Real-time Study Milestone Projection in Clinical Trials with Time-to-Event Endpoints Yanping Liu, Gang Jia Joint Statistical Meetings 2018

Introduction In randomized clinical trials with time-to-event endpoints, there are commonly one or more planned interim analyses plus a final analysis, spanning over a few years. Predicting the timing of these analyses (i.e predicting the number of events within a certain timeframe) is critical for success: Resource allocation; Regulatory submission planning; Clinical operation; Business strategy. … In practice, the protocol pre-specified triggers (i.e. # of events) are often ‘hit or miss’. The essence of such prediction mainly depends on patient accrual and event rates, which might vary throughout the whole trial.

Short-term Projection Prediction Methods Short-term Projection Linear Extrapolation Average Rate Method Long-term Projection Exposure-adjusted Event Rate Parametric Estimation Piecewise Constant Risk Approach

Short-term Projection Linear Extrapolation Plot cumulative # of events over time and extrapolate the near future values Average Rate Method Average over the preceding 3/6 monthly events Easy to implement Good for short-term projection Assumption of the same rate or same average rate before and after the projection

Long-term Projection Exposure-adjusted Event Rate Pros: relatively easy to implement Cons: keep the original assumptions and model Parametric Estimation Pros: relatively precise; utilize all observed data Cons: more complicated; if assumption misses, then not accurate; model selection is important Piecewise Constant Risk Approach Survival function is not smooth A sequential testing approach, using likelihood ratio statistics A sequence of significance levels, which adequately control the overall significance level.

Summary We should choose different methods based on different predicting timing and different practical data. Monitoring the data pattern during the study is important. If change points exist, the piecewise constant risk approach would be more appropriate.