1. 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

2. 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.

3. 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

4. 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

5. 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.

6. 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.