Predicting treatment effects using biomarker data in a meta-analysis of clinical trials.

A biomarker (S) measured after randomization in a clinical trial can often provide information about the true endpoint (T) and hence the effect of treatment (Z). It can usually be measured earlier and more easily than T and as such may be useful to shorten the trial length. A potential use of S is to completely replace T as a surrogate endpoint to evaluate whether the treatment is effective. Another potential use of S is to serve as an auxiliary variable to help provide information and improve the inference on the treatment effect prediction when T is not completely observed. The objective of this report is to focus on its role as an auxiliary variable and to identify situations when S can be useful to increase efficiency in predicting the treatment effect in a new trial in a multiple-trial setting. Both S and T are continuous. We find that higher efficiency gain is associated with higher trial-level correlation but not individual-level correlation when only S, but not T is measured in a new trial; but, the amount of information recovery from S is usually negligible. However, when T is partially observed in the new trial and the individual-level correlation is relatively high, there is substantial efficiency gain by using S. For design purposes, our results suggest that it is often important to collect markers that have high adjusted individual-level correlation with T and at least a small amount of data on T. The results are illustrated using simulations and an example from a glaucoma clinical trial.