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Predicting treatment effects using biomarker data in a meta‐analysis of clinical trials
Author(s) -
Li Y.,
Taylor J. M. G.
Publication year - 2010
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3931
Subject(s) - correlation , inference , surrogate endpoint , clinical trial , biomarker , statistics , computer science , variable (mathematics) , sample size determination , econometrics , medicine , mathematics , artificial intelligence , mathematical analysis , biochemistry , chemistry , geometry
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. Copyright © 2010 John Wiley & Sons, Ltd.