On Meta-Analytic Assessment of Surrogate Outcomes

• Activity Number: 152
• Type: Contributed
• Date/Time: Monday, August 12, 2002 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section*
• Author(s): Mitchell Gail*+ and Ruth Pfeiffer and Raymond Carroll and Hans Van Houwelingen
• Affiliation(s): National Cancer Institute and National Cancer Institute and Texas A&M University and Leiden University
• Keywords: Surrogate Endpoints ; Empirical Bayes Procedures ; Clinical Trials ; Meta-analysis ; Between Study Variation ; Bootstrap

Abstract

We discuss meta-analytic estimation of the effect of a new treatment on a true clinical outcome measure, T, from its effect on a surrogate response, S. The meta-analytic approach uses data from a series of previous studies of interventions similar to the new treatment to estimate relationships that can be used to infer the magnitude of the effect of the new treatment on T from its effects on S. We extend the class of models to cover a broad range of applications in which the parameters define features of the marginal distribution of (T, S). We present a new bootstrap procedure to allow for the variability in estimating the distribution that governs between study variation. Ignoring this variability can lead to confidence intervals that are much too narrow. Our calculations can be used to determine whether a new study, based only on S, will yield estimates of the treatment effect on T that are precise enough to be useful. Compared to direct measurement on T, the meta-analytic approach has a number of limitations, including serious loss of precision and difficulties in defining the class of previous studies to be used to predict the effects on T for a new intervention.