Biostatistics 1:231-246 (2000)
© 2000 Oxford University Press
On meta-analytic assessment of surrogate outcomes
1 National Cancer Institute, Division of
Cancer Epidemiology and Genetics, Executive Plaza South, Room 8032,
1620 Executive Boulevard, MSC 7244, Bethesda, MD 20892-7244, USA
2 National Cancer Institute, Division of
Cancer Epidemiology and Genetics, Executive Plaza South, Room 8017,
1620 Executive Boulevard, MSC 7244, Bethesda, MD 20892-7244, USA
3 Medical Statistics, Leiden University
Medical Center, Wassenaarseweg 62, PO Box 9604, 2300 RC Leiden, The
Netherlands
4 Department of Statistics, Texas A&M
University, College Station, TAMU 3143, TX 77843-3143, USA
We discuss the strengths and weaknesses of the meta-analytic
approach to estimating the effect of a new treatment on a true
clinical outcome measure, 
, from the effect of treatment on a
surrogate response, 
. The meta-analytic approach (see Daniels
and Hughes (1997) 16, 1965–1982) uses data from a series
of previous studies of interventions similar to the new treatment.
The data are used to estimate relationships between summary measures
of treatment effects on and that can be used to infer
the magnitude of the effect of the new treatment on from its
effects on . We extend the class of models to cover a broad
range of applications in which the parameters define features of the
marginal distribution of 
. We present a new bootstrap
procedure to allow for the variability in estimating the distribution
that governs the between-study variation. Ignoring this variability
can lead to confidence intervals that are much too narrow. The
meta-analytic approach relies on quite different data and assumptions
than procedures that depend, for example, on the conditional
independence, at the individual level, of treatment and ,
given (see Prentice (1989) 8, 431–440).
Meta-analytic calculations in this paper can be used to determine
whether a new study, based only on , will yield estimates of
the treatment effect on that are precise enough to be useful.
Compared to direct measurement on , the meta-analytic
approach has a number of limitations, including likely serious loss
of precision and difficulties in defining the class of previous
studies to be used to predict the effects on for a new
intervention.
Keywords: Bootstrap; Bootstrap confidence intervals; Clinical trials; Empirical Bayes procedures; Meta-analysis; Surrogate endpoints
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