A unified framework for the evaluation of surrogate endpoints in mental-health clinical trials.
For a number of reasons, surrogate endpoints are considered instead of the so-called true endpoint in clinical studies, especially when such endpoints can be measured earlier, and/or with less burden for patient and experimenter. Surrogate endpoints may occur more frequently than their standard counterparts. For these reasons, it is not surprising that the use of surrogate endpoints in clinical practice is increasing. Building on the seminal work of Prentice(1) and Freedman et al.,(2) Buyse et al. (3) framed the evaluation exercise within a meta-analytic setting, in an effort to overcome difficulties that necessarily surround evaluation efforts based on a single trial. In this article, we review the meta-analytic approach for continuous outcomes, discuss extensions to non-normal and longitudinal settings, as well as proposals to unify the somewhat disparate collection of validation measures currently on the market. Implications for design and for predicting the effect of treatment in a new trial, based on the surrogate, are discussed. A case study in schizophrenia is analysed.
Molenberghs
Geert
G
I-BioStat, Hasselt University, Diepenbeek, Belgium. geert.molenberghs@uhasselt.be
Burzykowski
Tomasz
T
Alonso
Ariel
A
Assam
Pryseley
P
Tilahun
Abel
A
Buyse
Marc
M
D015415:Biomarkers / Q000032:analysis*; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D055808:Drug Discovery; D023381:Endpoint Determination; D006801:Humans; D007257:Information Theory; D016013:Likelihood Functions; D008603:Mental Health; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care; D012559:Schizophrenia / Q000188:drug therapy
D015415:Biomarkers