Validation of Surrogate Markers in Multiple Randomized Clinical Trials with Repeated Measurements: Canonical Correlation Approach

Part of the recent literature on the evaluation of biomarkers as surrogate endpoints starts from a multitrial context, which leads to a definition of validity in terms of the quality of both trial‐level and individual‐level association between the surrogate and true endpoints (Buyse et al., 2000, Bi...

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Bibliographic Details
Published in:	Biometrics 2004-12, Vol.60 (4), p.845-853
Main Authors:	Alonso, Ariel, Geys, Helena, Molenberghs, Geert, Kenward, Michael G., Vangeneugden, Tony
Format:	Article
Language:	eng
Subjects:	Antipsychotic Agents - therapeutic use antipsychotics Biological markers Biomarkers Biomarkers - analysis Biometrics Biometry Bivariate longitudinal data Canonical correlations Clinical trials Correlation analysis Correlations Covariance Endpoint Determination - statistics & numerical data Experimentation Humans Longitudinal Studies Meta analysis Meta-Analysis as Topic Models, Statistical Psychiatry Randomized clinical trials Randomized Controlled Trials as Topic - statistics & numerical data Risperidone - therapeutic use Sample size Schizophrenia Schizophrenia - drug therapy Standard deviation Statistical variance Surrogate marker Validation variance
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Summary:	Part of the recent literature on the evaluation of biomarkers as surrogate endpoints starts from a multitrial context, which leads to a definition of validity in terms of the quality of both trial‐level and individual‐level association between the surrogate and true endpoints (Buyse et al., 2000, Biostatistics1, 49–67). These authors concentrated on cross‐sectional continuous responses. However, in many randomized clinical studies, repeated measurements are encountered on either or both endpoints. A challenge in this setting is the formulation of a simple and meaningful concept of “surrogacy.”Alonso et al. (2003, Biometrical Journal45, 931–945) proposed the variance reduction factor (VRF) to evaluate surrogacy at the individual level. They also showed how and when this concept should be extended to study surrogacy at the trial level. Here, we approach the problem from the natural canonical correlation perspective. We define a class of canonical correlation functions that can be used to study surrogacy at the trial and individual level. We show that the VRF and the R2 measure defined by Buyse et al. (2000) follow as special cases. Simulations are conducted to evaluate the performance of different members of this family. The methodology is illustrated on data from a meta‐analysis of five clinical trials comparing antipsychotic agents for the treatment of chronic schizophrenia.
ISSN:	0006-341X 1541-0420