The validation of surrogate endpoints in meta-analyses of randomized experiments.

By M. Buyse, Geert Molenberghs, T. Burzykowski, D. Renard, H. Geys. Biostatistics, 2000

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Random Effects Models for Longitudinal Data (2010, methodology): “For example, the approach has been used in a non-longitudinal setting to validate surrogate endpoints in meta-analyses (Buyse et al. 2000, Burzykowski et al. 2001) or to model multivariate clustered data (Thum 1997).”
Transportability across studies: A formal approach (2011, background): “…traditional deﬁnitions for a surrogate endpoint (e.g., Prentice (1989); Freedman et al. (1992); Lin et al. (1997); Buyse and Molenberghs (1998); Buyse et al. (2000)) are based on regressions, they are unwittingly motivated by causal considerations, and several attempts have been made since to…”
Principal Stratification in Causal Inference (2002, methodology): “7 studies with long follow-up, where whether or not the subject drops out is a post-treatment variable (missingness of outcome); 7 studies where the outcome intended to be recorded can be “censored by death”; 7 studies comparing drugs for AIDS patients, where “surrogate” markers of progression, such”