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Meta‐analysis for Surrogacy: Accelerated Failure Time Models and Semicompeting Risks Modeling
Author(s) -
Ghosh Debashis,
Taylor Jeremy M. G.,
Sargent Daniel J.
Publication year - 2012
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2011.01633.x
Subject(s) - computer science , accelerated failure time model , surrogate endpoint , risk analysis (engineering) , endpoint determination , econometrics , clinical trial , medicine , machine learning , mathematics , covariate , radiology , pathology
Summary There has been great recent interest in the medical and statistical literature in the assessment and validation of surrogate endpoints as proxies for clinical endpoints in medical studies. More recently, authors have focused on using metaanalytical methods for quantification of surrogacy. In this article, we extend existing procedures for analysis based on the accelerated failure time model to this setting. An advantage of this approach relative to proportional hazards model is that it allows for analysis in the semicompeting risks setting, where we model the region where the surrogate endpoint occurs before the true endpoint. Several estimation methods and attendant inferential procedures are presented. In addition, between‐ and within‐trial methods for evaluating surrogacy are developed; a novel principal components procedure is developed for quantifying trial‐level surrogacy. The methods are illustrated by application to data from several studies in colorectal cancer.