Meta-analysis for surrogacy: accelerated failure time models and semicompeting risks modeling |
| |
Authors: | Ghosh Debashis Taylor Jeremy M G Sargent Daniel J |
| |
Affiliation: | Department of Statistics, The Pennsylvania State University, University Park, Pennsylvania 16802, USA. ghoshd@psu.edu |
| |
Abstract: | 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. |
| |
Keywords: | Copula Dependent censoring Latent factor Linear regression Multivariate failure time data Singular value decomposition |
本文献已被 PubMed 等数据库收录! |
|