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Activity Number: 219
Type: Invited
Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #314290 View Presentation
Title: Robust Estimation of the Proportion of Treatment Effect Explained by Surrogate Marker Information
Author(s): Layla Parast* and Lu Tian
Companies: RAND Corporation and Stanford University
Keywords: surrogate marker ; nonparametric estimation ; survival ; kernel
Abstract:

Previous research on identifying and validating surrogate markers has focused on estimation of the proportion of treatment effect explained by a surrogate marker since a valid surrogate marker should capture a large proportion of the true treatment effect on the primary outcome. Current methods to estimate the proportion of treatment effect explained usually require restrictive model assumptions that may not hold in practice and thus may lead to biased estimates of this quantity. We propose a nonparametric procedure to estimate the proportion of treatment effect explained by a potential surrogate marker and extend this procedure to a setting with censored time-to-event outcomes. We compare our approach to previously proposed model-based approaches which require specification of models describing the relationship between the surrogate and the primary outcome and propose a variance estimation procedure based on perturbation-resampling. Simulation studies demonstrate that the procedure performs well in finite samples and outperforms model-based procedures when the specified models are not correct. We illustrate our proposed procedure using data from the Diabetes Prevention Program.


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