Model Free Approach to Quantifying the Proportion of Treatment Effect Explained by a Surrogate Marker
报告人：Xuan Wang（Harvard University）
Abstract: In randomized clinical trials, the primary outcome, Y, often requires long term follow-up and/or is costly to measure. For such settings, it is desirable to use a surrogate marker, S, to infer the treatment effect on Y. Identifying such an S and quantifying the proportion of treatment effect on Y explained (PTE) by the effect on S are thus of great importance. Most existing methods for quantifying the PTE are model-based and may yield biased estimates under model mis-specification. Recently proposed non-parametric methods require strong assumptions to ensure that the PTE is between [0, 1]. Additionally, optimal use of S to approximate the treatment effect on Y is especially important when S relates to Y non-linearly. In this paper, we identify an optimal transformation of S, gopt(S), such that the PTE can be inferred based on gopt(S). In addition, we provide two novel model free definitions of PTE and simple conditions for ensuring the PTE is between [0, 1]. We provide non-parametric estimation procedures and establish asymptotic properties of the proposed estimators. Simulation studies demonstrate that the proposed methods perform well in finite samples. We illustrate the proposed procedures using a randomized study of HIV patients.
About the Speaker:
Xuan Wang is now a Research Associate at Department of Biostatistics, Chan School of Public Health, Harvard University.
Zoom Meeting :https://us02web.zoom.us/j/81212638860
Meeting ID：812 1263 8860