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Semiparametric Models For Causal Inference In Time To Event Studies
【2014.8.4 10:00am, N613】

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 2014-8-4 

  Colloquia & Seminars 

  Speaker

Prof.Zhiliang Ying,Department of Statistics,Columbia University

  Title

Semiparametric Models For Causal Inference In Time To Event Studies

  Time

2014.8.4 10:00am

  Venue

N613  

  Abstract

We consider causal inference in randomized survival studies with right censored outcomes and all-or-nothing compliance, using semiparametric transformation models to estimate the distribution of survival times in treatment and control groups, conditional on covariates and latent compliance type. Maximum likelihood is used to estimate the parameters of the transformation models, using a specially designed expectation-maximization (EM) algorithm to overcome the computational difficulties created by the mixture structure of the problem and the infinite dimensional parameter in the transformation models. The estimators are shown to be consistent, asymptotically normal, and semi parametrically efficient. We will also show results of an application to the HIP study.*Joint work with W. Yu, K. Chen and M. Sobel. 

  Affiliation

 

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