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Activity Number: 35
Type: Contributed
Date/Time: Sunday, August 6, 2006 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #307077
Title: Estimating a Class of Causal Treatment Effect for Survival Data
Author(s): Jing Ning*+ and Mei-Cheng Wang and Zhiqiang Tan
Companies: Johns Hopkins University and Johns Hopkins University and Johns Hopkins University
Address: Department of Biostatistics, 615 N. Wolfe Street, Baltimore, MD, 21205,
Keywords: treatment effect ; proportional hazards model ; accelerated failure time model ; causal time ratio ; causal hazards ratio
Abstract:

To identify a treatment effect by time-to-event data, the proportional hazards model and accelerated failure time model have been widely used, which need strong model assumptions. In randomized clinical trial, the feature of randomization exists only at the initial time. In this talk, we define a class of causal treatment effect to overcome this problem due to the loss of the comparability of two groups. The treatment effects are compared with treatment effects arising out of proportional hazards model and accelerated failure time model. The asymptotic distribution of the estimator is derived. Real data from a randomized clinic trial, which aims to evaluate the treatment effect of brain malignant gliomas, are used for illustration.

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