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Activity Number: 378
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 12:20 PM
Sponsor: SSC
Abstract #320834
Title: Analysis of Clustered and Biased Survival Data with Incomplete Covariates
Author(s): Hua Shen*
Companies: University of Calgary
Keywords: clustered data ; time-to-event analysis ; incomplete covariates ; left truncation
Abstract:

In studies of chronic diseases individuals are often routinely sampled subject to certain conditions on an event time of interest. For example subjects are required to have survived long enough or to be adverse event free at the point of screening to be recruited. These conditions yield response-biased samples featuring left-truncated event time distributions. Incomplete covariate data are widely encountered in these settings. Moreover, the failure times of the individuals within some groups can be correlated due to natural common features or shared environmental factors. The fact that the covariate distribution is affected by the left truncation selection criterion and the clustering is often ignored in standard methods leading to biased estimates. An algorithm is developed to deal with incomplete covariates in the analysis of clustered time-to-event data when the distribution of survival time and the effects of explanatory variables are of interests.


Authors who are presenting talks have a * after their name.

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