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Activity Number: 655 - Improving Power and Generalizability in Causal Effect Estimation Using Multicenter and Network Designs
Type: Topic Contributed
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #330066 Presentation
Title: On the Parameter Estimation and Modeling of Clustered Survival Data with Delayed Entry and Missing Covariates
Author(s): Hua Shen*
Companies: University of Calgary
Keywords: Parameter Estimation; Clustered Data; Survival Data; Truncation; Missing Data

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 times. Moreover, to include a large number of participants, data often arise from distinct groups or different geographic locations. The failure times of the individuals within clusters can be correlated due to natural common features or shared environmental factors. Incomplete covariate data are widely encountered in these settings. The fact that the covariate distribution is affected by the left truncation selection criterion and the intracluster correlation in clusters is often ignored in standard methods leading to biased estimates. An algorithm is developed to deal with incomplete covariates in the analysis of clustered and left-truncated 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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