Adapting Threshold Regression to Analyzing Survival Data Collected in Complex Surveys

• Activity Number: 484
• Type: Topic Contributed
• Date/Time: Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Government Statistics Section
• Author(s): Dandan Liao* and Yan Li and Mei-Ling Ting Lee
• Companies: University of Maryland and University of Maryland and University of Maryland
• Keywords: threshold regression ; complex sampling designs ; Taylor linearization variance estimators ; time-to-event data ; intra-cluster correlation ; differential selection probabilities

Abstract

Cox proportional hazards (PH) model is a common statistical technique employed for analyzing time-to-event data to provide risk assessment of onset of events. In cases where the assumption of proportional hazards is not tenable, a series of threshold regression (TR) methods have been developed for analysis of time-to-event data collected by simple random sampling. In the setting of complex surveys that involve 1) differential selection probabilities of study subjects and 2) the intra-cluster correlations induced by the multistage cluster sampling, however, the TR models have not been explored. In this paper, we propose to extend TR procedures to account for complex sampling designs. The pseudo-maximum likelihood estimation technique is applied to estimate the TR model parameters and computationally-efficient Taylor linearization variance estimators that consider intra-cluster correlation as well as differential selection probabilities are developed. Simulation studies with various complex designs are conducted, which is further illustrated using a complex data collected from National Health and Nutrition Examination Survey (NHANES III) linked to death certificate records.