Outcome-Dependent Sampling with Interval-Censored Failure Time Data

• Activity Number: 299 - Survival and Recurrent Events in Epidemiology
• Type: Contributed
• Date/Time: Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Qingning Zhou* and Jianwen Cai and Haibo Zhou
• Companies: University of North Carolina at Chapel Hill and University of North Carolina at Chapel Hill and University of North Carolina at Chapel Hill
• Keywords: Biased sampling ; Empirical likelihood ; Interval-censoring ; Semiparametric inference ; Sieve estimation

Abstract

Epidemiologic studies and disease prevention trials often seek to relate an exposure variable to a failure time that suffers from interval-censoring. We propose an outcome-dependent sampling (ODS) design with interval-censored failure time data, where we enrich the observed sample by selectively including certain more informative failure subjects like the case-cohort design. We develop a novel sieve semiparametric maximum empirical likelihood approach to analyze data collected from the proposed interval-censoring ODS design. This approach employs the empirical likelihood and sieve methods to deal with the infinite-dimensional nuisance parameters, which greatly reduces the dimensionality of the estimation problem and eases the computation difficulty. The consistency and asymptotic normality of the resulting regression parameter estimator are established. The results from our extensive simulation study show that the proposed design and inference procedure works well for practical situations and is more efficient than the alternative designs and competing methods. An example from the Atherosclerosis Risk in Communities (ARIC) study is provided for illustration.