Statistical inference for the accelerated failure time model under multivariate outcome dependent sampling design

Tsui-Shan Lu

Department of Mathematics

National Taiwan Normal University

    Researchers are always seeking for cost-effective designs due to a limited budget, especially for large biomedical or epidemiological studies. An outcome-dependent sampling (ODS) design, a retrospective sampling scheme, has been shown to improve the study efficiency while effectively reducing the monetary burden. Under the ODS design, one observes the covariates with a probability depending on the outcome and selects several supplemental samples from the most informative and appealing segments. Lu, Longnecker, and Zhou (2017) extended the ODS design to incorporate multivariate data often appeared in the recent studies and proposed a further generalization of the biased sampling.
In this talk, we consider a multivariate ODS (MODS) design for time-to-different-events data under the framework of a semiparametric accelerated failure time (AFT) model, allowing multiple disease outcomes with clustered failure times. We establish an estimating equation approach to estimate parameters based on induced smoothing. The asymptotic properties of the proposed estimators are developed. Simulation results show that the proposed design is more efficient and powerful than other existing approaches. The proposed method is illustrated with a real data set.

Keyword: outcome-dependent sampling, multivariate, AFT model, semiparametric