Accelerated failure time model for data from outcome-dependent sampling.

TitleAccelerated failure time model for data from outcome-dependent sampling.
Publication TypeJournal Article
Year of Publication2021
AuthorsYu, Jichang, Haibo Zhou, and Jianwen Cai
JournalLifetime Data Anal
Date Published2021 Jan
KeywordsAlgorithms, Cohort Studies, Female, Fertility, Fluorocarbons, Humans, Likelihood Functions, Maternal Exposure, Norway, Outcome Assessment, Health Care, Sampling Studies, Survival Analysis

Outcome-dependent sampling designs such as the case-control or case-cohort design are widely used in epidemiological studies for their outstanding cost-effectiveness. In this article, we propose and develop a smoothed weighted Gehan estimating equation approach for inference in an accelerated failure time model under a general failure time outcome-dependent sampling scheme. The proposed estimating equation is continuously differentiable and can be solved by the standard numerical methods. In addition to developing asymptotic properties of the proposed estimator, we also propose and investigate a new optimal power-based subsamples allocation criteria in the proposed design by maximizing the power function of a significant test. Simulation results show that the proposed estimator is more efficient than other existing competing estimators and the optimal power-based subsamples allocation will provide an ODS design that yield improved power for the test of exposure effect. We illustrate the proposed method with a data set from the Norwegian Mother and Child Cohort Study to evaluate the relationship between exposure to perfluoroalkyl substances and women's subfecundity.

Alternate JournalLifetime Data Anal
Original PublicationAccelerated failure time model for data from outcome-dependent sampling.
PubMed ID33044612
PubMed Central IDPMC7856009
Grant ListP30 ES010126 / ES / NIEHS NIH HHS / United States
R01 ES021900 / ES / NIEHS NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
P42 ES031007 / ES / NIEHS NIH HHS / United States