Semiparametric accelerated failure time modeling for multivariate failure times under multivariate outcome-dependent sampling designs

Tsui Shan Lu, Sangwook Kang, Haibo Zhou

Research output: Contribution to journalArticle

Abstract

Researchers working on large cohort studies are always seeking for cost-effective designs due to a limited budget. An outcome-dependent sampling (ODS) design, a retrospective sampling scheme where one observes covariates with a probability depending on the outcome and selects supplemental samples from more informative segments, improves the study efficiency while effectively controlling for the budget. To take the advantage of the ODS scheme when multivariate failure times are main response variables, relevant study designs and inference procedures need to be studied. In this paper, we consider a general multivariate-ODS design for multivariate failure times under the framework of a semiparametric accelerated failure time model. We develop a weighted estimating equations approach, based on the induced smoothing method, for parameter estimation. Extensive simulation studies show that our proposed design and estimator are more efficient than other competing estimators based on simple random samples. The proposed method is illustrated with a real data set from the Busselton Health Study.

Original languageEnglish
Pages (from-to)373-383
Number of pages11
JournalStatistics and its Interface
Volume13
Issue number3
DOIs
Publication statusPublished - 2020

Keywords

  • Biased sampling
  • Induced smoothing
  • Rank-based estimation
  • Resampling
  • Sandwich variance estimation
  • Weighted estimating equations

ASJC Scopus subject areas

  • Statistics and Probability
  • Applied Mathematics

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