title: Testing goodness-of-fit for the proportional hazards model based on nested case-control data.
abstract: Nested case-control sampling is a popular design for large epidemiological cohort studies due to its cost effectiveness. A number of methods have been developed for the estimation of the proportional hazards model with nested case-control data; however, the evaluation of modeling assumption is less attended. In this article, we propose a class of goodness-of-fit test statistics for testing the proportional hazards assumption based on nested case-control data. The test statistics are constructed based on asymptotically mean-zero processes derived from Samuelsen's maximum pseudo-likelihood estimation method. In addition, we develop an innovative resampling scheme to approximate the asymptotic distribution of the test statistics while accounting for the dependent sampling scheme of nested case-control design. Numerical studies are conducted to evaluate the performance of our proposed approach, and an application to the Wilms' Tumor Study is given to illustrate the methodology.
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, North Carolina, U.S.A.
authors.forename: Wenbin
authors.initials: W
authors.lastname: Lu
authors.forename: Mengling
authors.initials: M
authors.lastname: Liu
authors.forename: Yi-Hau
authors.initials: YH
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D016022:Case-Control Studies; D015331:Cohort Studies; D006801:Humans; D016013:Likelihood Functions; D009367:Neoplasm Staging; D011379:Prognosis; D016016:Proportional Hazards Models; D012044:Regression Analysis; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012680:Sensitivity and Specificity; D009396:Wilms Tumor / Q000453:epidemiology* / Q000473:pathology*
keywords: Goodness-of-fit test; Nested case-control sampling; Proportional hazards model; Pseudo-likelihood estimation; Resampling method