title: Using the entire history in the analysis of nested case cohort samples.
abstract: Countermatching designs can provide more efficient estimates than simple matching or case-cohort designs in certain situations such as when good surrogate variables for an exposure of interest are available. We extend pseudolikelihood estimation for the Cox model under countermatching designs to models where time-varying covariates are considered. We also implement pseudolikelihood with calibrated weights to improve efficiency in nested case-control designs in the presence of time-varying variables. A simulation study is carried out, which considers four different scenarios including a binary time-dependent variable, a continuous time-dependent variable, and the case including interactions in each. Simulation results show that pseudolikelihood with calibrated weights under countermatching offers large gains in efficiency if compared to case-cohort. Pseudolikelihood with calibrated weights yielded more efficient estimators than pseudolikelihood estimators. Additionally, estimators were more efficient under countermatching than under case-cohort for the situations considered. The methods are illustrated using the Colorado Plateau uranium miners cohort. Furthermore, we present a general method to generate survival times with time-varying covariates. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Kresge 803B, Boston, MA 02115, U.S.A.
authors.forename: C L
authors.initials: CL
authors.lastname: Rivera
authors.affiliation: Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Kresge 803B, Boston, MA 02115, U.S.A.
authors.forename: T
authors.initials: T
authors.lastname: Lumley
mesh_terms: D016022:Case-Control Studies; D015331:Cohort Studies; D008906:Mining; D015233:Models, Statistical; D016273:Occupational Exposure; D014501:Uranium
chemical_list: D014501:Uranium
keywords: Cox model; calibration; countermatching; survey sampling; time-varying covariates