Weighted analyses for cohort sampling designs.
Weighted analysis methods are considered for cohort sampling designs that allow subsampling of both cases and non-cases, but with cases generally sampled more intensively. The methods fit into the general framework for the analysis of survey sampling designs considered by Lin (Biometrika 87:37-47, 2000). Details are given for applying the general methodology in this setting. In addition to considering proportional hazards regression, methods for evaluating the representativeness of the sample and for estimating event-free probabilities are given. In a small simulation study, the one-sample cumulative hazard estimator and its variance estimator were found to be nearly unbiased, but the true coverage probabilities of confidence intervals computed from these sometimes deviated significantly from the nominal levels. Methods for cross-validation and for bootstrap resampling, which take into account the dependencies in the sample, are also considered.
Gray
Robert J
RJ
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA 02115, USA. gray@jimmy.harvard.edu
D016022:Case-Control Studies; D015331:Cohort Studies; D003198:Computer Simulation; D015991:Epidemiologic Measurements; D006801:Humans; D015233:Models, Statistical; D011336:Probability; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D012107:Research Design / Q000592:standards*; D018401:Sample Size