Computational methods for case-cohort studies

• Published in: Computational statistics & data analysis Vol. 51; no. 8; pp. 3737 - 3748
• Main Authors: Langholz, Bryan, Jiao, Jenny
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.05.2007; Elsevier Science; Elsevier
• Series: Computational Statistics & Data Analysis
• Subjects: Bias; Cox model; Cumulative hazard; Exact sciences and technology; General topics; Linear inference, regression; Mathematics; Multivariate analysis; Numerical analysis; Numerical analysis. Scientific computation; Numerical methods in probability and statistics; Probability and statistics; Risk estimation; Risk sets; Sciences and techniques of general use; Statistics; Stratified Cox model; Time-dependent covariates; Risk sets; Risk estimation; Cox model; Bias; Time-dependent covariates; Stratified Cox model; Cumulative hazard; Breast disease; Variance estimation; Estimator robustness; Computing; Covariate; Asymptotic variance; Implementation; Cohort study; Cohort data; Method study; Data analysis; Fitting; Case study; Statistical regression; Lifetime; Cohort analysis; Statistical computation; Experimental design; Software; Small sample; Biased estimation
• ISSN: 0167-9473; 1872-7352
• DOI: 10.1016/j.csda.2006.12.028

Computational methods, which can be implemented using standard Cox regression software, are given for fitting “exact” pseudolikehood estimates and robust and asymptotic variance estimators from case-cohort data. These methods are based on the computational approach of Therneau and Li [1999. Computing the Cox model for case cohort designs. Lifetime Data Anal. 5, 99–112] but will be less subject to small sample bias. Further, it is shown how to accommodate time-dependent covariates and estimate absolute risk. Extensions to stratified case-cohort sampled data are also provided. The methods are illustrated in analyses of case-cohort samples from a study of radiation exposure from fluoroscopy and breast cancer using SAS software.