Conventional case–cohort design and analysis for studies of interaction

Abstract

The case-cohort study design has received significant methodological attention in the statistical and epidemiological literature but has not been used as widely as other cohort-based sampling designs, such as the nested case-control design. Despite its efficiency and practicality for a wide range of epidemiological study purposes, researchers may not yet be aware of the fact that the design can be analysed using standard software with only minor adjustments. Furthermore, although the large number of options for design and analysis of case-cohort studies may be daunting, they can be reduced to a few simple recommendations. We review conventional methods for the design and analysis of case-cohort studies and describe empirical comparisons based on a study of radiation, gene polymorphisms and cancer in the Japanese atomic bomb survivor cohort. Stratified, as opposed to simple, random subcohort selection is recommended, especially for studies of gene-environment interaction, which are notorious for lacking statistical power. Methods based on the score-unbiased exact pseudo-likelihood (or its analogue with stratified case-cohort data) are recommended for use in conjunction with the asymptotic variance estimator. We present an example of how to implement case-cohort analysis methods using SPSS, a popular statistical package that lacks some of the features necessary to directly adapt and implement published methods based on other software platforms. We also illustrate case-control analysis using Epicure, which provides greater risk-modelling flexibility than other software. Our conclusions and recommendations should help investigators to better understand and apply the case-cohort design in epidemiological research.