Combining data from 2 nested case-control studies of overlapping cohorts to improve efficiency

Abstract

Researchers subject to time and budget constraints may conduct small nested case-control studies with individually matched controls to help optimize statistical power. In this paper, we show how precision can be improved considerably by combining data from a small nested case-control study with data from a larger nested case-control study of a different outcome in the same or overlapping cohort. Our approach is based on the inverse probability weighting concept, in which the log-likelihood contribution of each individual observation is weighted by the inverse of its probability of inclusion in either study. We illustrate our approach using simulated data and an application where we combine data sets from 2 nested case-control studies to investigate risk factors for anorexia nervosa in a cohort of young women in Sweden.