Potential gain in efficiency and power to detect gene-environment interactions by matching in case-control studies

There is growing interest in interactions between genetic and environmental risk factors of disease, but adequate power to detect such interactions in epidemiologic studies is of concern. The aim of this paper is to quantify the effect of matching on the efficiency of estimation and power to detect gene-environment interactions in case-control studies.

Starting from an empirical example in cancer epidemiology, we simulated frequency matched and unmatched case-control studies for a wide range of assumptions regarding the prevalence and the effects of an environmental and a genetic factor on disease risk as well as the quality and quantity of the interaction between these factors. Simulated studies were analyzed with multivariable logistic regression.

Matching increased the efficiency and power in most scenarios. The gain was most pronounced in scenarios assuming a low prevalence of the environmental exposure. In such scenarios, equivalent power was only obtained with more than twice as many unmatched than matched controls.

Frequency matching for known environmental risk factors with a low prevalence in the population may increase the efficiency of estimation and power of case-control studies to detect gene-environment interactions considerably. Investigators should weigh the gain in efficiency and power against known potential disadvantages of matching.