T Stürmer1, H Brenner. 1. Department of Epidemiology, University of Ulm, Germany.
Abstract
BACKGROUND: To increase the precision and power of case-control studies, controls are often matched on age and sex, but rarely on other known risk factors. Expensive tests of genetic susceptibility in a case-control study of laryngeal cancer made us examine the effect of matching for smoking and alcohol consumption on the power and potential size reduction of the required control sample. METHODS: According to published smoking and alcohol consumption distributions in laryngeal cancer cases and population controls, we simulated 10000 frequency-matched and unmatched studies. The exposure of interest was distributed according to different scenarios concerning its relation with the disease and with smoking and alcohol consumption. Studies were analysed with multivariable logistic regression. RESULTS: Matching increased the precision and power in all scenarios. The gain was most pronounced in scenarios assuming moderate confounding by smoking and alcohol consumption. In such scenarios, equivalent precision or power was only obtained with three times as many unmatched as matched controls. CONCLUSIONS: Matching on strong risk factors may increase the precision and power of case-control studies considerably. In studies employing expensive biologic testing, matching on known strong risk factors may be cost-effective more often than previously thought.
BACKGROUND: To increase the precision and power of case-control studies, controls are often matched on age and sex, but rarely on other known risk factors. Expensive tests of genetic susceptibility in a case-control study of laryngeal cancer made us examine the effect of matching for smoking and alcohol consumption on the power and potential size reduction of the required control sample. METHODS: According to published smoking and alcohol consumption distributions in laryngeal cancer cases and population controls, we simulated 10000 frequency-matched and unmatched studies. The exposure of interest was distributed according to different scenarios concerning its relation with the disease and with smoking and alcohol consumption. Studies were analysed with multivariable logistic regression. RESULTS: Matching increased the precision and power in all scenarios. The gain was most pronounced in scenarios assuming moderate confounding by smoking and alcohol consumption. In such scenarios, equivalent precision or power was only obtained with three times as many unmatched as matched controls. CONCLUSIONS: Matching on strong risk factors may increase the precision and power of case-control studies considerably. In studies employing expensive biologic testing, matching on known strong risk factors may be cost-effective more often than previously thought.
Authors: Maria B Lyng; Anne-Vibeke Lænkholm; Qihua Tan; Werner Vach; Karina H Gravgaard; Ann Knoop; Henrik J Ditzel Journal: PLoS One Date: 2013-01-16 Impact factor: 3.240