Sample size and power for case-control studies when exposures are continuous.
In estimating the sample size for a case-control study, epidemiologic texts present formulae that require a binary exposure of interest. Frequently, however, important exposures are continuous and dichotomization may result in a 'not exposed' category that has little practical meaning. In addition, if risks vary monotonically with exposure, then dichotomization will obscure risk effects and require a greater number of subjects to detect differences in the exposure distributions among cases and controls. Starting from the usual score statistic to detect differences in exposure, this paper develops sample size formulae for case-control studies with arbitrary exposure distributions; this includes both continuous and dichotomous exposure measurements as special cases. The score statistic is appropriate for general differentiable models for the relative odds, and, in particular, for the two forms commonly used in prospective disease occurrence models: (1) the odds of disease increase linearly with exposure; or (2) the odds increase exponentially with exposure. Under these two models we illustrate calculation of sample sizes for a hypothetical case-control study of lung cancer among non-smokers who are exposed to radon decay products at home.
Lubin
J H
JH
Biostatistics Branch, National Cancer Institute, Bethesda, Maryland 20205.
Gail
M H
MH
Ershow
A G
AG
D004781:Environmental Exposure; D004812:Epidemiologic Methods; D006801:Humans; D008175:Lung Neoplasms / Q000209:etiology; D008962:Models, Theoretical; D009381:Neoplasms, Radiation-Induced / Q000209:etiology; D011336:Probability; D011829:Radiation Dosage; D011848:Radioactive Pollutants / Q000009:adverse effects; D011886:Radon / Q000009:adverse effects; D012189:Retrospective Studies; D012306:Risk; D012494:Sampling Studies; D013997:Time Factors
D011848:Radioactive Pollutants; D011886:Radon