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American Journal of Epidemiology Vol. 139, No. 3: 312-322
Copyright © 1994 by The Johns Hopkins University School of Hygiene and Public Health

research-article

Assessment of Ecologic Regression in the Study of Lung Cancer and Indoor Radon

Christine A. Stidley1,2, and Jonathan M. Samet1,3

1The New Mexico Tumor Registry, Cancer Research and Treatment Center Albuquerque, NM
2Department of Family and Community Medicine, University of New Mexico School of Medicine Albuquerque, NM
3Department of Medicine, University of New Mexico School of Medicine Albuquerque, NM

Reprint requests to Dr. Christine A. Stidley, University of New Mexico School of Medicine, New Mexico Tumor Registry, 900 Cammo de Salud, NE, Albuquerque, NM 87131-5306

Ecologic regression studies conducted to assess the cancer risk of indoor radon to the general population are subject to methodological limitations, and they have given seemingly contradictory results. The authors use simulations to examine the effects of two major methodological problems that affect these studies measurement error and misspecification of the risk model. In a simulation study of the effect of measurement error caused by the sampling process used to estimate radon exposure for a geographic unit, both the effect of radon and the standard error of the effect estimate were underestimated, with greater bias for smaller sample sizes. In another simulation study, which addressed the consequences of uncontrolled confounding by cigarette smoking, even small negative correlations between county geometric mean annual radon exposure and the proportion of smokers resulted in negative average estimates of the radon effect. A third study considered consequences of using simple linear ecologic models when the true underlying model relation between lung cancer and radon exposure is nonlinear. These examples quantify potential biases and demonstrate the limitations of estimating risks from ecologic studies of lung cancer and indoor radon.

bias (epidemiology); epidemiologic methods; lung neoplasms; models, statistical; radon; regression analysis; smoking


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