Flexible maximum likelihood methods for assessing joint effects in case-control studies with complex sampling.
Case-control studies can often be made more efficient by using frequency matching, randomized recruitment, stratified sampling, or two-stage sampling. These designs share two common features: (1) some "first-stage" variables are ascertained for all study subjects, while complete variable ascertainment is carried out for only a selected subsample, and (2) the subsampling of subjects for "second-stage" variable ascertainment depends jointly on their disease status and their observed first-stage variables. Because first-stage variables alter the subsampling fractions, standard analyses require a multiplicative specification of any joint effects of a second- and a first-stage variable. We show that by making use of missing data methods, maximum likelihood estimates can be obtained for risk parameters of interest, even those characterizing interactions between first- and second-stage variables. Joint effects can thus be modelled flexibly, with allowance for both additive and multiplicative models. Preliminary data from a case-control study of lung cancer as related to age, sex, and smoking provide an example, leading to the suggestion that the combined effect of age and smoking is multiplicative.
Wacholder
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Biostatistics Branch, National Cancer Institute, Rockville, Maryland 20852.
Weinberg
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CR
D000328:Adult; D000367:Age Factors; D000368:Aged; D001699:Biometry; D016022:Case-Control Studies; D003237:Connecticut / Q000453:epidemiology; D005260:Female; D006801:Humans; D007061:Idaho / Q000453:epidemiology; D008175:Lung Neoplasms / Q000453:epidemiology* / Q000209:etiology; D008297:Male; D008875:Middle Aged; D009381:Neoplasms, Radiation-Induced / Q000453:epidemiology* / Q000209:etiology; D011336:Probability; D011886:Radon; D011897:Random Allocation; D012307:Risk Factors; D012737:Sex Factors; D012907:Smoking; D014589:Utah / Q000453:epidemiology
D011886:Radon