TY  - JOUR
PY  - 1994//
TI  - Flexible maximum likelihood methods for assessing joint effects in case-control studies with complex sampling
JO  - Biometrics
A1  - Wacholder, S.
A1  - Weinberg, C. R.
SP  - 350
EP  - 357
VL  - 50
IS  - 2
N2  - 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.<p /> <p>Language: en</p>
LA  - en
SN  - 0006-341X
UR  - http://dx.doi.org/
ID  - ref1
ER  -