@article{ref1,
title="Flexible maximum likelihood methods for assessing joint effects in case-control studies with complex sampling",
journal="Biometrics",
year="1994",
author="Wacholder, S. and Weinberg, C. R.",
volume="50",
number="2",
pages="350-357",
abstract="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 &quot;first-stage&quot; 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 &quot;second-stage&quot; 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>",
language="en",
issn="0006-341X",
doi="",
url="http://dx.doi.org/"
}