Flexible maximum likelihood methods for assessing joint effects in case-control studies with complex sampling

Biometrics. 1994 Jun;50(2):350-7.

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 "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.

Publication types

  • Comparative Study

MeSH terms

  • Adult
  • Age Factors
  • Aged
  • Biometry*
  • Case-Control Studies*
  • Connecticut / epidemiology
  • Female
  • Humans
  • Idaho / epidemiology
  • Lung Neoplasms / epidemiology*
  • Lung Neoplasms / etiology
  • Male
  • Middle Aged
  • Neoplasms, Radiation-Induced / epidemiology*
  • Neoplasms, Radiation-Induced / etiology
  • Probability
  • Radon
  • Random Allocation
  • Risk Factors
  • Sex Factors
  • Smoking*
  • Utah / epidemiology

Substances

  • Radon