A weighting approach to causal effects and additive interaction in case-control studies: marginal structural linear odds models.

ABSTRACT: Estimates of additive interaction from case-control data are often obtained by logistic regression; such models can also be used to adjust for covariates. This approach to estimating additive interaction has come under some criticism because of possible misspecification of the logistic model: If the underlying model is linear, the logistic model will be misspecified. The authors propose an inverse probability of treatment weighting approach to causal effects and additive interaction in case-control studies. Under the assumption of no unmeasured confounding, the approach amounts to fitting a marginal structural linear odds model. The approach allows for the estimation of measures of additive interaction between dichotomous exposures, such as the relative excess risk due to interaction, usin

SUBMITTER: VanderWeele TJ 

PROVIDER: S-EPMC3246690 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature