The impact of unmeasured baseline effect modification on estimates from an inverse probability of treatment weighted logistic model.
We present the results of a Monte Carlo simulation study in which we demonstrate how strong baseline interactions between a confounding variable and a treatment can create an important difference between the marginal effect of exposure on outcome (as estimated by an inverse probability of treatment weighted logistic model) and the conditional effect (as estimated by an adjusted logistic regression model). The scenarios that we explored included one with a rare outcome and a strong and prevalent effect measure modifier where, across 1,000 simulated data sets, the estimates from an adjusted logistic regression model (mean beta = 0.475) and an inverse probability of treatment weighted logistic model (mean beta = 2.144) do not coincide with the known true effect (beta = 0.68925) when the effect measure modifier is not accounted for. When the marginal and conditional estimates do not coincide despite a rare outcome this may suggest that there is heterogeneity in the effect of treatment between individuals. Failure to specify effect measure modification in the statistical model appears to results in systematic differences between the conditional and marginal estimates. When these differences in estimates are observed, testing for or including interactions or non-linear modeling terms may be advised.
Collaborative Health Studies Coordinating Center, Department of Biostatistics, University of Washington, Seattle, WA 98115, USA. jacd@u.washington.edu
Joseph A C
JA
Delaney
Robert W
RW
Platt
Samy
S
Suissa
D015986:Confounding Factors, Epidemiologic; D016021:Epidemiologic Studies; D016015:Logistic Models; D009010:Monte Carlo Method