It is common to present multiple adjusted effect estimates from a single model in a single
table. For example, a table might show odds ratios for one or more exposures and also for …

Measurement error in explanatory variables and unmeasured confounders can cause
considerable problems in epidemiologic studies. It is well recognized that under certain …

Background: Correlated data are ubiquitous in epidemiologic research, particularly in
nutritional and environmental epidemiology where mixtures of factors are often studied. Our …

Causal inferences about the effect of an exposure on an outcome may be biased by errors in
the measurement of either the exposure or the outcome. Measurement errors of exposure …

PURPOSE: Uncontrolled confounders are an important source of bias in epidemiologic
studies. The authors review and derive a set of parallel simple formulas for bias factors in the …

The article by Starr et al.(1) in this issue of the Journal provides a valuable starting point to
examine the role of model selection when using multivariate models in causal inference. In …

The assessment of indirect effects is an important tool for epidemiologists interested in
exploring the mechanisms of exposure-disease relations. A standard way of expressing an …

A number of covariate-balancing methods, based on the propensity score, are widely used
to estimate treatment effects in observational studies. If the treatment effect varies with the …

Abstract Mickey, RM (Dept of Mathematics and Statistics, U. of Vermont, Burlington, VT
05405) and S. Greenland. The impact of confounder selection criteria on effect estimation …

We use causal graphs and a partly hypothetical example from the Physicians' Health Study
to explain why a common standard method for quantifying direct effects (ie stratifying on the …