Abstract Treatment effects, especially when comparing two or more therapeutic alternatives
as in comparative effectiveness research, are likely to be heterogeneous across age …

Confounding can cause substantial bias in nonexperimental studies that aim to estimate
causal effects. Propensity score methods allow researchers to reduce bias from measured …

Selection bias remains a subject of controversy. Existing definitions of selection bias are
ambiguous. To improve communication and the conduct of epidemiologic research focused …

Methods based on propensity score (PS) have become increasingly popular as a tool for
causal inference. A better understanding of the relative advantages and disadvantages of …

Robins' generalized methods (g methods) provide consistent estimates of contrasts (eg
differences, ratios) of potential outcomes under a less restrictive set of identification …

Propensity score–based analysis is increasingly being used in observational studies to
estimate the effects of treatments, interventions, and exposures. We introduce the concept of …

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 …

Selection bias due to loss to follow up represents a threat to the internal validity of estimates
derived from cohort studies. Over the past 15 years, stratification-based techniques as well …

Increasingly, the statistical and epidemiologic literature is focusing beyond issues of internal
validity and turning its attention to questions of external validity. Here, we discuss some of …

Doubly robust estimation combines a form of outcome regression with a model for the
exposure (ie, the propensity score) to estimate the causal effect of an exposure on an …