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

How often do articles depend on suppression effects for their findings? How often do they
disclose this fact? By suppression effects, we mean control-variable-induced increases in …

In the analysis of causal effects in non-experimental studies, conditioning on observable
covariates is one way to try to reduce unobserved confounder bias. However, a developing …

Estimating causal effects from randomized experiments is central to clinical research.
Reducing the statistical uncertainty in these analyses is an important objective for …

Propensity score analysis is a common approach to addressing confounding in
nonrandomized studies. Its implementation, however, requires important assumptions (eg …

In a case-control study, matching on a disease risk score (DRS), which includes many
confounders, should theoretically result in greater precision than matching on only a few …

Objective The objective of this study was to characterize the diffusion of methodological
innovation. Study Design and Setting Comparative case study analysis of the diffusion of two …

Suppose that an investigator is interested in quantifying an exposure-disease causal
association in a setting where the exposure, disease, and some potential confounders of the …

Abstract Summary scores that reduce baseline covariate information to a single value have
become standard tools for confounding control in pharmacoepidemiologic studies. The …

How often do articles depend on suppression effects for their findings? How often do they
disclose this fact? By suppression effects, we mean covariate-induced increases in effect …