Objectives: Inverse probability of treatment weighting (IPTW) has been used in observational
studies to reduce selection bias. For estimates of the main effects to be obtained, a pseudo …

Propensity score methods are used to reduce the effects of observed confounding when
using observational data to estimate the effects of treatments or exposures. A popular …

Propensity score weighting methods are often used in non-randomized studies to adjust for
confounding and assess treatment effects. The most popular among them, the inverse …

The propensity score is defined as a subject's probability of treatment selection, conditional
on observed baseline covariates. Weighting subjects by the inverse probability of treatment …

This article discusses the augmented inverse propensity weighted (AIPW) estimator as an
estimator for average treatment effects. The AIPW combines both the properties of the …

Inverse probability weighting can be used to estimate the average treatment effect in
propensity score analysis. When there is lack of overlap in the propensity score distributions …

In individually randomised controlled trials, adjustment for baseline characteristics is often
undertaken to increase precision of the treatment effect estimate. This is usually performed …

Background It is important to estimate the treatment effect of interest accurately and precisely
within the analysis of randomised controlled trials. One way to increase precision in the …

Inverse probability-weighted estimation is a powerful tool for use with observational data. In
this article, we describe how this propensity score-based method can be used to compare …

Inverse propensity weighting (IPW) is a popular method for estimating treatment effects from
observational data. However, its correctness relies on the untestable (and frequently …