Purpose To demonstrate improvements in the precision of inverse probability-weighted
estimators by use of auxiliary variables, ie, determinants of the outcome that are …

Inverse probability weights are increasingly used in epidemiological analysis, and
estimation and application of weights to address a single bias are well discussed in the …

Standardized means, commonly used in observational studies in epidemiology to adjust for
potential confounders, are equal to inverse probability weighted means with inverse weights …

Weighting methods that adjust for observed covariates, such as inverse probability
weighting, are widely used for causal inference and estimation with incomplete outcome …

In the analysis of observational studies, inverse probability weighting (IPW) is commonly
used to consistently estimate the average treatment effect (ATE) or the average treatment …

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 …

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 …

Missing data is a common occurrence in epidemiologic research. In this paper, 3 data sets
with induced missing values from the Collaborative Perinatal Project, a multisite US study …

Correct specification of the exposure model is essential for unbiased estimation in marginal
structural models with inverse-probability-of-treatment weights. However, although flexible …

Inverse probability-weighted estimators are widely used in applications where data are
missing due to nonresponse or censoring and in the estimation of causal effects from …