An introduction to inverse probability of treatment weighting in observational research

In this article we introduce the concept of inverse probability of treatment weighting (IPTW) and describe how this method can be applied to adjust for measured confounding in observational research, illustrated by a clinical example from nephrology. IPTW involves two main steps. First, the probability-or propensity-of being exposed to the risk factor or intervention of interest is calculated, given an individual's characteristics (i.e. propensity score). Second, weights are calculated as the inverse of the propensity score. The application of these weights to the study population creates a pseudopopulation in which confounders are equally distributed across exposed and unexposed groups. We also elaborate on how weighting can be applied in longitudinal studies to deal with informative censoring and time-dependent confounding in the setting of treatment-confounder feedback.

Automated summary

This article introduces the concept of inverse probability of treatment weighting (IPTW) and its application in adjusting for measured confounding in observational research, using a clinical example in nephrology. IPTW involves calculating the probability of exposure to the risk factor or intervention of interest based on individual characteristics (propensity score), and then calculating weights as the inverse of the propensity score. Application of these weights creates a pseudopopulation with balanced confounders across exposed and unexposed groups in studies, and it can also be applied in longitudinal studies to address informative censoring and time-dependent confounding.