An introduction to inverse probability of treatment weighting in observational research

Nicholas C Chesnaye, Vianda S Stel, Giovanni Tripepi, Friedo W Dekker, Edouard L Fu, Carmine Zoccali, Kitty J Jager

doi:10.1093/ckj/sfab158

An introduction to inverse probability of treatment weighting in observational research

Nicholas C Chesnaye

ERA Registry, Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
Vianda S Stel

ERA Registry, Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands
Giovanni Tripepi

CNR-IFC, Center of Clinical Physiology, Clinical Epidemiology of Renal Diseases and Hypertension, Reggio Calabria, Italy
Friedo W Dekker

Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
Edouard L Fu

Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
Carmine Zoccali

CNR-IFC, Clinical Epidemiology of Renal Diseases and Hypertension, Reggio Calabria, Italy
Kitty J Jager

ERA Registry, Department of Medical Informatics, Academic Medical Center, University of Amsterdam, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands

説明

<jats:title>ABSTRACT</jats:title><jats:p>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.</jats:p>