Inverse Probability Weighting Enhances Absolute Risk Estimation in Three Common Study Designs of Nosocomial Infections

Abstract

Paulina Staus,1 Maja von Cube,1,* Derek Hazard,1,* Sam Doerken,1 Ksenia Ershova,2 James Balmford1 ,† Martin Wolkewitz1 1Institute of Medical Biometry and Statistics, Division Methods in Clinical Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany; 2Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA, USA*These authors contributed equally to this work†James Balmford passed away in March 2020Correspondence: Paulina Staus, Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany, Email [email protected]: When studying nosocomial infections, resource-efficient sampling designs such as nested case-control, case-cohort, and point prevalence studies are preferred. However, standard analyses of these study designs can introduce selection bias, especially when interested in absolute rates and risks. Moreover, nosocomial infection studies are often subject to competing risks. We aim to demonstrate in this tutorial how to address these challenges for all three study designs using simple weighting techniques.Patients and Methods: We discuss the study designs and explain how inverse probability weights (IPW) are applied to obtain unbiased hazard ratios (HR), odds ratios and cumulative incidences. We illustrate these methods in a multi-state framework using a dataset from a nosocomial infections study (n = 2286) in Moscow, Russia.Results: Including IPW in the analysis corrects the unweighted naïve analyses and enables the estimation of absolute risks. Resulting estimates are close to the full cohort estimates using substantially smaller numbers of patients.Conclusion: IPW is a powerful tool to account for the unequal selection of controls in case-cohort, nested case-control and point prevalence studies. Findings can be generalized to the full population and absolute risks can be estimated. When applied to a multi-state model, competing risks are also taken into account.Graphical Abstract: Keywords: selection bias, hospital infection, intensive care units, proportional hazards models, risk assessment, cohort studies

Keywords

• selection bias
• hospital infection
• intensive care units
• proportional hazards models
• risk assessment
• cohort studies