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

• Published in: Clinical epidemiology Vol. 14; pp. 1053 - 1064
• Main Authors: Staus, Paulina, von Cube, Maja, Hazard, Derek, Doerken, Sam, Ershova, Ksenia, Balmford, James, Wolkewitz, Martin
• Format: Journal Article
• Language: English
• Published: New Zealand Dove Medical Press Limited 01.01.2022; Taylor & Francis Ltd; Dove; Dove Medical Press
• Subjects: Bias; Clinical medicine; Cohort analysis; cohort studies; Cross-sectional studies; Epidemiology; Generalized linear models; Health aspects; hospital infection; Infection; Information management; Intensive care; intensive care units; Length of stay; Medical research; Medicine, Experimental; Morbidity; Mortality; Nosocomial infections; Original Research; Patients; Prevalence studies (Epidemiology); proportional hazards models; risk assessment; Risk factors; selection bias; Russia; selection bias; cohort studies; risk assessment; hospital infection; proportional hazards models; intensive care units
• ISSN: 1179-1349; 1179-1349
• DOI: 10.2147/CLEP.S357494

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. 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. 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. 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.