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Updated: Aug 20, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Overestimation of Relative Risk and Prevalence Ratio: Misuse of Logistic Modeling
Charalambos Gnardellis1, Venetia Notara2, Maria Papadakaki3
1Department of Fisheries and Aquaculture, School of Agricultural Sciences, University of Patras, 26504 Patra, Greece.
Abstract:
The extensive use of logistic regression models in analytical epidemiology as well as in randomized clinical trials, often creates inflated estimates of the relative risk (RR). Particularly, in cases where a binary outcome has a high or moderate incidence in the studied population (>10%), the bias in assessing the relative risk may be very high. Meta-analysis studies have estimated that about 40% of the relative risk estimates in prospective investigations, through binary logistic models, lead to extensive bias of the population parameters. The problem of risk inflation also appears in cross-sectional studies with binary outcomes, where the parameter of interest is the prevalence ratio. As an alternative to the use of logistic regression models in both longitudinal and cross-sectional studies, the modified Poisson regression model is proposed.
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