Work

Comparing performance of three propensity score weighting methods for continuous exposures in nutritional epidemiology

Year: 2026

Type: article

Abstract: Inverse probability of treatment weighting (IPTW) targets entire study population and is frequently used to adjust for confounding. When exposures are continuous, such as nutrient intake, we need to deal with the problem of large weights caused by a lack of positivity. Generalized overlap weighting (GOW) and generalized matching weighting (GMW) target the populations whose generalized propensity score distributions most overlap among the compared groups. Considering that these two weighting methods mitigate the influence of a lack of positivity, they may estimate exposure effects with less bias and higher precision. The primary and secondary objectives were to compare the performance of IPTW, GOW and GMW in evaluating the associations between nutrient intake and diabetes complications and to determine the favorable number of bins based on performance metrics when the quantile binning approach was applied to nutrient intake. We reanalyzed a dataset from a nutritional epidemiologic cohort study, Japan Diabetes Complications Study (JDCS), including 1,414 patients with type 2 diabetes. The generalized propensity score was estimated using quantile binning approach and used for each weighting method. We assessed covariate balance, weight variability, and the precision of the estimated risk ratios for complications among patients with type 2 diabetes. The results suggested that relatively small number of bins, 2 or 5 bins, might be favorable in quantile binning approach when considering covariate balance and weight variability, making quantile binning approach feasible for applied research. The performance of three methods was similar, with sufficient overlap in the generalized propensity score distributions. However, with reduced overlap, GOW and GMW showed a better covariate balance with 5 bins, and GOW had the smallest weight variability. Our findings suggested that with reduced overlap, GOW might be useful to adjust for confounding in nutritional epidemiology.

Source: BMC Medical Research Methodology

DOI: 10.1186/s12874-026-02971-5

Authors: Yuriko Muramatsu, Tosiya Sato, Hirohito Sone, Shiro Tanaka

Institutions: Kyoto University, Shiga University, The Institute of Statistical Mathematics, Niigata University, Kyushu University Hospital

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,090