Propensity score trimming mitigates bias due to covariate measurement error in inverse probability of treatment weighted analyses: A plasmode simulation. (23rd February 2021)

• Record Type: Journal Article
• Authors: Conover, Mitchell M.; Rothman, Kenneth J.; Stürmer, Til; Ellis, Alan R.; Poole, Charles; Jonsson Funk, Michele
• Is Part Of: Statistics in medicine. Volume 40:Number 9(2021)
• Page Start: 2101
• Page End: 2112
• Publication Date: 2021-02-23
• Subjects: bias -- classification -- confounding factors -- Monte Carlo method -- propensity score; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8887
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : Background: Inverse probability of treatment weighting (IPTW) may be biased by influential observations, which can occur from misclassification of strong exposure predictors. Methods: We evaluated bias and precision of IPTW estimators in the presence of a misclassified confounder and assessed the effect of propensity score (PS) trimming. We generated 1000 plasmode cohorts of size N = 10 000, sampled with replacement from 6063 NHANES respondents (1999‐2014) age 40 to 79 with labs and no statin use. We simulated statin exposure as a function of demographics and CVD risk factors; and outcomes as a function of 10‐year CVD risk score and statin exposure (rate ratio [RR] = 0.5). For 5% of the people in selected populations (eg, all patients, exposed, those with outcomes), we randomly misclassified a confounder that strongly predicted exposure. We fit PS models and estimated RRs using IPTW and 1:1 PS matching, with and without asymmetric trimming. Results: IPTW bias was substantial when misclassification was differential by outcome (RR range: 0.38‐0.63) and otherwise minimal (RR range: 0.51‐0.53). However, trimming reduced bias for IPTW, nearly eliminating it at 5% trimming (RR range: 0.49‐0.52). In one scenario, when the confounder was misclassified for 5% of those with outcomes (0.3% of cohort), untrimmed IPTW was more biased and less precise (RR = 0.37 [SE(logRR) = 0.21]) than matching (RR = 0.50 [SE(logRR) = 0.13]). After 1% trimming, IPTW estimates were unbiased and more precise (RR = 0.49 [SE(logRR) = 0.12]) than matching (RR = 0.51 [SE(logRR) = 0.14]). Conclusions: Differential misclassification of a strong predictor of exposure resulted in biased and imprecise IPTW estimates. Asymmetric trimming reduced bias, with more precise estimates than matching.