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Lookup NU author(s): Dr Charlie Tomson
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© 2023 Lippincott Williams and Wilkins. All rights reserved. Background: Availability of detailed data from electronic health records (EHRs) has increased the potential to examine the comparative effectiveness of dynamic treatment strategies using observational data. Inverse probability (IP) weighting of dynamic marginal structural models can control for time-varying confounders. However, IP weights for continuous treatments may be sensitive to model choice. Methods: We describe a target trial comparing strategies for treating anemia with darbepoetin in hemodialysis patients using EHR data from the UK Renal Registry 2004 to 2016. Patients received a specified dose (microgram/week) or did not receive darbepoetin. We compared 4 methods for modeling time-varying treatment: (A) logistic regression for zero dose, standard linear regression for log dose; (B) logistic regression for zero dose, heteroscedastic linear regression for log dose; (C) logistic regression for zero dose, heteroscedastic linear regression for log dose, multinomial regression for patients who recently received very low or high doses; and (D) ordinal logistic regression. Results: For this dataset, method (C) was the only approach that provided a robust estimate of the mortality hazard ratio (HR), with less-extreme weights in a fully weighted analysis and no substantial change of the HR point estimate after weight truncation. After truncating IP weights at the 95th percentile, estimates were similar across the methods. Conclusions: EHR data can be used to emulate target trials estimating the comparative effectiveness of dynamic strategies adjusting treatment to evolving patient characteristics. However, model checking, monitoring of large weights, and adaptation of model strategies to account for these is essential if an aspect of treatment is continuous.
Author(s): Birnie K, Tomson C, Caskey FJ, Ben-Shlomo Y, Nitsch D, Casula A, Murray EJ, Sterne JAC
Publication type: Article
Publication status: Published
Journal: Epidemiology
Year: 2023
Volume: 34
Issue: 6
Pages: 879-887
Print publication date: 01/11/2023
Acceptance date: 02/04/2018
ISSN (print): 1044-3983
ISSN (electronic): 1531-5487
Publisher: Lippincott Williams and Wilkins
URL: https://doi.org/10.1097/EDE.0000000000001649
DOI: 10.1097/EDE.0000000000001649
PubMed id: 37757876
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