Estimating the comparative effectiveness of dynamic treatment strategies for medication use and dosage: application of marginal structural models to emulate a hypothetical target trial using observational data

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 the choice of model. Methods: We describe a target trial comparing strategies for treating anaemia with darbepoetin, in haemodialysis patients, using EHR data from the UK Renal Registry 2004-2016. Patients received a specified dose (mcg/week), or did not receive darbepoetin. We compare four methods to model time-varying treatment: (A) Logistic regression for zero dose and standard linear regression for log dose; (B) Logistic regression for zero dose and heteroscedastic linear regression for log dose; (C) Logistic regression for zero dose, heteroscedastic linear regression for log dose and multinomial regression for patients who recently received very low or high doses; (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. However, after truncating IP weights at the 95th percentile, estimates were similar across methods. Conclusions: EHR data can be used to emulate target trials to estimate the comparative effectiveness of dynamic strategies that are sustained over time and adjust treatment to evolving patient characteristics. However, careful model checking, monitoring of large model weights, and adaptation of model strategies to account for these, is essential if an aspect of treatment is continuous.