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. 2023 Nov 1;34(6):879-887.
doi: 10.1097/EDE.0000000000001649. Epub 2023 Sep 26.

Comparative Effectiveness of Dynamic Treatment Strategies for Medication Use and Dosage: Emulating a Target Trial Using Observational Data

Kate Birnie  1 , Charles Tomson  2 , Fergus J Caskey  1   3 , Yoav Ben-Shlomo  1 , Dorothea Nitsch  4   5 , Anna Casula  6 , Eleanor J Murray  7 , Jonathan A C Sterne  1   8   9
Affiliations

Comparative Effectiveness of Dynamic Treatment Strategies for Medication Use and Dosage: Emulating a Target Trial Using Observational Data

Kate Birnie et al. Epidemiology. .

Abstract

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.

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Conflict of interest statement

Conflicts of interest: There are no conflicts of interest

Figures

Figure 1
Figure 1
Distribution of weekly darbepoetin dose (mcg/week) (left panel, A), probability of zero darbepoetin dose by previous hemoglobin level (g/L) (middle panel, B), and darbepoetin dose (mcg/week) by previous hemoglobin level (g/L) (right panel, C).
Figure 2
Figure 2. Dose change decisions protocol. Hb=hemoglobin.
Figure 3
Figure 3. Monthly treatment weight for the low hemoglobin strategy by previous darbepoetin dose (mcg/week), for Methods A-D.
Figure 4
Figure 4. The process followed to arrive at the final estimate of the causal effect of the treatment strategies compared.
See this image and copyright information in PMC

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