Estimation of Multi-Treatment Effects from Observational Data with Application to Diabetes Mellitus [Methods Study], 2014-2021 (ICPSR 39576)
Version Date: Nov 24, 2025 View help for published
Principal Investigator(s): View help for Principal Investigator(s)
Roee Gutman, Brown University
https://doi.org/10.3886/ICPSR39576.v1
Version V1
Summary View help for Summary
Comparative effectiveness research compares two or more treatments to see which one works best for which patients. But patient traits, such as age or income, may affect patients' treatment choices. These traits may also affect patients' responses to treatments. As a result, researchers may have trouble telling whether a patient's traits, the treatment, or a mix of the two affected how well a treatment worked.
Statistical methods called matching methods can help address this problem when researchers use patient data to compare the effects of treatments. Matching methods help researchers find data from patients who had similar traits such as age or race and received different treatments. Because the patients are similar except for the treatment they receive, the differences in patients' health can more likely be credited to the treatment. Existing methods work well for comparing up to two treatments. But they may not work with three or more treatments.
In this study, the research team created two new matching methods to compare the effects of three or more treatments. The team then analyzed the new methods under different conditions to see how well each worked."
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Study Purpose View help for Study Purpose
To develop two new matching methods and compare their performance with that of existing methods. The first objective aimed at development, testing, and guidance for estimation of effects of multiple treatment options that are either ordinal (eg, greater than or equal to 3 possible doses of a drug) or categorical (eg, greater than or equal to 3 possible drugs). Specifically, we will concentrate on different matching procedures. In the second objective, we use the developed methods to estimate the effects of multiple add-on, noninsulin antihyperglycemic treatments on major adverse cardiovascular events (MACE) or death.
Study Design View help for Study Design
The research team developed two new methods, basic matching (BM) and vector matching (VM), with variations that included
- Choice of distance measure, such as the Mahalanobis distance, which tells researchers how similar patients are to each other
- With or without caliper, a predefined threshold for distance when matching patients
- With and without replacement of matched observations.
The research team first compared VM with three existing methods, GPS, SBC, and CRPM. They then compared BM and VM by conducting simulations with 3, 5, and 10 treatments. In each simulation, one treatment served as the reference treatment. The team looked at two outcomes for all comparisons:
- The proportion of patients from the eligible population who received the reference treatment and were included in the final matched set
- Standardized pairwise bias, which evaluates the quality of matches on each characteristic.
Data Source View help for Data Source
Simulated data for comparing the treatment effect of three or more treatments; UK Clinical Practice Research Datalink for empirical application/
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