Using propensity scores subclassification to estimate effects of longitudinal treatments: an example using a new diabetes medication

Published: October 1, 2007
Category: Bibliography > Papers
Authors: Achy-Brou A, Bass EB, Dy SM, Frangakis CE, Griswold M, Herbert R, Millman AE, Segal JB, Wu AW
Countries: United States
Language: null
Types: Care Management
Settings: Academic

Med Care 45:S149.

Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

BACKGROUND: When using observational data to compare the effectiveness of medications, it is essential to account parsimoniously for patients’ longitudinal characteristics that lead to changes in treatments over time.

OBJECTIVES: We developed a method of estimating effects of longitudinal treatments that uses subclassification on a longitudinal propensity score to compare outcomes between a new drug (exenatide) and established drugs (insulin and oral medications) assuming knowledge of the variables influencing the treatment assignment. RESEARCH DESIGN/SUBJECTS: We assembled a retrospective cohort of patients with diabetes mellitus from among a population of employed persons and their dependents.

METHODS: The data, from i3Innovus, includes claims for utilization of medications and inpatient and outpatient services. We estimated a model for the longitudinal propensity score process of receiving a medication of interest. We used our methods to estimate the effect of the new versus established drugs on total health care charges and hospitalization.

RESULTS: We had data from 131,714 patients with diabetes filling prescriptions from June through December 2005. Within propensity score quintiles, the explanatory covariates were well-balanced. We estimated that the total health care charges per month that would have occurred if all patients had been continually on exenatide compared with if the same patients had been on insulin were minimally higher, with a mean monthly difference of $397 [95% confidence interval (CI), $218-$1054]. The odds of hospitalization were also comparable (relative odds, 1.02; 95% CI, 0.33-1.98).

CONCLUSIONS: We used subclassification of a longitudinal propensity score for reducing the multidimensionality of observational data, including treatments changing over time. In our example, evaluating a new diabetes drug, there were no demonstrable differences in outcomes relative to existing therapies.

PMID: 17909374

Medication,High-Impact Chronic Conditions,Practice Patterns Comparison,Outcome Measures,United States,Adolescent,Adult,Diabetes Mellitus/drug therapy,Diabetes Mellitus/epidemiology,Drug Prescriptions/statistics & numerical data,Hypoglycemic Agents/therapeutic use,Logistic Models,Middle Aged,Patient Compliance,Peptides/therapeutic use,Pharmacoepidemiology/methods,Retrospective Studies,Treatment Outcome,Venoms/therapeutic use

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