Using Propensity Scores Subclassification to Estimate Effects of Longitudinal Treatments

An Example Using a New Diabetes Medication

Datos Bibliográficos

ID9103099
AutoresJodi B Segal (0000-0003-3978-9662, Johns Hopkins Medicine, autor de correspondencia), Michael Griswold (0000-0001-8106-5516, Johns Hopkins University), Aristide Achy-Brou, Aristide Achy‐Brou (Johns Hopkins University), Robert Herbert, Robert J Herbert (Johns Hopkins University), Eric B Bass (0000-0001-9106-527X, Johns Hopkins University, autor de correspondencia), Sydney M Dy (0000-0001-6530-7415, Johns Hopkins Medicine, autor de correspondencia), Anne E Millman (Johns Hopkins University), Albert W Wu (0000-0001-6189-7120, Johns Hopkins University, autor de correspondencia), Constantine Frangakis (0000-0002-6932-0614, Johns Hopkins University), Constantine E Frangakis
Año2007
Volumen45
Número10
PáginasS149-S157
Fecha de publicación2007-10-01
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaMedical Care (JOURNAL)
Identificadores de la revistaISSN: 0025-7079 • E-ISSN: 1537-1948
EditorialOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0b013e31804ffd6d
PMID17909374
OpenAlexW1988339318
IdiomaEN
Citas recibidas2
Referencias citadas18

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

Cohort study · Confidence interval · Diabetes mellitus · Environmental health · Exenatide · Logistic regression · Longitudinal study · Medical prescription · Observational study · Odds · Odds ratio · Population · Propensity score matching · Retrospective cohort study · Type 2 diabetes · Advanced Causal Inference Techniques · Emergency Medicine · Health Systems, Economic Evaluations, Quality of Life · Internal Medicine · Medication Adherence and Compliance · Medicine · Pharmacology