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An application of kernel-based versus one-to-one propensity score matching for a nonexperimental causal study: example from a disease management program evaluation

Listed author(s):
  • Gregory Berg
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    Objective: To discuss and compare kernel-based matching with one-to-one propensity score matching applied to disease management. Data sources: Administrative claims data from a US Medicaid fee for service plan. Study design: Matched two group analyses using both kernel-based matching and one-to-one propensity score matching. This comparison is applied to the estimation of diabetes disease management treatment effects. Principle findings: Kernel-based matching is found to be better than one-to-one propensity score matching when there is no sufficient number of potential controls from which to draw a matched cohort but similar when there is a sufficient number of potential controls. Matching was applied in the context of a diabetes disease management program that showed an increase in management of each person's medical care through the disease management program. Conclusions: The approach provides a methodology for researchers to evaluate healthcare service innovations without a randomized trial design and delineates the requirements for a matched analysis. Matching was applied in the context of a disease management program showing better patient management through the disease management program.

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    Article provided by Taylor & Francis Journals in its journal Applied Economics Letters.

    Volume (Year): 18 (2011)
    Issue (Month): 5 ()
    Pages: 439-447

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    Handle: RePEc:taf:apeclt:v:18:y:2011:i:5:p:439-447
    DOI: 10.1080/13504851003689692
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