An application of kernel-based versus one-to-one propensity score matching for a nonexperimental causal study: example from a disease management program evaluation
AbstractObjective: 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Applied Economics Letters.
Volume (Year): 18 (2011)
Issue (Month): 5 ()
Contact details of provider:
Web page: http://www.tandfonline.com/RAEL20
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty).
If references are entirely missing, you can add them using this form.