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Related Experiment Videos

Evaluating disease management programme effectiveness: an introduction to instrumental variables.

Ariel Linden1, John L Adams

  • 1Linden Consulting Group, Portland, OR 97124, USA. alinden@lindenconsulting.org

Journal of Evaluation in Clinical Practice
|April 4, 2006
PubMed
Summary

Instrumental variables (IVs) offer unbiased treatment effect estimates for disease management (DM) programs. IV estimation improved accuracy for hospital days, highlighting its utility over ordinary least squares (OLS) when hidden bias is present.

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Area of Science:

  • Health Economics
  • Biostatistics
  • Epidemiology

Background:

  • Evaluating disease management (DM) program effectiveness requires unbiased estimation of treatment effects.
  • Ordinary least squares (OLS) models can be susceptible to confounding from unobserved factors (hidden bias).

Purpose of the Study:

  • To introduce and demonstrate the utility of instrumental variables (IVs) for unbiased estimation of DM program effectiveness.
  • To compare IV estimates with OLS estimates for key diabetes outcomes.

Main Methods:

  • Developed a model using zip codes as an instrumental variable (IV).
  • Evaluated three diabetes outcomes: annual costs, emergency department (ED) visits, and hospital days.
  • Compared results from IV estimation with traditional OLS regression.

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Main Results:

  • Both OLS and IV methods found significant treatment effects for annual diabetes costs.
  • Neither OLS nor IV models showed significant treatment effects for ED visits.
  • IV estimation revealed a significant treatment effect for hospital days, which OLS did not detect.

Conclusions:

  • Instrumental variables provide a valuable method for obtaining unbiased treatment effect estimates in DM program evaluations.
  • IV estimation is particularly useful when OLS is compromised by hidden bias, as demonstrated for hospital days.