Eliminating Aggregation Bias When Estimating Treatment Effects on Combined Outcomes with Applications to Quality of Life Assessment

29 Pages Posted: 11 Dec 2014

See all articles by Ian M. McCarthy

Ian M. McCarthy

Emory University - Department of Economics

Date Written: November 4, 2014

Abstract

Researchers are often interested in combined measures such as overall ratings, indices of physical or mental health, or health-related quality-of-life (HRQoL) outcomes. Such measures are typically composed of two or more underlying discrete variables. I show that estimating the effect of a treatment on the combined measure is biased with non-random treatment selection. I provide a solution to this problem by adopting an alternative estimator that first estimates treatment effects on the underlying variables and then combines these effects into an overall effect on the combined outcome of interest.

Keywords: Program evaluation, treatment effects, quality of life, cost-effectiveness, comparative effectiveness

JEL Classification: C18, C21, I10

Suggested Citation

McCarthy, Ian M., Eliminating Aggregation Bias When Estimating Treatment Effects on Combined Outcomes with Applications to Quality of Life Assessment (November 4, 2014). Available at SSRN: https://ssrn.com/abstract=2536521 or http://dx.doi.org/10.2139/ssrn.2536521

Ian M. McCarthy (Contact Author)

Emory University - Department of Economics ( email )

1602 Fishburne Drive
Atlanta, GA 30322
United States

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