Inequality in treatment benefits: Can we determine if a new treatment benefits the many or the few?

Emily J. Huang, Ethan X. Fang, Daniel F. Hanley, Michael Rosenblum

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

In many randomized controlled trials, the primary analysis focuses on the average treatment effect and does not address whether treatment benefits are widespread or limited to a select few. This problem affects many disease areas, since it stems from how randomized trials, often the gold standard for evaluating treatments, are designed and analyzed. Our goal is to learn about the fraction who benefit from a new treatment using randomized trial data. We consider the case where the outcome is ordinal, with binary outcomes as a special case. In general, the fraction who benefit is non-identifiable, and the best that can be obtained are sharp lower and upper bounds. Our contributions include (i) proving the plug-in estimator of the bounds can be inconsistent if support restrictions are made on the joint distribution of the potential outcomes; (ii) developing the first consistent estimator for this case; and (iii) applying this estimator to a randomized trial of a medical treatment to determine whether the estimates can be informative. Our estimator is computed using linear programming, allowing fast implementation. R code is provided.

Original languageEnglish (US)
Pages (from-to)308-324
Number of pages17
JournalBiostatistics
Volume18
Issue number2
DOIs
StatePublished - Apr 1 2017

Fingerprint

Linear Programming
Randomized Trial
Randomized Controlled Trials
Plug-in Estimator
Average Treatment Effect
Potential Outcomes
Randomized Controlled Trial
Estimator
Binary Outcomes
Consistent Estimator
Joint Distribution
Gold
Inconsistent
Linear programming
Upper and Lower Bounds
Restriction
Estimate
Randomized trial

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Medicine(all)
  • Statistics, Probability and Uncertainty

Cite this

Huang, Emily J. ; Fang, Ethan X. ; Hanley, Daniel F. ; Rosenblum, Michael. / Inequality in treatment benefits : Can we determine if a new treatment benefits the many or the few?. In: Biostatistics. 2017 ; Vol. 18, No. 2. pp. 308-324.
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Inequality in treatment benefits : Can we determine if a new treatment benefits the many or the few? / Huang, Emily J.; Fang, Ethan X.; Hanley, Daniel F.; Rosenblum, Michael.

In: Biostatistics, Vol. 18, No. 2, 01.04.2017, p. 308-324.

Research output: Contribution to journalArticle

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