Treatment Choice With Trial Data: Statistical Decision Theory Should Supplant Hypothesis Testing

Charles F. Manski*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


A central objective of empirical research on treatment response is to inform treatment choice. Unfortunately, researchers commonly use concepts of statistical inference whose foundations are distant from the problem of treatment choice. It has been particularly common to use hypothesis tests to compare treatments. Wald’s development of statistical decision theory provides a coherent frequentist framework for use of sample data on treatment response to make treatment decisions. A body of recent research applies statistical decision theory to characterize uniformly satisfactory treatment choices, in the sense of maximum loss relative to optimal decisions (also known as maximum regret). This article describes the basic ideas and findings, which provide an appealing practical alternative to use of hypothesis tests. For simplicity, the article focuses on medical treatment with evidence from classical randomized clinical trials. The ideas apply generally, encompassing use of observational data and treatment choice in nonmedical contexts.

Original languageEnglish (US)
Pages (from-to)296-304
Number of pages9
JournalAmerican Statistician
Issue numbersup1
StatePublished - Mar 29 2019


  • Analysis of treatment response
  • Medical decisions
  • Minimax regret
  • Randomized clinical trials

ASJC Scopus subject areas

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


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