 Treatment Choice With Trial Data: Statistical Decision Theory Should Supplant Hypothesis TestingThe American Statistician, 2019, vol. 73, issue S1, 296-304 Abstract: 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. Date: 2019 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:amstat:v:73:y:2019:i:s1:p:296-304 Ordering information: This journal article can be ordered from DOI: 10.1080/00031305.2018.1513377 Access Statistics for this article The American Statistician is currently edited by Eric Sampson More articles in The American Statistician from Taylor & Francis Journals |
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