Making Narrative Statements to Describe Treatment Effects

M. Hassan Murad, Celia Fiordalisi, Jennifer Pillay, Timothy J. Wilt, Elizabeth O’Connor, Leila Kahwati, Adrian V. Hernandez, Carolyn M. Rutter, Roger Chou, Ethan M. Balk, Dale W. Steele, Ian J. Saldanha, Orestis A. Panagiotou, Stephanie Chang, Martha Gerrity

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Accurately describing treatment effects using plain language and narrative statements is a critical step in communicating research findings to end users. However, the process of developing these narratives has not been historically guided by a specific framework. The Agency for Healthcare Research and Quality Evidence-based Practice Center Program developed guidance for narrative summaries of treatment effects that identifies five constructs. We explicitly identify these constructs to facilitate developing narrative statements: (1) direction of effect, (2) size of effect, (3) clinical importance, (4) statistical significance, and (5) strength or certainty of evidence. These constructs clearly overlap. It may not always be feasible to address all five constructs. Based on context and intended audience, investigators can determine which constructs will be most important to address in narrative statements.

Original languageEnglish (US)
Pages (from-to)196-199
Number of pages4
JournalJournal of general internal medicine
Volume36
Issue number1
DOIs
StatePublished - Jan 2021

Bibliographical note

Funding Information:
This work was funded by the Agency for Healthcare Research and Quality. Contract Numbers: HHSA 290 2015 00013I, HHSA 290 2017 00003C, HHSA 290 2015 00001I, HHSA 290 2015 00008I, HHSA 290 2015 00007I, HHSA 290 2015 00012I, HHSA 290 2015 00011I, HHSA 290 2015 00010I, HHSA 290 2015 00009I, HHSA 290 2015 00002, and 1R03HS025840-01A1. Acknowledgments

Publisher Copyright:
© 2020, Society of General Internal Medicine.

Keywords

  • dissemination
  • evidence-based medicine
  • plain language summary
  • statistics and numerical data
  • systematic reviews

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