Electronic versions

Documents

DOI

  • Samer G. Karam
    McMaster University, Hamilton
  • Yuan Zang
    McMaster University, Hamilton
  • Hector Pardo-Hernandez
    Sant Pau Biomedical Research Institute
  • Uwe Siebert
    Harvard Medical School, Boston
  • Laura Koopman
    National Health Care Institute, Diemen
  • Jane Noyes
  • Jean-Eric Tarride
    McMaster University, Hamilton
  • Adrienne Stevens
    Public Health Agency of Canade
  • Vivian Welch
    University of Ottawa
  • Suleika Saz Parkinson
    European Commission, Joint Research Centre (JRC), Ispra, Italy
  • Brendalynn Ens
    Canadian Agency for Drugs and Technology in Health
  • Tahira Devji
    University of Toronto
  • Feng Xie
    McMaster University, Hamilton
  • Glen Hazlewood
    University of Calgary
  • Lawrence Mbuagbaw
    McMaster University, Hamilton
  • Pablo Alonso Coello
    McMaster University, Hamilton
  • Jan L. Brozek
    Sant Pau Biomedical Research Institute
  • Holger J. Schünemann
    Humanitas University, Milan

People’s values are an important driver in healthcare decision making. The certainty of an intervention’s effect on benefits and harms relies on two factors: the certainty in the measured effect on an outcome in terms of risk difference and the certainty in its value, also known as utility or importance. The GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) working group has proposed a set of questions to assess the risk of bias in a body of evidence from studies investigating how people value outcomes. However, these questions do not address risk of bias in individual studies that, similar to risk-of-bias tools for other research studies, is required to evaluate such evidence. Thus, the Risk of Bias in studies of Values and Utilities (ROBVALU) tool was developed. ROBVALU has good psychometric properties and will be useful when assessing individual studies in measuring values, utilities, or the importance of outcomes. As such, ROBVALU can be used to assess risk of bias in studies included in systematic reviews and health guidelines. It also can support health research assessments, where the risk of bias of input variables determines the certainty in model outputs. These assessments include, for example, decision analysis and cost utility or cost effectiveness analysis for health technology assessment, health policy, and reimbursement decision making.

Original languageEnglish
Article numbere079890
JournalBMJ
Volume385
DOIs
Publication statusPublished - 12 Jun 2024

Total downloads

No data available
View graph of relations