Literature DB >> 19926253

A valid and reliable belief elicitation method for Bayesian priors.

Sindhu R Johnson1, George A Tomlinson, Gillian A Hawker, John T Granton, Haddas A Grosbein, Brian M Feldman.   

Abstract

OBJECTIVE: Bayesian inference has the advantage of formally incorporating prior beliefs about the effect of an intervention into analyses of treatment effect through the use of prior probability distributions or "priors." Multiple methods to elicit beliefs from experts for inclusion in a Bayesian study have been used; however, the measurement properties of these methods have been infrequently evaluated. The objectives of this study were to evaluate the feasibility, validity, and reliability of a belief elicitation method for Bayesian priors. STUDY DESIGN AND
SETTING: A single-center, cross-sectional study using a sample of academic specialists who treat pulmonary hypertension patients was conducted to test the feasibility, face and construct validity, and reliability of a belief elicitation method. Using this method, participants expressed the probability of 3-year survival with and without warfarin. Applying adhesive dots or "chips," each representing 5% probability, in "bins" on a line, participants expressed their uncertainty and weight of belief about the effect of warfarin on 3-year survival.
RESULTS: Of the 12 participants, 11 (92%) reported that the belief elicitation method had face validity, 10 (83%) found the questions clear, and 11 (92%) found the response option easy to use. The median time to completion was 10 minutes (5-15 minutes). Internal validity testing found moderate agreement (weighted kappa=0.54-0.57). The intraclass correlation coefficient for test-retest reliability was 0.93.
CONCLUSION: This method of belief elicitation for Bayesian priors is feasible, valid, and reliable. It can be considered for application in Bayesian clinical studies. Copyright 2010 Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Substances:

Year:  2009        PMID: 19926253     DOI: 10.1016/j.jclinepi.2009.08.005

Source DB:  PubMed          Journal:  J Clin Epidemiol        ISSN: 0895-4356            Impact factor:   6.437


  21 in total

1.  Defining the appropriateness and inappropriateness of antibiotic prescribing in primary care.

Authors:  David R M Smith; F Christiaan K Dolk; Koen B Pouwels; Morag Christie; Julie V Robotham; Timo Smieszek
Journal:  J Antimicrob Chemother       Date:  2018-02-01       Impact factor: 5.790

2.  Chiropractic spinal manipulation and the risk for acute lumbar disc herniation: a belief elicitation study.

Authors:  Cesar A Hincapié; J David Cassidy; Pierre Côté; Y Raja Rampersaud; Alejandro R Jadad; George A Tomlinson
Journal:  Eur Spine J       Date:  2017-09-18       Impact factor: 3.134

Review 3.  Improving postapproval drug safety surveillance: getting better information sooner.

Authors:  Sean Hennessy; Brian L Strom
Journal:  Annu Rev Pharmacol Toxicol       Date:  2014-09-25       Impact factor: 13.820

4.  Microbiome-pathogen interactions drive epidemiological dynamics of antibiotic resistance: A modeling study applied to nosocomial pathogen control.

Authors:  Laura Temime; Lulla Opatowski; David Rm Smith
Journal:  Elife       Date:  2021-09-14       Impact factor: 8.140

5.  Expert prior elicitation and Bayesian analysis of the Mycotic Ulcer Treatment Trial I.

Authors:  Catherine Q Sun; N Venkatesh Prajna; Tiruvengada Krishnan; Jeena Mascarenhas; Revathi Rajaraman; Muthiah Srinivasan; Anita Raghavan; Kieran S O'Brien; Kathryn J Ray; Stephen D McLeod; Travis C Porco; Nisha R Acharya; Thomas M Lietman
Journal:  Invest Ophthalmol Vis Sci       Date:  2013-06-14       Impact factor: 4.799

Review 6.  Bayesian Approaches for Confirmatory Trials in Rare Diseases: Opportunities and Challenges.

Authors:  Moreno Ursino; Nigel Stallard
Journal:  Int J Environ Res Public Health       Date:  2021-01-24       Impact factor: 3.390

7.  Effectiveness of initial methotrexate-based treatment approaches in early rheumatoid arthritis: an elicitation of rheumatologists' beliefs.

Authors:  Gyanendra Pokharel; Rob Deardon; Sindhu R Johnson; George Tomlinson; Pauline M Hull; Glen S Hazlewood
Journal:  Rheumatology (Oxford)       Date:  2021-08-02       Impact factor: 7.580

8.  How does reviewing the evidence change veterinary surgeons' beliefs regarding the treatment of ovine footrot? A quantitative and qualitative study.

Authors:  Helen M Higgins; Laura E Green; Martin J Green; Jasmeet Kaler
Journal:  PLoS One       Date:  2013-05-16       Impact factor: 3.240

9.  From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support.

Authors:  Anthony Costa Constantinou; Norman Fenton; William Marsh; Lukasz Radlinski
Journal:  Artif Intell Med       Date:  2016-01-16       Impact factor: 5.326

10.  Integrating Expert Knowledge with Data in Bayesian Networks: Preserving Data-Driven Expectations when the Expert Variables Remain Unobserved.

Authors:  Anthony Costa Constantinou; Norman Fenton; Martin Neil
Journal:  Expert Syst Appl       Date:  2016-03-18       Impact factor: 6.954

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.