DELPHI METHOD- A QUALITATIVE APPROACH FOR QUANTITATIVE RESULTS

Author(s)

Ng J
IQVIA, Singapore, Singapore

BACKGROUND: Delphi method involves gathering expert opinion through a series of progressive and iterative questionnaires to reach consensus. In low-resource setting, researchers may not be able to conduct surveys representative of target population in order to obtain precise estimates of health outcomes. Delphi method has increasingly been used to obtain quantitative data, such as estimating country-specific prevalence and disease-specific costs, probabilities or resource utilization for health economic models. Although results from this method have an equal potential to affect the study quality and validity, it has received proportionally less attention in terms of description in the methodology section. Given the variance in the use of Delphi method, reporting guidelines could help improve reporting of this research, and thereby allow readers to be aware of the accuracy of data and conclusions. APPROACH: We proposed a set of reporting guidelines to communicate quantitative findings derived from this method. These include (1) explaining how the Delphi method is used, (2) stating variables which have to be estimated by the expert panel, (3) providing definition of the variables, (4) specifying references of base values which experts referred to, (5) describing expert panel selection with eligibility criteria and including conflicts of interest, (6) outlining participation and attrition rates for each round, (7) detailing statistical analyses and interpretation in arriving at final agreed values, (8) reporting both quantitative results and textual comments for each round of analysis and (9) appending revised questionnaires. CONCLUSION: We anticipate the implementation of this will promote transparent and accurate reporting of research using Delphi method for obtaining quantitative data.

Conference/Value in Health Info

2018-05, ISPOR 2018, Baltimore, MD, USA

Value in Health, Vol. 21, S1 (May 2018)

Code

PCP29

Topic

Methodological & Statistical Research

Topic Subcategory

Confounding, Selection Bias Correction, Causal Inference

Disease

Multiple Diseases

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