Wednesday, October 26, 2011: 10:45 AM
Columbus Hall C-F (Hyatt Regency Chicago)
(ESP) Applied Health Economics, Services, and Policy Research

Ties Hoomans, PhD1, Justine Seidenfeld, BA1, Anirban Basu, PhD2 and David Owen Meltzer, MD, PhD1, (1)University of Chicago, Chicago, IL, (2)University of Washington, Seattle, WA

Purpose: This study explores how health technology assessment (HTA) and research-funding agencies might effectively and efficiently use value-of-information (VOI) analysis to inform priorities for systematic reviews.

Methods: We reviewed 1) priority setting processes used by 13 international HTA and research-funding agencies, and 2) methods applied in 75 VOI studies from the literature. Following this, we developed an algorithm for deciding about the most effective and efficient approach to analyzing the value of systematic reviews in specific contexts.

Results: Our review revealed that the use of VOI and modeling is rarely applied in prioritizing systematic reviews. We identified conditions under which four alternative VOI approaches may be used for this purpose. The construction of “maximal” models of a broad disease process – often including multiple interventions to screen, diagnose and treat patients - can be worthwhile for prioritizing reviews when topics cluster in particular domains, such as diabetes, heart disease, and prostate cancer. VOI analyses commonly involve full modeling of a disease and its treatment but such exercises are generally too complex and too costly for prioritizing systematic reviews. Modeling can be minimized when existing comparative effectiveness studies provide appropriate data on comprehensive measures of health outcomes. Another approach is “conceptual VOI”, which uses information about the multiplicative elements of VOI, such as the burden of illness, uncertainty in treatment benefits, and the expected clinical use or implementation of research evidence, to provide informative bounds on the value of systematic reviews. Our algorithm describes a multi-stage process for deciding about the analysis of VOI in reviewing evidence. This process begins with clustering review topics and decisions about the use of maximal models, followed by conceptual VOI and then minimal modeling approaches. Although full models may aid in the planning and design of future research and HTA, we find limited conditions for the effective and efficient use of this traditional approach in prioritizing systematic reviews. 

Conclusion: An algorithmic approach that includes maximal modeling, full modeling, minimal modeling and conceptual VOI analysis may be useful in informing priorities for systematic reviews. In future work, we will illustrate the application of the algorithm for prioritizing review topics nominated to the Agency for Healthcare Research and Quality (AHRQ) Evidence-based Practice Centers.