Behavioral Theories That Have Influenced the Way Health State Preferences Are Elicited and Interpreted: A Bibliometric Mapping Analysis of the Time Trade-Off Method With VOSviewer Visualization

Abstract

AimThe aim of this paper is to develop an understanding of how behavioral theories have influenced the way preferences for health-related quality of life are elicited and interpreted. We focus on the Time Trade-off (TTO) method given it represents the quality-adjusted life-year (QALY) concept—that survival in less-than-full health can be deemed equivalent to a shorter survival in full health. To our knowledge this is the first review using a combination of systematic scoping review, bibliometrics and VOSviewer visualization to map the development of ideas in health economics.MethodsA priori, we selected three behavioral theories to explore within our review, referred to here as Expected Utility Theory, Non-Expected Utility Theory and Probabilistic Choice Theory. A fourth topic, Order Effects, is defined broadly to encompass behavioral theories around timing/sequence of events. For the main search, Scopus was used to identify literature that had (a) elicited TTO values and/or (b) contributed to the way TTO values were elicited and interpreted, from inception to July 2021. Papers that focused on the latter category were given the label “behavioral” and underwent additional analyses. A two stage-screening was applied to assess eligibility. Co-citation, co-authorship and co-occurrence of keywords was used to chart the development of TTO over time.ResultsA total of 1,727 records were retrieved from Scopus and were supplemented by an additional 188 papers. There were 856 applied and 280 behavioral papers included in the final corpus, with the behavioral set split equally into four sets of 70 papers to chart the development of keywords over time: (1) 1972–1999; (2) 2000–2010, (3) 2010–2015 and (4) 2015–2021.DiscussionThe keyword analysis suggested that whilst some ideas transition quickly from economic theory to the TTO literature, such as the impact of Order Effects, others take longer to be assimilated, for example Non-Expected Utility models or failure of constant discounting. It is therefore important that researchers within health economics work more closely with those in mainstream economics and keep abreast of the wider economics and behavioral sciences to expedite the uptake of new and relevant ideas.

Bibliometric and visualization analysis of risk management in the doctor–patient relationship: A systematic quantitative literature review

“…This deepened our understanding of relationships in the literature compared to using one form of mapping alone. [33] 2.2.2. Bibliometric analysis and visualization.…”

A systematic literature review of the stereotype content model in the fields of psychology and marketing: main themes examined in the literature and an agenda for future research in marketing

“…Regarding the threshold chosen to perform this analysis, there is some judgment needed in setting the threshold for the minimum number of citations for visualization. A low threshold allows more items to be analyzed and displayed on the map, but also may create overly complex images ( Henstock et al, 2022 ). Following Henstock et al (2022) , whilst VOSviewer applies techniques to optimize the visualization so that labels of nodes do not overlap with each other, for the analysis and interpretation, it was necessary to apply a threshold to limit the number of nodes displayed in the map for clarity.…”

“…A low threshold allows more items to be analyzed and displayed on the map, but also may create overly complex images ( Henstock et al, 2022 ). Following Henstock et al (2022) , whilst VOSviewer applies techniques to optimize the visualization so that labels of nodes do not overlap with each other, for the analysis and interpretation, it was necessary to apply a threshold to limit the number of nodes displayed in the map for clarity. The authors add that applying thresholds is a means to control the VOSviewer’s viewing capabilities, e.g., adjust the number of clusters and nodes displayed to give a clear visualization of the individual nodes and networks.…”