Assessing the Expected Value of Research Studies in Reducing Uncertainty and Improving Implementation Dynamics

Grimm, Sabine; Dixon, Simon; Stevens, John

doi:10.1177/0272989x16686766

Cited by 18 publications

(52 citation statements)

References 22 publications

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“…Fourteen studies [9] , [10] , [11] , [12] , [13] , [14] , [18] , [19] , [22] , [23] , [24] , [27] , [28] , [35] elicited individually from experts and aggregated mathematically, three aimed to achieve consensus among experts [15] , [17] , [21] , and three others did not explicitly report the method of aggregation used [16] , [20] , [25] ( Table 2 ).…”

Section: Resultsmentioning

confidence: 99%

Experiences of Structured Elicitation for Model-Based Cost-Effectiveness Analyses

et al. 2018

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BackgroundEmpirical evidence supporting the cost-effectiveness estimates of particular health care technologies may be limited, or it may even be missing entirely. In these situations, additional information, often in the form of expert judgments, is needed to reach a decision. There are formal methods to quantify experts’ beliefs, termed as structured expert elicitation (SEE), but only limited research is available in support of methodological choices. Perhaps as a consequence, the use of SEE in the context of cost-effectiveness modelling is limited.ObjectivesThis article reviews applications of SEE in cost-effectiveness modelling with the aim of summarizing the basis for methodological choices made in each application and recording the difficulties and challenges reported by the authors in the design, conduct, and analyses.MethodsThe methods used in each application were extracted along with the criteria used to support methodological and practical choices and any issues or challenges discussed in the text. Issues and challenges were extracted using an open field, and then categorised and grouped for reporting.ResultsThe review demonstrates considerable heterogeneity in methods used, and authors acknowledge great methodological uncertainty in justifying their choices. Specificities of the context area emerging as potentially important in determining further methodological research in elicitation are between- expert variation and its interpretation, the fact that substantive experts in the area may not be trained in quantitative subjects, that judgments are often needed on various parameter types, the need for some form of assessment of validity, and the need for more integration with behavioural research to devise relevant debiasing strategies.ConclusionsThis review of experiences of SEE highlights a number of specificities/constraints that can shape the development of guidance and target future research efforts in this area.

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Section: Resultsmentioning

confidence: 99%

Experiences of Structured Elicitation for Model-Based Cost-Effectiveness Analyses

et al. 2018

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“…For difficult-to-study parameters, an integration of their framework with our own may be necessary to identify the optimal time to initiate a study given the presence of time-varying parameters affecting the study population, uncertain study costs over the study horizon, and an uncertain time to study completion. We also assume that a change in policy can be implemented immediately; implementation can be slow and this may influence the optimal time to collect information [60, 61]. In our example, we only consider one time-varying parameter and one time invariant parameter.…”

Section: Discussionmentioning

confidence: 99%

Optimal Information Collection Policies in a Markov Decision Process Framework

et al. 2018

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“…The approach of subcategorizing EVPIM can be applied to retrospective implementation patterns, as in the current case study, as well as future predicted implementation patterns. Works by Grimm et al [13][14][15] have outlined methods for predicting future implementation patterns, based on diffusion theory and expert beliefs. Although it is not yet common practice to perform detailed predictions of future implementations patterns, this work demonstrates how EVPIM and EVSIM, as well as our proposed subcategorization, can be estimated prior to the introduction of new technologies.…”

Section: Strength and Limitations Of The Proposed Subcategorization Omentioning

confidence: 99%

“…5,6 Applications and extensions of the EVPIM framework have formalized and investigated the expected value of specific implementation strategies (EVSIM), estimating the value of specific (but imperfect) improvements in implementation that may be achieved by actual or hypothetical implementation strategies. [7][8][9][10][11][12][13][14][15][16][17] Some of these contributions have also demonstrated how the framework can be used to estimate the value of improving implementation in specific subgroups, 10,11 estimating the value of implementation improvements resulting from further evidence being generated, 7,[12][13][14][15][16][17] and taking into account, as well as predicting, future implementation patterns. [10][11][12][13][14][15] These contributions have improved the applicability and versatility of the EVPIM and EVSIM framework in assessing the value of implementation strategies based on retrospective as well as predicted future implementation patterns.…”

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confidence: 99%

Subcategorizing the Expected Value of Perfect Implementation to Identify When and Where to Invest in Implementation Initiatives

et al. 2020

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Purpose. Clinical practice variations and low implementation of effective and cost-effective health care technologies are a key challenge for health care systems and may lead to suboptimal treatment and health loss for patients. The purpose of this work was to subcategorize the expected value of perfect implementation (EVPIM) to enable estimation of the absolute and relative value of eliminating slow, low, and delayed implementation. Methods. Building on the EVPIM framework, this work defines EVPIM subcategories to estimate the expected value of eliminating slow, low, or delayed implementation. The work also shows how information on regional implementation patterns can be used to estimate the value of eliminating regional implementation variation. The application of this subcategorization is illustrated by a case study of the implementation of an antiplatelet therapy for the secondary prevention after myocardial infarction in Sweden. Incremental net benefit (INB) estimates are based on published cost-effectiveness assessments and a threshold of SEK 250,000 (£22,300) per quality-adjusted life year (QALY). Results. In the case study, slow, low, and delayed implementation was estimated to represent 22%, 34%, and 44% of the total population EVPIM (2941 QALYs or SEK 735 million), respectively. The value of eliminating implementation variation across health care regions was estimated to 39% of total EVPIM (1138 QALYs). Conclusion. Subcategorizing EVPIM estimates the absolute and relative value of eliminating different parts of suboptimal implementation. By doing so, this approach could help decision makers to identify which parts of suboptimal implementation are contributing most to total EVPIM and provide the basis for assessing the cost and benefit of implementation activities that may address these in future implementation of health care interventions.

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Assessing the Expected Value of Research Studies in Reducing Uncertainty and Improving Implementation Dynamics

Abstract: Background: With low implementation of cost-effective health technologies being a problem in many

Cited by 18 publications

References 22 publications

Experiences of Structured Elicitation for Model-Based Cost-Effectiveness Analyses

Experiences of Structured Elicitation for Model-Based Cost-Effectiveness Analyses

Optimal Information Collection Policies in a Markov Decision Process Framework

Subcategorizing the Expected Value of Perfect Implementation to Identify When and Where to Invest in Implementation Initiatives

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