Bridging health technology assessment (HTA) with multicriteria decision analyses (MCDA): field testing of the EVIDEM framework for coverage decisions by a public payer in Canada
Bibliographic record
Abstract
BACKGROUND: Consistent healthcare decision making requires systematic consideration of decision criteria and evidence available to inform them. This can be tackled by combining multicriteria decision analysis (MCDA) and Health Technology Assessment (HTA). The objective of this study was to field-test a decision support framework (EVIDEM), explore its utility to a drug advisory committee and test its reliability over time. METHODS: Tramadol for chronic non-cancer pain was selected by the health plan as a case study relevant to their context. Based on extensive literature review, a by-criterion HTA report was developed to provide synthesized evidence for each criterion of the framework (14 criteria for the MCDA Core Model and 6 qualitative criteria for the Contextual Tool). During workshop sessions, committee members tested the framework in three steps by assigning: 1) weights to each criterion of the MCDA Core Model representing individual perspective; 2) scores for tramadol for each criterion of the MCDA Core Model using synthesized data; and 3) qualitative impacts of criteria of the Contextual Tool on the appraisal. Utility and reliability of the approach were explored through discussion, survey and test-retest. Agreement between test and retest data was analyzed by calculating intra-rater correlation coefficients (ICCs) for weights, scores and MCDA value estimates. RESULTS: The framework was found useful by the drug advisory committee in supporting systematic consideration of a broad range of criteria to promote a consistent approach to appraising healthcare interventions. Directly integrated in the framework as a "by-criterion" HTA report, synthesized evidence for each criterion facilitated its consideration, although this was sometimes limited by lack of relevant data. Test-retest analysis showed fair to good consistency of weights, scores and MCDA value estimates at the individual level (ICC ranging from 0.676 to 0.698), thus lending some support for the reliability of the approach. Overall, committee members endorsed the inclusion of most framework criteria and revealed important areas of discussion, clarification and adaptation of the framework to the needs of the committee. CONCLUSIONS: By promoting systematic consideration of all decision criteria and the underlying evidence, the framework allows a consistent approach to appraising healthcare interventions. Further testing and validation are needed to advance MCDA approaches in healthcare decisionmaking.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.455 | 0.611 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".