Stakeholder engagement in economic evaluation: Protocol for using the nominal group technique to elicit patient, healthcare provider, and health system stakeholder input in the development of an early economic evaluation model of chimeric antigen receptor T-cell therapy
Bibliographic record
Abstract
INTRODUCTION: Chimeric antigen receptor T-cell (CAR-T) therapy is a class of immunotherapy. An economic evaluation conducted at an early stage of development of CAR-T therapy for treatment of adult relapsed or refractory acute lymphoblastic leukaemia could provide insight into factors contributing to the cost of treatment, the potential clinical benefits, and what the health system can afford. Traditionally, stakeholders are engaged in certain parts of health technology assessment processes, such as in the identification and selection of technologies, formulation of recommendations, and implementation of recommendations; however, little is known about processes for stakeholder engagement during the conduct of the assessment. This is especially the case for economic evaluations. Stakeholders, such as clinicians, policy-makers, patients, and their support networks, have insight into factors that can enhance the validity of an economic evaluation model. This research outlines a specific methodology for stakeholder engagement and represents an avenue to enhance health economic evaluations and support the use of these models to inform decision making for resource allocation. This protocol may inform a tailored framework for stakeholder engagement processes in future economic evaluation model development. METHODS AND ANALYSIS: We will involve clinicians, healthcare researchers, payers, and policy-makers, as well as patients and their support networks in the conduct and verification of an early economic evaluation of a novel health technology to incorporate stakeholder-generated knowledge. Three stakeholder-specific focus groups will be conducted using an online adaptation of the nominal group technique to elicit considerations from each. This study will use CAR-T therapy for adults with relapsed or refractory B-cell acute lymphoblastic leukaemia as a basis for investigating broader stakeholder engagement processes. ETHICS AND DISSEMINATION: This study received ethics approval from the Ottawa Hospital Research Institute Research Ethics Board (REB 20200320-01HT) and the results will be shared via conference presentations, peer-reviewed publications, and ongoing stakeholder engagement.
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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 | 0.347 | 0.305 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.071 | 0.016 |
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".