Converting Evidence‐Based Summary of Findings Evidence Tables Into Decision Analytical, Quality Adjusted Life Years (QALY) and Life Expectancies Metrics: A Tutorial
Bibliographic record
Abstract
RATIONALE, AIMS, AND OBJECTIVES: We have recently succeeded in integrating evidence estimation with decision-analytical frameworks, thereby addressing a major challenge in advancing the science of evidence-based medicine (EBM) and clinical practice guidelines. However, the primary output of our analysis was expressed as net differences in expected utility (ΔEU) between competing treatment interventions. Although expected utility is a standard decision-analytic metric, it is not intuitively understood by most clinicians. Here, we demonstrate how ΔEU can be converted into gains in quality-adjusted life years (QALYs) and life expectancy (LE). METHODS: We begin with GRADE (Grading of Recommendations Assessment, Development, and Evaluation) Summary of Findings (SoF) tables-the primary outputs of systematic reviews that underpin guideline recommendations-to generate ΔEU, which we subsequently convert into QALY and LE gains using the DEALE (Declining Exponential Approximation of Life Expectancy) method. We also integrate patients' values and preferences by relating minimal important differences (MIDs)-the smallest change in an outcome that patients perceive as important enough to justify a change in management-to relative values, which reflect the preference (or weight) assigned to avoiding a specific health outcome compared to the worst outcome (mortality). To convert a deterministic ΔEU model into a probabilistic one, we employ Monte Carlo simulation to assess the credibility of recommendations under the evidentiary uncertainty included in the SoF tables. We also provide a method to assess the impact of the certainty of evidence (CoE) on the robustness of the results. RESULTS: We developed a user-friendly, Excel-based calculator for converting evidence-based SoF tables into ΔEU, and subsequently into QALY and LE gains. We illustrate our methods by comparing the effects of short-term versus indefinite anticoagulation for the prevention of recurrent venous thromboembolism. The complete analysis can be performed in approximately 5-10 min. CONCLUSION: We extend our methods to link estimation metrics commonly used in the EBM field with decision-analytic metrics such as expected utility, QALY, and LE. We present a user-friendly calculator that integrates all key domains underpinning contemporary guideline development.
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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 | 0.065 | 0.343 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.043 | 0.013 |
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".