Systematic Reviews and Economic Evaluations in Tecchnology Appraisals Conducted for Nice in the UK: A Game of Two Halves?
Bibliographic record
Abstract
Rationale: Decision analytic models,as used in economic evaluations,require data on several clinical parameters.The gold standard approach is to conduct a systematic review of the relevant clinical literature, although reviews of economic evaluations indicate that this is rarely done.Technology appraisals for the National Institute for Health and Clinical Excellence (NICE), which are fully funded, represent the best case scenario for the close integration of economic evaluations and systematic reviews. Objectives: To assess the extent to which the systematic review of the clinical literature informs the economic evaluation in NICE technology appraisals Methods: All NICE technology assessment reports (TARs) published between January 2003 and July 2006 were considered. Data were abstracted on the TAR topics, the primary measure of clinical effectiveness, the approach to pooling in the clinical review, the measure of economic benefit and the use, or non-use, of the systematic review in the economic evaluation. Results: Forty four TARs were published in the period studied, all of which contained a systematic review. Most of the economic evaluations (40) were cost-utility analyses, reflecting NICE's guidelines for economic evaluation. The other analyses were cost-effectiveness analyses (2) and cost-minimisation studies (2). In 21 cases the clinical data were not pooled in the review, owing to heterogeneity in the clinical data or the limited number of studies. In these cases the economists used alternative approaches for estimating the key effectiveness parameter in the model. The results of the review (when pooled) were always used when the primary clinical effectiveness measure corresponded with the measure of economic benefit (eg survival). However, since preferenced-based quality of life measures are rarely included in clinical trials, the results of the systematic review were never directly used in the cost-utility analyses. Nevertheless, the outputs of the systematic review were used when the data were useful in estimating components of the QALY (eg the life-years gained, or the frequencies of health states to which QALYs could be assigned). Problems occurred mainly when the clinical data were not pooled, or when the measure of clinical benefit could not be converted into health states to which QALYs could be assigned. Conlusions: Economic evaluations can benefit from systematic reviews of the clinical literature. However, such reviews are not a panacea for conducting a good economic evaluation. Much of the relevant data for estimating QALYs are not contained in such reviews and the chosen method for summarising the clinical data may inhibit the assessment of economic benefit. Problems would be reduced if those undertaking the various components of technology appraisals discussed the data requirements for the economic model at an early stage.
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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 | 0.761 | 0.903 |
| Meta-epidemiology (narrow) | 0.006 | 0.009 |
| Meta-epidemiology (broad) | 0.017 | 0.008 |
| Bibliometrics | 0.027 | 0.029 |
| Science and technology studies | 0.005 | 0.054 |
| Scholarly communication | 0.039 | 0.058 |
| Open science | 0.012 | 0.026 |
| Research integrity | 0.047 | 0.031 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".