Critical appraisal of nonrandomized studies—A review of recommended and commonly used tools
Bibliographic record
Abstract
RATIONALE, AIMS, AND OBJECTIVES: When randomized controlled trial data are limited or unavailable, or to supplement randomized controlled trial evidence, health technology assessment (HTA) agencies may rely on systematic reviews of nonrandomized studies (NRSs) for evidence of the effectiveness of health care interventions. NRS designs may introduce considerable bias into systematic reviews, and several methodologies by which to evaluate this risk of bias are available. This study aimed to identify tools commonly used to assess bias in NRS and determine those recommended by HTA bodies. METHODS: Appraisal tools used in NRS were identified through a targeted search of systematic reviews (January 2013-March 2017; MEDLINE and EMBASE [OVID SP]). Recommendations for the critical appraisal of NRS by expert review groups and HTA bodies were reviewed. RESULTS: From the 686 studies included in the narrative synthesis, 48 critical appraisal tools were identified. Commonly used tools included the Newcastle-Ottawa Scale, the methodological index for NRS, and bespoke appraisal tools. Neither the Cochrane Handbook nor the Centre for Reviews and Dissemination recommends a particular instrument for the assessment of risk of bias in NRS, although Cochrane has recently developed their own NRS critical appraisal tool. Among HTA bodies, only the Canadian Agency for Drugs and Technologies in Health recommends use of a specific critical appraisal tool-SIGN 50 (for cohort or case-control studies). Several criteria including reporting, external validity, confounding, and power were examined. CONCLUSION: There is no consensus between HTA groups on the preferred appraisal tool. Reviewers should select from a suite of tools on the basis of the design of studies included in their review.
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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 | 0.411 | 0.683 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.064 | 0.038 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.010 | 0.010 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".