Current use of routinely collected health data to complement randomized controlled trials: a meta-epidemiological survey
Bibliographic record
Abstract
BACKGROUND: Studies that use routinely collected health data (RCD studies) are advocated to complement evidence from randomized controlled trials (RCTs) for comparative effectiveness research and to inform health care decisions when RCTs would be unfeasible. We aimed to evaluate the current use of routinely collected health data to complement RCT evidence. METHODS: We searched PubMed for RCD studies published to 2010 that evaluated the comparative effectiveness of medical treatments on mortality using propensity scores. We identified RCTs of the same treatment comparisons and evaluated how frequently the RCD studies analyzed treatments that had not been compared previously in randomized trials. When RCTs did exist, we noted the claimed motivations for each RCD study. We also analyzed the citation impact of the RCD studies. RESULTS: Of 337 eligible RCD studies identified, 231 (68.5%) analyzed treatments that had already been compared in RCTs. The study investigators rarely claimed that it would be unethical (6/337) or difficult (18/337) to perform RCTs on the same question. Evidence from RCTs was mentioned or cited by authors of 213 RCD studies. The most common motivations for conducting the RCD studies were alleged limited generalizability of trial results to the "real world" (37.6%), evaluation of specific outcomes (31.9%) or specific populations (23.5%), and inconclusive or inconsistent evidence from randomized trials (25.8%). Studies evaluating "real world" effects had the lowest citation impact. INTERPRETATION: Most of the RCD studies we identified explored comparative treatment effects that had already been investigated in RCTs. The objective of such studies needs to shift more toward answering pivotal questions that are not supported by trial evidence or for which RCTs would be unfeasible.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.442 | 0.773 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.024 |
| Bibliometrics | 0.051 | 0.046 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".