Treatment effects in randomised trials using routinely collected data for outcome assessment versus traditional trials: meta-research study
Bibliographic record
Abstract
Abstract Objective To compare effect estimates of randomised clinical trials that use routinely collected data (RCD-RCT) for outcome ascertainment with traditional trials not using routinely collected data. Design Meta-research study. Data source Studies included in the same meta-analysis in a Cochrane review. Eligibility criteria for study selection Randomised clinical trials using any type of routinely collected data for outcome ascertainment, including from registries, electronic health records, and administrative databases, that were included in a meta-analysis of a Cochrane review on any clinical question and any health outcome together with traditional trials not using routinely collected data for outcome measurement. Review methods Effect estimates from trials using or not using routinely collected data were summarised in random effects meta-analyses. Agreement of (summary) treatment effect estimates from trials using routinely collected data and those not using such data was expressed as the ratio of odds ratios. Subgroup analyses explored effects in trials based on different types of routinely collected data. Two investigators independently assessed the quality of each data source. Results 84 RCD-RCTs and 463 traditional trials on 22 clinical questions were included. Trials using routinely collected data for outcome ascertainment showed 20% less favourable treatment effect estimates than traditional trials (ratio of odds ratios 0.80, 95% confidence interval 0.70 to 0.91, I 2 =14%). Results were similar across various types of outcomes (mortality outcomes: 0.92, 0.74 to 1.15, I 2 =12%; non-mortality outcomes: 0.71, 0.60 to 0.84, I 2 =8%), data sources (electronic health records: 0.81, 0.59 to 1.11, I 2 =28%; registries: 0.86, 0.75 to 0.99, I 2 =20%; administrative data: 0.84, 0.72 to 0.99, I 2 =0%), and data quality (high data quality: 0.82, 0.72 to 0.93, I 2 =0%). Conclusions Randomised clinical trials using routinely collected data for outcome ascertainment show smaller treatment benefits than traditional trials not using routinely collected data. These differences could have implications for healthcare decision making and the application of real world evidence.
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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 | 0.139 | 0.376 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.037 | 0.067 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".