An Introduction to Individual Participant Data Meta-analysis
Bibliographic record
Abstract
Meta-analysis using individual participant data (IPD-MA) from randomized controlled trials (RCTs) can strengthen evidence used for decision making and is considered the "gold standard" approach. In this study, we present the importance, properties, and main approaches of conducting an IPD-MA. We exemplify the main approaches of conducting an IPD-MA and how these can be used to obtain subgroup effects through estimation of interaction terms. IPD-MA has several benefits over traditional aggregate data (AD) meta-analysis. These include standardization of definitions of outcomes and/or scales, reanalysis of eligible RCTs using the same analysis model across all studies, accounting for missing outcome data, detecting outliers, using participant-level covariates to explore intervention-by-covariate interactions, and tailoring intervention effects to participant characteristics. IPD-MA can be performed in either a 2-stage or 1-stage approach. We exemplify the presented methods using 2 illustrative examples. The first real-life example includes 6 studies assessing sonothrombolysis with or without addition of microspheres against IV thrombolysis alone (i.e., control) in acute ischemic stroke participants with large vessel occlusions. The second real-life example includes 7 studies evaluating the association between blood pressure levels after endovascular thrombectomy and functional improvement of acute ischemic stroke in patients with large vessel occlusion. IPD reviews can be associated with higher quality statistical analysis and may differ from AD reviews. Unlike individual trials that lack power and AD meta-analysis results, which suffer from confounding and aggregation bias, the use of IPD allows us to explore intervention-by-covariate interactions. However, a key limitation of conducting an IPD-MA is retrieval of IPD from original RCTs. Time and resources should be carefully planned before embarking on retrieving IPD.
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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 | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.187 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.035 | 0.013 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.013 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.030 |
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".