Research Note: Individual participant data (IPD) meta-analysis
Bibliographic record
Abstract
Research Note: Individual participant data (IPD) meta-analysis A systematic review is a robust method with which to search for, identify, extract, and synthesise evidence from individual studies to answer a specific research question. 1 Meta-analysis is a statistical analysis approach used in some systematic reviews to combine quantitative information across studies, in order to produce overall summaries of the evidence (eg, of a treatment's effect).Meta-analyses are most often conducted using data that has been extracted from peer-reviewed publications included in systematic reviews; such data are often called aggregate data, since they represent information combined across all participants in a particular study.The extracted data typically include a small number of data pieces from each study, such as the change in pain (mean, standard deviation) between treated and untreated study groups, which would allow a treatment effect estimate and its confidence interval to be calculated.An aggregate data meta-analysis is a useful approach with which to summarise the average overall effect of a treatment.However, having only aggregated group data limits the analyses that are possible, and in particular makes it problematic to examine relationships where individual participant-level covariates are of interest.To address this, another option to synthesise evidence across studies is to use the original, participant-level study data, using an approach called individual participant data (IPD) meta-analysis.This Research Note describes the steps involved in an IPD metaanalysis, explains when this research approach is most useful, and discusses key advantages, challenges and potential future directions.Table 1 provides definitions of some key terms.Although this Research Note focuses on meta-analysis of randomised trials evaluating treatment effectiveness, most points apply more broadly.
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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 | high |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | 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 | 0.063 | 0.370 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.018 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.163 | 0.014 |
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.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".