Integrating Aggregate-Data and Individual-Patient-Data in Meta-Analysis: An Empirical Assessment and an Alternative Method for the Two-Stage Approach
Bibliographic record
Abstract
In this study, we compared the efficacy of the overall meta-analysis estimates that used only the available aggregate data (AD) studies against those that combined the available AD and individual patient data (IPD) studies. We introduced some modifications to the existing two-stage method for combining the AD and IPD studies. We evaluated the effects of these modifications on the estimates of the overall treatment effect, and investigated the influence of the number of studies included in the meta-analysis, N, and the ratio of AD: IPD on these estimates. We used percentage relative bias (PRB), root mean-square-error (RMSE), and coverage probability to assess the overall efficiency of these estimates. The results revealed the superiority of estimates from the combined AD: IPD studies over those that utilized only the available AD in terms of both the accuracy and the RMSE. We found that the current method for combining the AD:IPD studies provided poor coverage probabilityand that the proposed methods generated improved coverage probability by more than 40% while maintaining the level of bias and RMSE at par to their existing counterparts. These findings validated the importance of utilizing both the AD and IPD studies whenever they are available, and demonstrated the significance of proper technique for combining these studies in order to obtain better overall estimates.
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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 | 0.227 | 0.444 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.019 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".