Impact of approximating or ignoring within‐study covariances in multivariate meta‐analyses
Bibliographic record
Abstract
Multivariate meta-analyses are used to derive summary estimates of treatment effects for two or more outcomes from a joint model. In addition to treatment effects, these models also quantify the correlations between outcomes across studies. To be fully specified, the model requires an estimate of the covariance or correlations between outcomes observed in each study. These are rarely available in published reports, so that analysts must either approximate these or ignore correlations between effect estimates from the same studies. We examined the impact of errors in approximating within-study covariances on the parameters of multivariate models in a simulation study. We found that treatment effect and heterogeneity estimates were not strongly affected by inaccurate approximations, but estimates of the correlation between outcomes were sometimes highly biased. The potential for error is greatest when the covariance between outcomes within- and between-studies are of comparable scale.
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How this classification was reachedexpand
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 | 0.210 | 0.157 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads 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".