Comparison of statistical inferences from the DerSimonian–Laird and alternative random‐effects model meta‐analyses – an empirical assessment of 920 Cochrane primary outcome meta‐analyses
Why this work is in the frame
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Bibliographic record
Abstract
In random-effects model meta-analysis, the conventional DerSimonian-Laird (DL) estimator typically underestimates the between-trial variance. Alternative variance estimators have been proposed to address this bias. This study aims to empirically compare statistical inferences from random-effects model meta-analyses on the basis of the DL estimator and four alternative estimators, as well as distributional assumptions (normal distribution and t-distribution) about the pooled intervention effect. We evaluated the discrepancies of p-values, 95% confidence intervals (CIs) in statistically significant meta-analyses, and the degree (percentage) of statistical heterogeneity (e.g. I(2)) across 920 Cochrane primary outcome meta-analyses. In total, 414 of the 920 meta-analyses were statistically significant with the DL meta-analysis, and 506 were not. Compared with the DL estimator, the four alternative estimators yielded p-values and CIs that could be interpreted as discordant in up to 11.6% or 6% of the included meta-analyses pending whether a normal distribution or a t-distribution of the intervention effect estimates were assumed. Large discrepancies were observed for the measures of degree of heterogeneity when comparing DL with each of the four alternative estimators. Estimating the degree (percentage) of heterogeneity on the basis of less biased between-trial variance estimators seems preferable to current practice. Disclosing inferential sensitivity of p-values and CIs may also be necessary when borderline significant results have substantial impact on the conclusion. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.280 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 it