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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.411 | 0.657 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.043 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".