Assessing the Robustness of Meta-Analysis for the Fixed-Effect Model
Bibliographic record
Abstract
The current meta-analysis methods for the fixed-effect model with continuous outcome variables have been developed based on the assumption that the variation of the outcome variable between patients within treatment groups for each study follows a normal distribution. However, real-world data does not always follow a normal distribution, which may lead to unreliable meta-analysis results. This study uses the Monte Carlo simulation to evaluate robustness by comparing the analysis results with the truth when the normal assumption is violated; performance measures include the relative bias of the estimated treatment effect, the coverage probability of the estimates, and the power and type I error rate of the test of the null hypothesis. We simulate various non-normal outcome data, including a mixture of normals, lognormal, gamma, and χ^2 distributions. We examine the impact of the sample size per study, the number of studies, the magnitude of skewness, and the effect sizes on the results. The results show that small studies with highly skewed data provide non-robust meta-analysis results for a fixed-effect model. Moreover, increasing the number of studies without sufficient sample sizes worsens the relative bias, coverage probability, and power. Therefore, this simulation suggests that investigators must be cautious when applying the fixed-effect model to small studies, particularly with respect to the potential non-normality of the data. This study recommends that investigators include large trials whenever possible. If large trials are not feasible, they should always assess the normality of the datasets and select an appropriate meta-analysis method to obtain robust results. This will help ensure that policies and guidelines are based on reliable evidence, thereby minimizing the risk of implementing ineffective and harmful policies and guidelines.
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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 | 0.462 | 0.679 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.033 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".