Meta-analysis of longitudinal studies
Bibliographic record
Abstract
BACKGROUND: Longitudinal studies typically report estimates of the effect of a treatment or exposure at various times during the course of follow-up. Meta-analyses of these studies must account for correlations between effect estimates from the same study. PURPOSE: To describe and contrast alternative approaches to handling correlations inherent to longitudinal effect estimates in meta-analyses. METHODS: Linear mixed-effects models can account for correlations in a number of ways. We considered three alternatives: including study-specific random-effects, correlated time-specific random-effects or a general multivariate specification that also allows correlated within-study residuals. Data from a review of studies of the effect of deep-brain stimulation (DBS) in patients with Parkinson's disease are used to illustrate the application of these models. RESULTS: are contrasted with those from a naïve meta-analysis in which the correlations are ignored. Results The data included 46 studies that yielded 82 estimates of the effect of DBS measured at 3, 6, 12 months or later after implantation of the stimulator. Models that accounted for correlations, particularly the full multivariate specification, provided better fit (lower AIC) and yielded slightly more precise effect estimates. This was in part due to a relatively extreme observation from a study that provided similar estimates at other times, which in the naïve approach exerts greater influence since it is treated as an independent observation. LIMITATIONS: Since the true values of the parameters are not known, it is impossible to confirm that estimates from the multivariate approach are necessarily more accurate. CONCLUSION: Standard meta-analytic models can be readily extended to account for correlations between effects in longitudinal studies. These models may provide better fit and possibly more precise summary effect 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 | 0.073 | 0.173 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.050 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".