Meta-analysis of multi-jurisdictional health administrative data from distributed networks approximated individual-level multivariable regression
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Compare meta-analysis in a distributed network to individual-level analysis for assessment of time trends of health services utilization with health administrative data. METHODS: We used administrative data from Ontario, Canada to analyze temporal trends in pediatric inflammatory bowel disease health services use. Beta coefficients were obtained using negative binomial, logistic, and Cox proportional hazards regression models. We replicated the individual-level analyses in each Ontario Local Health Integration Network (LHIN), then meta-analyzed aggregate trends using both fixed and random effects meta-analysis. We compared the pooled estimates of effect with individual-level analysis. RESULTS: Beta coefficients, summary effect estimates, and 95% confidence intervals (CIs) from the meta-analysis of data from distributed networks were not different than those from individual-level data, regardless of meta-analytic approach used. For example, the 5-year odds ratio of colectomy in ulcerative colitis using individual-level analysis was 0.978 (95% CI 0.950 to 1.007) compared to distributed network fixed effects meta-analysis: 0.982 (95% CI 0.950 to 1.015), and random effects meta-analysis: 0.982 (95% CI 0.950 to 1.015). CONCLUSION: Meta-analysis of multi-jurisdictional estimates were similar to estimates obtained from individual-level analysis. This method is a valid alternative for analysis of multi-jurisdictional data when individual-level data cannot be shared.
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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 | 0.030 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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; 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".