Relative Risk Regression for Clustered Data with Application to Oral Health Research
Bibliographic record
Abstract
Introduction: Cluster-weighted generalized estimating equations (CWGEE) and doubly-weighted GEE (DWGEE) are used to produce unbiased estimates when informative cluster size (ICS) exists. However, their performance in estimating the relative risk (RR) from a Poisson regression is unknown. Methods: Using the dental data from the San Juan Overweight Adults Longitudinal Study (SOALS), we estimated the association between patient-level (sex, education level, smoking status, age) and tooth-level (bleeding upon probing) predictors and two types of outcomes with high and low prevalence each. We compared the odds ratio (OR) and RR estimates from logistic and Poisson CWGEE/DWGEE, respectively. Results: For patient-level covariates, the ORs estimated from logistic CWGEE/DWGEE and the RRs estimated from Poisson CWGEE/DWGEE were similar to the low-prevalence outcome (tooth loss). With the high-prevalence outcome (tooth loss or increase in attachment loss or pocket depth), the ORs were further from the null compared to the RRs. For example, within CWGEE, the OR of smoking was 1.301 (95% CI: 1.063-1.592), whereas the RR of smoking was 1.235 (95% CI: 1.052-1.450). For the tooth-level covariate (bleeding), there was a considerable difference between OR/RR on tooth loss estimated from CWGEE vs DWGEE. For example, the RR estimated from CWGEE was 1.692 (95% CI: 1.386-2.067), whereas the RR estimated from DWGEE was 1.354 (95% CI: 1.072-1.710). Discussion: CWGEE and DWGEE may produce different estimates for tooth-level (sub-cluster) covariates, especially when the prevalence of the outcome is low. In general, RRs estimated from Poisson CWGEE/DWGEE are closer to the null compared to ORs estimated from logistic CWGEE/DWGEE.
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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 | 0.132 | 0.324 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".