Comparison of Methods for Clustered Data Analysis in a Non-Ideal Situation: Results from an Evaluation of Predictors of Yellow Fever Vaccine Refusal in the Global TravEpiNet (GTEN) Consortium
Bibliographic record
Abstract
Not accounting for clustering in data from multiple centers might yield biased estimates and their standard errors, potentially leading to incorrect inferences. We fit 15 different models with different correlation structures and with/without adjustment for small clusters, including unadjusted logistic regression, Population-averaged models (Generalized Estimating Equations), Cluster-specific models (linear and non-linear with random intercept) and Survey data analysis methods to study the association of variables with the probability of declining yellow fever vaccine among patients seeking pre-travel health consultations at 18 US practices in the Global TravEpiNet Consortium from 1 January, 2009, to 6 June, 2012. Results varied by the method chosen. Generally, when the odds ratio estimates were similar, adjusting for clustering and the small number of clinics increased the standard errors. We chose the random intercept model with the Morel, Bokossa and Neerchal (MBN) adjustment to be the most preferable method for the GTEN dataset since this was one of the more conservative models that accounted for clustering, small sample sizes and also the random effect due to site. Investigators should not ignore clustering and consider the appropriate adjustments necessary for their studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".