Comparing intraclass correlation coefficient estimators for binary outcomes in sample size calculations in twin pregnancies
Bibliographic record
Abstract
PURPOSE: Intraclass correlation coefficients (ICCs) can be used to adjust for clustering in sample size calculations, but different ICC estimators for binary outcomes can return different estimates. We assessed the ability of five common ICC estimators to calculate sample sizes that achieve the desired power, for studies that compare binary outcomes in treated and untreated twin pregnancies. METHODS: We simulated studies in twin pregnancies with varying levels of clustering and outcome prevalence. We used ICC estimators derived from logistic generalized estimating equations (GEE), analysis of variance (ANOVA), linear mixed modelling (LMM), and logistic generalized linear mixed modelling (GLMM). We calculated the required sample size to obtain 80 % power (5 % Type I error) using a standard formula and used simulation to estimate the empirical power. RESULTS: ICC estimates from GEE, ANOVA, and LMM were similar to each other, constant across outcome prevalence, and yielded required sample sizes that achieved the desired power. ICC estimators using logistic GLMM varied across outcome prevalence and yielded required sample sizes that were larger than necessary (power >80 %) when clustering was high or when outcome prevalence was low. CONCLUSIONS: Investigators using ICCs in sample size calculations including twin pregnancies should consider avoiding estimates from logistic GLMMs.
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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 | 0.267 | 0.708 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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; 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".