A Simple Approach to Sample Size Calculation for Count Data in Matched Cohort Studies
Bibliographic record
Abstract
In matched cohort studies exposed and unexposed individuals are matched on certain characteristics to form clusters to reduce potential confounding effects. Data in these studies are clustered and thus dependent due to matching. When the outcome is a Poisson count, specialized methods have been proposed for sample size estimation. However, in practice the variance of the counts often exceeds the mean (i.e. counts are overdispersed), so that Poisson methods don’t apply. We propose a simple approach for calculating statistical power and sample size for clustered Poisson data when the proportion of exposed subjects in a cluster is constant across clusters. We extend the approach to clustered count data with overdispersion, which is common in practice. We evaluate these approaches with simulation studies and apply them to a matched cohort study examining the association of parental depression with health care utilization. Simulation results show that the methods for estimating power and sample size performed reasonably well under the scenarios examined and were robust in the presence of mixed exposure proportions up to 30%.
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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 | 0.018 | 0.413 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".