Accounting for expected attrition in the planning of community intervention trials
Bibliographic record
Abstract
Trials in which intact communities are the units of randomization are increasingly being used to evaluate interventions which are more naturally administered at the community level, or when there is a substantial risk of treatment contamination. In this article we focus on the planning of community intervention trials in which k communities (for example, medical practices, worksites, or villages) are to be randomly allocated to each of an intervention and a control group, and fixed cohorts of m individuals enrolled in each community prior to randomization. Formulas to determine k or m may be obtained by adjusting standard sample size formulas to account for the intracluster correlation coefficient rho. In the presence of individual-level attrition however, observed cohort sizes are likely to vary. We show that conventional approaches of accounting for potential attrition, such as dividing standard sample size formulas by the anticipated follow-up rate pi or using the average anticipated cohort size m pi, may, respectively, overestimate or underestimate the required sample size when cluster follow-up rates are highly variable, and m or rho are large. We present new sample size estimation formulas for the comparison of two means or two proportions, which appropriately account for variation among cluster follow-up rates. These formulas are derived by specifying a model for the binary missingness indicators under the population-averaged approach, assuming an exchangeable intracluster correlation coefficient, denoted by tau. To aid in the planning of future trials, we recommend that estimates for tau be reported in published community intervention trials.
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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 | 0.363 | 0.690 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".