Accounting for complex intracluster correlations in longitudinal cluster randomized trials: a case study in malaria vector control
Bibliographic record
Abstract
Abstract Background: The effectiveness of malaria vector control interventions is commonly evaluated using parallel-arm cluster randomized trials with outcomes assessed using repeated cross-sectional surveys. A key requirement in designing and analyzing cluster randomized trials is to account for the intra-cluster correlation coefficient (ICC). In addition to exchangeable correlation (which assumes a constant ICC over time), correlation structures proposed for longitudinal cluster trials are block exchangeable (which allows a different within- and between-period ICC) and exponential decay (which allows the between-period ICC to decay at an exponential rate). More flexible correlation structures that do not require a decay are available in statistical software packages and, although not formally proposed for longitudinal cluster trials, may offer some advantages. Our objectives were to empirically explore the impact of these correlation structures on treatment effect inferences, identify gaps in the methodological literature, and make practical recommendations for investigators designing and analyzing such trials.Methods: We obtained data from a longitudinal parallel-arm cluster randomized trial conducted in Tanzania to compare four different types of insecticide-treated bed-nets. Malaria prevalence was assessed in repeated cross-sectional surveys of 45 households in each of 84 villages at baseline, 12 months, 18 months and 24 months post-randomization (19,083 children in total). We re-analyzed the data using mixed-effects logistic regression according to a prespecified analysis plan but under five different correlation structures as well as a robust variance estimator under exchangeable correlation and compared the estimated correlations and treatment effects.Results: The estimated correlation structures varied substantially across different models. The unstructured model was the best-fitting model based on information criteria. Although point estimates and confidence intervals for the treatment effect were similar, allowing for more flexible correlation structures led to different conclusions based on statistical significance. Use of robust variance estimators generally led to wider confidence intervals.Conclusion: More flexible correlation structures should not be ruled out in longitudinal cluster randomized trials. This may be particularly important in malaria trials where outcomes may fluctuate over time. In the absence of robust methods for selecting the best-fitting correlation structure, researchers should examine sensitivity of results to different assumptions about the ICC.
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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 | 0.601 | 0.711 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.012 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".