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Analysis of counts for cluster randomized trials: Negative controls and test-negative designs
Suzanne M Dufault1, Nicholas P Jewell1,2
1Division of Epidemiology and Biostatistics, University of California, Berkeley, California.
Statistics in Medicine
|January 31, 2020
Summary
Cluster randomized trials (CRTs) analyzing count data can be biased if case ascertainment differs between groups. Using negative control counts helps reduce this bias, ensuring reliable results when denominators are unknown.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health Interventions
Background:
- Cluster randomized trials (CRTs) often analyze count outcomes like disease infections or hospitalizations at the cluster level.
- Standard analysis uses cluster outcome rates with denominators, but these are sometimes unknown, especially with passive surveillance.
- Directly comparing counts without denominators relies on randomization but can be biased by differential ascertainment in unblinded CRTs.
Purpose of the Study:
- To evaluate methods for analyzing count data in cluster randomized trials (CRTs) when denominators are unknown.
- To introduce and assess the use of negative control counts for bias reduction in CRTs.
- To compare the performance of count-only estimators versus those using negative controls via simulation.
Main Methods:
- Simulation study comparing statistical approaches for cluster randomized trials.
- Analysis of count data without known denominators.
- Application of permutation tests for small numbers of randomized clusters.
- Introduction and evaluation of negative control counts to mitigate ascertainment bias.
Main Results:
- Count-only approaches perform comparably to negative control methods when there is no differential ascertainment.
- Count-only estimators become unreliable under even modest differential ascertainment across intervention arms.
- Negative control counts effectively reduce bias caused by differential ascertainment in unblinded CRTs.
Conclusions:
- Negative control counts are a valuable tool for improving the reliability of analyses in cluster randomized trials (CRTs) with unknown denominators and potential for differential ascertainment.
- Standard count-only methods are insufficient when differential ascertainment is present, highlighting the need for bias-mitigation strategies like negative controls.
- The findings support the use of negative control counts in CRTs, particularly for community-level interventions where blinding may not be feasible.
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