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Estimands and inference in cluster-randomized vaccine trials
Kayla W Kilpatrick1, Michael G Hudgens1, M Elizabeth Halloran2,3
1Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA.
This study defines estimands for vaccine effects in cluster-randomized trials. It shows the direct vaccine effect is generally not estimable without assumptions, unlike overall, indirect, and total effects.
Area of Science:
- Epidemiology
- Biostatistics
- Clinical Trials
Background:
- Cluster-randomized trials are crucial for evaluating vaccine efficacy.
- Clearly defining estimands is essential for aligning study objectives with data collection and analysis.
Purpose of the Study:
- To define estimands and estimators for overall, indirect, and total vaccine effects in cluster-randomized trials.
- To investigate the estimability of the direct vaccine effect.
Main Methods:
- The study considers a scenario with individual self-selection into a cluster-randomized trial.
- Outcomes are measured on all individuals within each cluster.
- It analyzes estimands for various vaccine effect components.
Main Results:
- Estimands and estimators for overall, indirect, and total vaccine effects are proposed.
- The direct vaccine effect is generally not estimable without additional assumptions, such as the absence of unmeasured confounding.
Conclusions:
- The paper provides a framework for defining vaccine effect estimands in cluster-randomized trials.
- It highlights the challenges in estimating the direct vaccine effect, emphasizing the need for careful consideration of assumptions.
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