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Likelihood-Based Methods for Assessing Principal Surrogate Endpoints in Vaccine Trials
Ying Huang1, Shibasish Dasgupta2
1Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA.
Statistics in Biosciences
|October 9, 2020
Summary
This study introduces a new likelihood-based method for vaccine trials with missing outcome data. The new method outperforms existing approaches, especially when using a close-out placebo vaccination component.
Area of Science:
- Biostatistics
- Vaccinology
- Clinical Trial Design
Background:
- Missing outcome data is a challenge in vaccine trials.
- Auxiliary variables and close-out placebo vaccination (CPV) are used for prediction.
- The estimated likelihood (EL) method is commonly adopted.
Purpose of the Study:
- To develop a joint likelihood-based method for estimating biomarker distribution and maximizing likelihood.
- To describe methods for selecting auxiliary variables.
- To compare the new method with the EL method.
Main Methods:
- Developed a likelihood-based (ML) approach for joint estimation.
- Simulated numerical studies to compare ML and EL methods.
- Applied methods to data from a dengue vaccine trial.
Main Results:
- ML and EL methods showed similar performance in standard trial designs.
- ML method demonstrated superior estimation performance with a CPV component.
- ML method performed comparably or better than EL with numerous covariates.
Conclusions:
- The likelihood-based method offers improved estimation in vaccine trials, particularly with CPV.
- The method is valuable for planning future vaccine trials.
- The study highlights the potential of CPV in enhancing trial accuracy.
Keywords:
Baseline PredictorCloseout-Placebo VaccinationLikelihoodPrincipal SurrogateSelectionVaccine TrialMore Related Videos
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