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Misclassified group-tested current status data
1Division of Biostatistics, School of Public Health, 101 Haviland Hall, University of California, Berkeley, California 94720, U.S.A.jewell@berkeley.edu.
Biometrika
|February 10, 2018
Summary
Group testing enhances disease prevalence estimation, especially for low prevalence and misclassification, by analyzing pooled samples. This method offers more precise estimates for time-to-event data, like age at infection.
Area of Science:
- Biostatistics
- Epidemiology
- Statistical modeling
Background:
- Group testing reduces costs for estimating disease prevalence using pooled samples.
- Current status data involves a binary response related to a time-to-event variable and an observed screening time.
- Nonparametric estimation of survival functions is possible with current status data.
Purpose of the Study:
- To develop and evaluate a nonparametric estimator for time-to-event distributions using group-tested current status data.
- To compare the performance of the group-based estimator against the individual testing estimator, particularly concerning precision in the presence of misclassification.
Main Methods:
- Utilized group testing on current status data, where a group tests positive if any individual's event time is less than the observed screening time.
- Employed the pool-adjacent-violators algorithm for nonparametric estimation.
- Investigated the precision of the group-based estimator versus the individual test nonparametric maximum likelihood estimator.
Main Results:
- The group-based estimator can be more precise than individual testing when misclassification is present and prevalence is low.
- Demonstrated the applicability of the method to estimate age-at-incidence curves.
Conclusions:
- Group testing offers a cost-effective and potentially more precise method for estimating time-to-event distributions from current status data, especially in scenarios with low prevalence and misclassification.
- The developed estimator has practical applications in public health, such as analyzing disease incidence over time.
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