Regression analysis of group-tested current status data

Summary Group testing is an effective way to reduce the time and cost associated with conducting large-scale screening for infectious diseases. Benefits are realized through testing pools formed by combining specimens, such as blood or urine, from different individuals. In some studies, individuals...

Full description

Saved in:
Bibliographic Details
Published in:Biometrika 2024-09, Vol.111 (3), p.1047-1061
Main Authors: Li, Shuwei, Hu, Tao, Wang, Lianming, McMahan, Christopher S, Tebbs, Joshua M
Format: Article
Language:English
Subjects:
Algorithms
Asymptotic methods
Asymptotic properties
Cost benefit analysis
Data analysis
Data augmentation
Hygiene
Infectious diseases
Mathematical models
Maximum likelihood estimates
Maximum likelihood estimation
Maximum likelihood estimators
Regression analysis
Regression models
Sexually transmitted diseases
STD
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Summary Group testing is an effective way to reduce the time and cost associated with conducting large-scale screening for infectious diseases. Benefits are realized through testing pools formed by combining specimens, such as blood or urine, from different individuals. In some studies, individuals are assessed only once and a time-to-event endpoint is recorded, for example, the time until infection. Combining group testing with this type of endpoint results in group-tested current status data (Petito & Jewell, 2016). To analyse these complex data, we propose methods that estimate a proportional hazard regression model based on test outcomes from measuring the pools. A sieve maximum likelihood estimation approach is developed that approximates the cumulative baseline hazard function with a piecewise constant function. To identify the sieve estimator, a computationally efficient expectation-maximization algorithm is derived by using data augmentation. Asymptotic properties of both the parametric and nonparametric components of the sieve estimator are then established by applying modern empirical process theory. Numerical results from simulation studies show that our proposed method performs nominally and has advantages over the corresponding estimation method based on individual testing results. We illustrate our work by analysing a chlamydia dataset collected by the State Hygienic Laboratory at the University of Iowa.
ISSN:0006-3444
1464-3510
DOI:10.1093/biomet/asae006