Stat Med. 2026 Aug;45(18-19):e70681. doi: 10.1002/sim.70681.
ABSTRACT
Group testing is widely recognized for its ability to reduce time and cost in screening for rare diseases. However, with limited samples, the parameter estimation from group-testing data might be inefficient. In some scenarios, the external summary statistics are available. To make full use of them, we propose a multiple-source knowledge transfer method. The resulting prevalence estimator is consistent and more efficient than the conventional estimator based on the target group-testing data alone. We establish the asymptotic normality of the proposed estimator. In addition, we develop a data-driven screening procedure to identify the transferable sources. We extend this framework to Dorfman’s two-stage group testing design and show that integrating the retesting data yields efficiency gains. The asymptotic normality of the corresponding estimator is also established. Extensive simulations and a real-data application support the theoretical results and demonstrate the favorable finite-sample performance of the proposed method.
PMID:42504761 | DOI:10.1002/sim.70681
