Multiple Summary Knowledge-Based Disease Prevalence Estimation for Group Testing Data.

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.