Regression analysis of group-tested current status data

• 成果类型: Article
• 署名作者: Li, Shuwei; Hu, Tao; Wang, Lianming; McMahan, Christopher S.; Tebbs, Joshua M.
• 署名单位: Guangzhou University; Capital Normal University; University of South Carolina System; University of South Carolina Columbia; Clemson University; University of South Carolina System; University of South Carolina Columbia
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asae006
• 发表日期: 2024
• 关键词: maximum-likelihood-estimation; additive hazards regression; Nonparametric Regression; chlamydia-trachomatis; efficient estimation; MODEL; infections; PREVALENCE; specimens; pools

摘要

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 (). 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.