Yuan Ao, Piao Jin, Ning Jing, Qin Jing
Department of Biostatistics, Bioinformatics & Biomathematics, Georgetown University, Washington, DC USA.
Department of Preventive Medicine, The University of Southern California, Los Angeles, CA USA.
Can J Stat. 2021 Sep;49(3):659-677. doi: 10.1002/cjs.11581. Epub 2020 Oct 28.
In the group testing procedure, several individual samples are grouped and the pooled samples, instead of each individual sample, are tested for outcome status (e.g., infectious disease status). Although this cost-effectiveness strategy in data collection is both labor and time efficient, it poses statistical challenges to derive statistically and computationally efficient estimators under semiparametric models. We consider semiparametric isotonic regression models for the simultaneous estimation of the conditional probability curve and covariate effects, in which a parametric form for combining the covariate information is assumed and the monotonic link function is left unspecified. We develop an expectation-maximization algorithm to overcome the computational challenge and embed the pool-adjacent violators algorithm in the M-step to facilitate the computation. We establish the large sample behavior of the proposed estimators and examine their finite sample performance in simulation studies. We apply the proposed method to data from the National Health and Nutrition Examination Survey for illustration.
在分组检测程序中，几个个体样本被组合在一起，对合并后的样本而非每个个体样本进行结果状态（如传染病状态）检测。尽管这种数据收集方面的成本效益策略在人力和时间上都很高效，但在半参数模型下推导具有统计和计算效率的估计量时会带来统计挑战。我们考虑用于同时估计条件概率曲线和协变量效应的半参数等距回归模型，其中假设了一种用于组合协变量信息的参数形式，而单调链接函数未作具体规定。我们开发了一种期望最大化算法来克服计算挑战，并在M步中嵌入池相邻违规者算法以促进计算。我们建立了所提出估计量的大样本性质，并在模拟研究中检验了它们的有限样本性能。为作说明，我们将所提出的方法应用于来自国家健康与营养检查调查的数据。
