Bilder Christopher R, Tebbs Joshua M
Department of Statistics, University of Nebraska, Lincoln, NE 68583, U.S.A.
J Stat Comput Simul. 2009 Jan 1;79(1):67-80. doi: 10.1080/00949650701608990.
Group testing involves pooling individual items together and testing them simultaneously for a rare binary trait. Whether the goal is to estimate the prevalence of the trait or to identify those individuals that possess it, group testing can provide substantial benefits when compared to testing subjects individually. Recently, group-testing regression models have been proposed as a way to incorporate covariates when estimating trait prevalence. In this paper, we examine these models by comparing fits obtained from individual and group testing samples. Relative bias and efficiency measures are used to assess the accuracy and precision of the resulting estimates using different grouping strategies. We also investigate the agreement of individual and group-testing regression estimates for various grouping strategies and the effects of group size selection. Depending on how groups are formed, our results show that group-testing regression models can perform very well when compared to the analogous models based on individual observations. However, different grouping strategies can provide very different results in finite samples.
分组检测涉及将个体样本汇集在一起，并同时对一种罕见的二元性状进行检测。无论目标是估计该性状的流行率还是识别具有该性状的个体，与对个体进行检测相比，分组检测都能带来显著益处。最近，分组检测回归模型被提出，作为在估计性状流行率时纳入协变量的一种方法。在本文中，我们通过比较从个体检测样本和分组检测样本获得的拟合结果来研究这些模型。相对偏差和效率度量被用于评估使用不同分组策略得出的估计值的准确性和精确性。我们还研究了各种分组策略下个体检测和分组检测回归估计值的一致性以及组大小选择的影响。根据分组方式的不同，我们的结果表明，与基于个体观测的类似模型相比，分组检测回归模型可以表现得非常出色。然而，在有限样本中，不同的分组策略可能会产生非常不同的结果。
