Montesinos-López Osval A, Montesinos-López Abelardo, Eskridge Kent, Crossa José
1 Facultad de Telemática, Universidad de Colima, Colima, México.
2 Departamento de Estadística, Centro de Investigación en Matemáticas (CIMAT), Guanajuato, Guanajuato, México.
Stat Methods Med Res. 2017 Jun;26(3):1093-1109. doi: 10.1177/0962280214568047. Epub 2015 Jan 19.
Because pools are tested instead of individuals in group testing, this technique is helpful for estimating prevalence in a population or for classifying a large number of individuals into two groups at a low cost. For this reason, group testing is a well-known means of saving costs and producing precise estimates. In this paper, we developed a mixed-effect group testing regression that is useful when the data-collecting process is performed using inverse sampling. This model allows including covariate information at the individual level to incorporate heterogeneity among individuals and identify which covariates are associated with positive individuals. We present an approach to fit this model using maximum likelihood and we performed a simulation study to evaluate the quality of the estimates. Based on the simulation study, we found that the proposed regression method for inverse sampling with group testing produces parameter estimates with low bias when the pre-specified number of positive pools (r) to stop the sampling process is at least 10 and the number of clusters in the sample is also at least 10. We performed an application with real data and we provide an NLMIXED code that researchers can use to implement this method.
由于在分组检测中是对样本池而非个体进行检测，所以该技术有助于估计人群中的患病率，或以低成本将大量个体分为两组。因此，分组检测是一种广为人知的节省成本并进行精确估计的方法。在本文中，我们开发了一种混合效应分组检测回归模型，当使用逆抽样进行数据收集过程时该模型很有用。该模型允许纳入个体层面的协变量信息，以体现个体间的异质性，并确定哪些协变量与阳性个体相关。我们提出了一种使用最大似然法拟合该模型的方法，并进行了模拟研究以评估估计的质量。基于模拟研究，我们发现，当用于停止抽样过程的预先指定的阳性样本池数量（r）至少为10且样本中的聚类数量也至少为10时，所提出的用于分组检测逆抽样的回归方法会产生低偏差的参数估计。我们使用真实数据进行了应用，并提供了一个NLMIXED代码，研究人员可用于实施该方法。
