Department of Biostatistics, University of Washington, Seattle, Washington, USA.
Department of Biostatistics and Informatics, University of Colorado - Anschutz Medical Campus, Aurora, Colorado, USA.
Stat Med. 2022 Oct 30;41(24):4809-4821. doi: 10.1002/sim.9537. Epub 2022 Aug 17.
Serial limiting dilution (SLD) assays are a widely used tool in many areas of public health research to measure the concentration of target entities. This concentration can be estimated via maximum likelihood. Asymptotic as well as exact inference methods have been proposed for hypothesis testing and confidence interval construction in this one-sample problem. However, in many scientific applications, it may be of interest to compare the concentration of target entities between a pair of samples and construct valid confidence intervals for the difference in concentrations. In this paper, an exact, computationally efficient inferential procedure is proposed for hypothesis testing and confidence interval construction in the two-sample SLD assay problem. The proposed exact method is compared to an approach based on asymptotic approximations in simulation studies. The methods are illustrated using data from the University of North Carolina HIV Cure Center.
连续极限稀释 (SLD) 测定法是公共卫生研究许多领域中广泛使用的一种工具,用于测量目标实体的浓度。可以通过最大似然法来估计该浓度。对于该单样本问题,已经提出了渐近和精确推断方法用于假设检验和置信区间构建。然而,在许多科学应用中,可能有兴趣比较一对样本中目标实体的浓度,并为浓度差异构建有效的置信区间。在本文中,针对 SLD 测定中二样本问题,提出了一种精确、计算高效的假设检验和置信区间构建推断方法。在模拟研究中,将所提出的精确方法与基于渐近近似的方法进行了比较。使用北卡罗来纳大学艾滋病毒治愈中心的数据说明了这些方法。