Zheng Qi
Department of Epidemiology and Biostatistics, Texas A&M School of Public Health, College Station, TX 77843, United States of America.
Math Biosci. 2021 May;335:108572. doi: 10.1016/j.mbs.2021.108572. Epub 2021 Mar 1.
For nearly eight decades the Luria-Delbrück protocol remains the preferred method for experimentally determining microbial mutation rates. However, earnest development and rigorous applications of statistical methods for mutation rate comparison using fluctuation assay data are a relatively recent phenomenon. While likelihood ratio tests tailored for the fluctuation protocol give investigators appropriate tools, researchers sometimes may prefer to view the comparison of two mutation rates through the lens of the ratio of the two mutation rates. The idea of using the bootstrap technique to construct intervals for mutation rate fold change was proposed nearly a decade ago, but it failed to gain traction partly due to a failure to incorporate likelihood-based estimation. In addition to extending the bootstrap method, this paper proposes two new methods of constructing intervals for mutation rate fold change: a profile likelihood method and a Bayesian method utilizing Monte Carlo Markov chain. All three methods are assessed by large-scale simulations.
近八十年来,鲁里亚-德尔布吕克实验方案一直是通过实验测定微生物突变率的首选方法。然而,利用波动试验数据进行突变率比较的统计方法的认真开发和严格应用却是相对较新的现象。虽然为波动试验方案量身定制的似然比检验为研究人员提供了合适的工具,但研究人员有时可能更倾向于通过两个突变率的比值来比较两个突变率。近十年前就有人提出使用自助法技术构建突变率倍数变化区间的想法,但部分由于未能纳入基于似然的估计,该想法未能获得广泛应用。除了扩展自助法,本文还提出了两种构建突变率倍数变化区间的新方法:一种轮廓似然法和一种利用蒙特卡罗马尔可夫链的贝叶斯方法。所有这三种方法都通过大规模模拟进行了评估。