交叉试验中基于似然性的缺失数据分析

IF 0.6 4区 数学 Q4 STATISTICS & PROBABILITY
S. Pareek, K. Das, S. Mukhopadhyay
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引用次数: 1

摘要

多变量混合效应模型似乎最适合交叉试验中收集的基因表达数据。然而,当某些响应缺失时,使用标准统计推断很难获得可靠的结果。特别是对于交叉研究,缺失是一个严重的问题,因为试验需要少量的参与者。采用蒙特卡罗电磁法(MCEM)来处理这种情况。除了估计之外,MCEM似然比检验(LRTs)被开发用于检验缺失数据的交叉模型中的固定效应。在分析基因表达数据之前,进行了密集的模拟研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Likelihood-based missing data analysis in crossover trials
A multivariate mixed-effects model seems to be the most appropriate for gene expression data collected in a crossover trial. It is, however, difficult to obtain reliable results using standard statistical inference when some responses are missing. Particularly for crossover studies, missingness is a serious concern as the trial requires a small number of participants. A Monte Carlo EM (MCEM)-based technique was adopted to deal with this situation. In addition to estimation, MCEM likelihood ratio tests (LRTs) are developed to test fixed effects in crossover models with missing data. Intensive simulation studies were conducted prior to analyzing gene expression data.
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来源期刊
CiteScore
1.60
自引率
10.00%
发文量
30
审稿时长
>12 weeks
期刊介绍: The Brazilian Journal of Probability and Statistics aims to publish high quality research papers in applied probability, applied statistics, computational statistics, mathematical statistics, probability theory and stochastic processes. More specifically, the following types of contributions will be considered: (i) Original articles dealing with methodological developments, comparison of competing techniques or their computational aspects. (ii) Original articles developing theoretical results. (iii) Articles that contain novel applications of existing methodologies to practical problems. For these papers the focus is in the importance and originality of the applied problem, as well as, applications of the best available methodologies to solve it. (iv) Survey articles containing a thorough coverage of topics of broad interest to probability and statistics. The journal will occasionally publish book reviews, invited papers and essays on the teaching of statistics.
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