Likelihood-based missing data analysis in crossover trials

被引：2
|
作者：
Pareek, Savita [1 ]
Das, Kalyan [1 ]
Mukhopadhyay, Siuli [1 ]
机构：
[1] Indian Inst Technol, Dept Math, Mumbai 400076, India
来源：
BRAZILIAN JOURNAL OF PROBABILITY AND STATISTICS | 2023年 / 37卷 / 02期
关键词：
Crossover trials; Monte Carlo EM algorithm; MCEM likelihood ratio tests; INCOMPLETE DATA; STATISTICAL-METHODS; MAXIMUM-LIKELIHOOD; DROP-OUT; DESIGNS; INFERENCE; RESPONSES; 2-PERIOD; SUBJECT; MODELS;
D O I：
10.1214/23-BJPS570
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
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 are developed to test fixed effects in crossover models with missing data. Intensive simulation stud-ies were conducted prior to analyzing gene expression data.
引用
收藏
页码：329 / 350
页数：22
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