A sequential density‐based empirical likelihood ratio test for treatment effects. (20th January 2019)

• Record Type: Journal Article
• Authors: Zou, Li; Vexler, Albert; Yu, Jihnhee; Wan, Hongzhi
• Is Part Of: Statistics in medicine. Volume 38:Number 12(2019)
• Page Start: 2115
• Page End: 2125
• Publication Date: 2019-01-20
• Subjects: density‐based empirical likelihood -- empirical likelihood -- entropy -- likelihood ratio -- paired data -- sequential signed‐rank test -- treatment effect -- ventilator‐associated pneumonia; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8095
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : In health‐related experiments, treatment effects can be identified using paired data that consist of pre‐ and posttreatment measurements. In this framework, sequential testing strategies are widely accepted statistical tools in practice. Since performances of parametric sequential testing procedures vitally depend on the validity of the parametric assumptions regarding underlying data distributions, we focus on distribution‐free mechanisms for sequentially evaluating treatment effects. In fixed sample size designs, the density‐based empirical likelihood (DBEL) methods provide powerful nonparametric approximations to optimal Neyman‐Pearson–type statistics. In this article, we extend the DBEL methodology to develop a novel sequential DBEL testing procedure for detecting treatment effects based on paired data. The asymptotic consistency of the proposed test is shown. An extensive Monte Carlo study confirms that the proposed test outperforms the conventional sequential Wilcoxon signed‐rank test across a variety of alternatives. The excellent applicability of the proposed method is exemplified using the ventilator‐associated pneumonia study that evaluates the effect of Chlorhexidine Gluconate treatment in reducing oral colonization by pathogens in ventilated patients.