scholarly journals Two-Stage Design of Sequencing Studies for Testing Association with Rare Variants

2011 ◽  
Vol 71 (4) ◽  
pp. 209-220 ◽  
Author(s):  
Fan Yang ◽  
Duncan C. Thomas
Keyword(s):  
Rare Variants ◽  
Two Stage ◽  
Stage Design ◽  
Sequencing Studies ◽  
Two Stage Design

scholarly journals Evaluating rare variants under two-stage design

2012 ◽  
Vol 57 (6) ◽  
pp. 352-357
Author(s):  
Qizhai Li ◽  
Dongdong Pan ◽  
Weihua Yue ◽  
Ying Gao ◽  
Kai Yu
Keyword(s):  
Rare Variants ◽  
Two Stage ◽  
Stage Design ◽  
Two Stage Design

Tubular electrostatic precipitators of two-stage design

1981 ◽  
Vol 6 (1-6) ◽  
pp. 239-244 ◽  
Author(s):  
Harish S. Surati ◽  
Michael R. Beltran ◽  
Isaac Raigorodsky
Keyword(s):  
Two Stage ◽  
Stage Design ◽  
Electrostatic Precipitators ◽  
Two Stage Design

scholarly journals Bayesian Two-Stage Adaptive Design in Bioequivalence

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Shengjie Liu ◽  
Jun Gao ◽  
Yuling Zheng ◽  
Lei Huang ◽  
Fangrong Yan
Keyword(s):  
Statistical Power ◽  
Adaptive Design ◽  
Type I Error ◽  
Probability Model ◽  
Type I ◽  
Two Stage ◽  
Stage Design ◽  
Estimation Strategy ◽  
Drug Products ◽  
Two Stage Design

AbstractBioequivalence (BE) studies are an integral component of new drug development process, and play an important role in approval and marketing of generic drug products. However, existing design and evaluation methods are basically under the framework of frequentist theory, while few implements Bayesian ideas. Based on the bioequivalence predictive probability model and sample re-estimation strategy, we propose a new Bayesian two-stage adaptive design and explore its application in bioequivalence testing. The new design differs from existing two-stage design (such as Potvin’s method B, C) in the following aspects. First, it not only incorporates historical information and expert information, but further combines experimental data flexibly to aid decision-making. Secondly, its sample re-estimation strategy is based on the ratio of the information in interim analysis to total information, which is simpler in calculation than the Potvin’s method. Simulation results manifested that the two-stage design can be combined with various stop boundary functions, and the results are different. Moreover, the proposed method saves sample size compared to the Potvin’s method under the conditions that type I error rate is below 0.05 and statistical power reaches 80 %.

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