Nonparametric group sequential methods for recurrent and terminal events from multiple follow‐up windows

2019 ◽  
Vol 38 (30) ◽  
pp. 5657-5669
Author(s):  
Meng Xia ◽  
Susan Murray ◽  
Nabihah Tayob
Keyword(s):  
Group Sequential ◽  
Sequential Methods ◽  
Terminal Events ◽  
Group Sequential Methods

Group sequential methods based on ranked set samples

2012 ◽  
Vol 54 (3) ◽  
pp. 547-562 ◽  
Author(s):  
A. Hussein ◽  
H. A. Muttlak ◽  
E. Al-Sawi
Keyword(s):  
Group Sequential ◽  
Sequential Methods ◽  
Group Sequential Methods

scholarly journals Adapting Group Sequential Methods to Observational Postlicensure Vaccine Safety Surveillance: Results of a Pentavalent Combination DTaP-IPV-Hib Vaccine Safety Study

2013 ◽  
Vol 177 (2) ◽  
pp. 131-141 ◽  
Author(s):  
Jennifer C. Nelson ◽  
Onchee Yu ◽  
Clara P. Dominguez-Islas ◽  
Andrea J. Cook ◽  
Do Peterson ◽  
...  
Keyword(s):  
Vaccine Safety ◽  
Safety Study ◽  
Group Sequential ◽  
Sequential Methods ◽  
Safety Surveillance ◽  
Group Sequential Methods

scholarly journals Exact Group Sequential Methods for Estimating a Binomial Proportion

2013 ◽  
Vol 2013 ◽  
pp. 1-24
Author(s):  
Zhengjia Chen ◽  
Xinjia Chen
Keyword(s):  
Sequential Sampling ◽  
Asymptotic Optimality ◽  
Distribution Functions ◽  
Cumulative Distribution ◽  
Group Sequential ◽  
Sampling Schemes ◽  
Sequential Methods ◽  
New Family ◽  
Binomial Proportion ◽  
Group Sequential Methods

We first review existing sequential methods for estimating a binomial proportion. Afterward, we propose a new family of group sequential sampling schemes for estimating a binomial proportion with prescribed margin of error and confidence level. In particular, we establish the uniform controllability of coverage probability and the asymptotic optimality for such a family of sampling schemes. Our theoretical results establish the possibility that the parameters of this family of sampling schemes can be determined so that the prescribed level of confidence is guaranteed with little waste of samples. Analytic bounds for the cumulative distribution functions and expectations of sample numbers are derived. Moreover, we discuss the inherent connection of various sampling schemes. Numerical issues are addressed for improving the accuracy and efficiency of computation. Computational experiments are conducted for comparing sampling schemes. Illustrative examples are given for applications in clinical trials.

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