scholarly journals Group Sequential Methods and Sample Size Savings in Biomarker-Disease Association Studies

Genetics ◽  
2003 ◽  
Vol 163 (3) ◽  
pp. 1215-1219
Author(s):  
R Aplenc ◽  
H Zhao ◽  
T R Rebbeck ◽  
K J Propert

Abstract Molecular epidemiological association studies use valuable biosamples and incur costs. Statistical methods for early genotyping termination may conserve biosamples and costs. Group sequential methods (GSM) allow early termination of studies on the basis of interim comparisons. Simulation studies evaluated the application of GSM using data from a case-control study of GST genotypes and prostate cancer. Group sequential boundaries (GSB) were defined in the EAST-2000 software and were evaluated for study termination when early evidence suggested that the null hypothesis of no association between genotype and disease was unlikely to be rejected. Early termination of GSTM1 genotyping, which demonstrated no association with prostate cancer, occurred in >90% of the simulated studies. On average, 36.4% of biosamples were saved from unnecessary genotyping. In contrast, for GSTT1, which demonstrated a positive association, inappropriate termination occurred in only 6.6%. GSM may provide significant cost and sample savings in molecular epidemiology studies.

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

2013 ◽  
Vol 177 (2) ◽  
pp. 131-141 ◽  
Author(s):  
Jennifer C. Nelson ◽  
Onchee Yu ◽  
Clara P. Dominguez-Islas ◽  
Andrea J. Cook ◽  
Do Peterson ◽  
...  

2013 ◽  
Vol 2013 ◽  
pp. 1-24
Author(s):  
Zhengjia Chen ◽  
Xinjia Chen

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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