On Sample Size and Inference for Two‐Stage Adaptive Designs

Biometrics ◽  
2001 ◽  
Vol 57 (1) ◽  
pp. 172-177 ◽  
Author(s):  
Qing Liu ◽  
George Y. H. Chi
2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Bo Yu ◽  
Xiaonan Liang ◽  
Ying Wang ◽  
Yun Liu ◽  
Qiao Chang ◽  
...  

When designing the sample scheme, it is important to determine the sample size. The survey accuracy and cost of survey and sampling method should be considered comprehensively. In this article, we discuss the method of determining the sample size of complex successive sampling with rotation sample for sensitive issue and deduce the formulas for the optimal sample size under two-stage sampling and stratified two-stage sampling by using Cauchy-Schwartz inequality, respectively, so as to minimize the cost for given sampling errors and to minimize the sampling errors for given cost.


1992 ◽  
Vol 71 (1) ◽  
pp. 3-14 ◽  
Author(s):  
John E. Overall ◽  
Robert S. Atlas

A statistical model for combining p values from multiple tests of significance is used to define rejection and acceptance regions for two-stage and three-stage sampling plans. Type I error rates, power, frequencies of early termination decisions, and expected sample sizes are compared. Both the two-stage and three-stage procedures provide appropriate protection against Type I errors. The two-stage sampling plan with its single interim analysis entails minimal loss in power and provides substantial reduction in expected sample size as compared with a conventional single end-of-study test of significance for which power is in the adequate range. The three-stage sampling plan with its two interim analyses introduces somewhat greater reduction in power, but it compensates with greater reduction in expected sample size. Either interim-analysis strategy is more efficient than a single end-of-study analysis in terms of power per unit of sample size.


2016 ◽  
Vol 27 (1) ◽  
pp. 158-171 ◽  
Author(s):  
Haolun Shi ◽  
Guosheng Yin

Conventional phase II clinical trials use either a single- or multi-arm comparison scheme to examine the therapeutic effects of the experimental drug. Both single- and multi-arm evaluations have their own merits; for example, single-arm phase II trials are easy to conduct and often require a smaller sample size, while multiarm trials are randomized and typically lead to a more objective comparison. To bridge the single- and double-arm schemes in one trial, we propose a two-stage design, in which the first stage takes a single-arm comparison of the experimental drug with the standard response rate (no concurrent treatment) and the second stage imposes a two-arm comparison by adding an active control arm. The design is calibrated using a new concept, the detectable treatment difference, to balance the trade-offs between futility termination, power, and sample size. We conduct extensive simulation studies to examine the operating characteristics of the proposed method and provide an illustrative example of our design.


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