Group sequential, sample size re-estimation and two-stage adaptive designs in clinical trials: a comparison

2006 ◽  
Vol 25 (6) ◽  
pp. 933-941 ◽  
Author(s):  
Weichung Joe Shih
Biometrics ◽  
2001 ◽  
Vol 57 (1) ◽  
pp. 172-177 ◽  
Author(s):  
Qing Liu ◽  
George Y. H. Chi

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.


2020 ◽  
Author(s):  
Shesh Rai ◽  
Chen Qian ◽  
Jianmin Pan ◽  
Anand Seth ◽  
Deo Kumar Srivast ◽  
...  

Abstract Background Researchers around the world are urgently conducting clinical trials to develop new treatments for reducing mortality and morbidity related to COVID-19. However, due to unknown features of the disease and complexity of the patient population, traditional trial designs may not be optimal in such patients. We propose two independent clinical trials designs based on careful grouping of the expected characteristics of patient population. This could serve as a useful guide for researchers designing COVID-19 related Phase II/III trials. Methods Using the commonly utilized World Health Organization ordinal scale on patient status, we classify patients into three risk groups. In this approach, patients in Stages 3, 4 and 5 are categorized as the intermediate-risk group while patients in Stages 6 and 7 are categorized as the high-risk group. To ensure that an intervention, if deemed efficacious, is promptly made available to vulnerable patients, we propose a group sequential design with two interim analyses along with a final analysis and a toxicity monitoring rule for the intermediate-risk group. For the high-risk group, we propose a group sequential design with two interim analyses without toxicity monitoring. Results Based on different response rates, effect sizes, and power, required sample size and toxicity boundaries are calculated for each scenario. Sample size requirements for the designs with interim analyses are only marginally greater than the ones without. In addition, for both the intermediate-risk group and the high-risk group, conducting two interim analyses have identical required sample size compared with just one interim analysis. Additional issues that could potentially impact the trial are discussed. Conclusions We recommend using composite endpoints, with binary outcome for those in Stages 3, 4 and 5 with a power of 90% to detect an improvement of 20% in response rate, and 30 days mortality rate outcome for those in Stages 6 and 7 with a power of 90% to detect 15% (effect size) reduced mortality rate, in the COVID-19 trial design. For the intermediate-risk group, two interim analyses for efficacy evaluation along with toxicity monitoring are encouraged. For the high-risk group, two interim analyses without toxicity monitoring is advised.


2014 ◽  
Vol 33 (17) ◽  
pp. 2897-2913 ◽  
Author(s):  
Koko Asakura ◽  
Toshimitsu Hamasaki ◽  
Tomoyuki Sugimoto ◽  
Kenichi Hayashi ◽  
Scott R. Evans ◽  
...  

2008 ◽  
Vol 13 (1) ◽  
pp. 31-40 ◽  
Author(s):  
Sevil Bacanlı ◽  
Özgür Peker ◽  
Yaprak Demirhan

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