scholarly journals Sample size determination in group-sequential clinical trials with two co-primary endpoints

2014 ◽  
Vol 33 (17) ◽  
pp. 2897-2913 ◽  
Author(s):  
Koko Asakura ◽  
Toshimitsu Hamasaki ◽  
Tomoyuki Sugimoto ◽  
Kenichi Hayashi ◽  
Scott R. Evans ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Sample Size Determination ◽  
Size Determination ◽  
Group Sequential ◽  
Primary Endpoints

Sample size determination in clinical trials with multiple co-primary endpoints including mixed continuous and binary variables

2012 ◽  
Vol 54 (5) ◽  
pp. 716-729 ◽  
Author(s):  
Takashi Sozu ◽  
Tomoyuki Sugimoto ◽  
Toshimitsu Hamasaki
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Sample Size Determination ◽  
Size Determination ◽  
Binary Variables ◽  
Primary Endpoints

Sample size determination for group sequential clinical trials with immediate response

1992 ◽  
Vol 11 (10) ◽  
pp. 1391-1399 ◽  
Author(s):  
Kyungmann Kim ◽  
David L. Demets
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Sample Size Determination ◽  
Size Determination ◽  
Group Sequential ◽  
Immediate Response

scholarly journals Sample size determination for a specific region in multiregional clinical trials with multiple co-primary endpoints

PLoS ONE ◽  
2017 ◽  
Vol 12 (6) ◽  
pp. e0180405 ◽  
Author(s):  
Wong-Shian Huang ◽  
Hui-Nien Hung ◽  
Toshimitsu Hamasaki ◽  
Chin-Fu Hsiao
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Specific Region ◽  
Sample Size Determination ◽  
Size Determination ◽  
Primary Endpoints

Sample Size Determination for Three-Level Randomized Clinical Trials with Randomization at the First or Second Level

2014 ◽  
Vol 24 (3) ◽  
pp. 579-599 ◽  
Author(s):  
Melissa J. Fazzari ◽  
Mimi Y. Kim ◽  
Moonseong Heo
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Randomized Clinical Trials ◽  
Sample Size Determination ◽  
Size Determination

scholarly journals Efficient and flexible simulation-based sample size determination for clinical trials with multiple design parameters

2020 ◽  
pp. 096228022097579
Author(s):  
Duncan T Wilson ◽  
Richard Hooper ◽  
Julia Brown ◽  
Amanda J Farrin ◽  
Rebecca EA Walwyn
Keyword(s):  
Sample Size ◽  
Computer Experiments ◽  
Small Sample ◽  
Sample Size Determination ◽  
Design Parameters ◽  
Size Determination ◽  
Parametric Regression ◽  
Software Packages ◽  
Primary Endpoints ◽  
Simulation Based

Simulation offers a simple and flexible way to estimate the power of a clinical trial when analytic formulae are not available. The computational burden of using simulation has, however, restricted its application to only the simplest of sample size determination problems, often minimising a single parameter (the overall sample size) subject to power being above a target level. We describe a general framework for solving simulation-based sample size determination problems with several design parameters over which to optimise and several conflicting criteria to be minimised. The method is based on an established global optimisation algorithm widely used in the design and analysis of computer experiments, using a non-parametric regression model as an approximation of the true underlying power function. The method is flexible, can be used for almost any problem for which power can be estimated using simulation, and can be implemented using existing statistical software packages. We illustrate its application to a sample size determination problem involving complex clustering structures, two primary endpoints and small sample considerations.

Use of interval estimations in design and evaluation of multiregional clinical trials with continuous outcomes

2018 ◽  
Vol 28 (7) ◽  
pp. 2179-2195 ◽  
Author(s):  
Chieh Chiang ◽  
Chin-Fu Hsiao
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Type I Error ◽  
Interval Estimation ◽  
Error Rates ◽  
New Drugs ◽  
Sample Size Determination ◽  
Type I ◽  
Size Determination ◽  
Interval Estimators

Multiregional clinical trials have been accepted in recent years as a useful means of accelerating the development of new drugs and abridging their approval time. The statistical properties of multiregional clinical trials are being widely discussed. In practice, variance of a continuous response may be different from region to region, but it leads to the assessment of the efficacy response falling into a Behrens–Fisher problem—there is no exact testing or interval estimator for mean difference with unequal variances. As a solution, this study applies interval estimations of the efficacy response based on Howe’s, Cochran–Cox’s, and Satterthwaite’s approximations, which have been shown to have well-controlled type I error rates. However, the traditional sample size determination cannot be applied to the interval estimators. The sample size determination to achieve a desired power based on these interval estimators is then presented. Moreover, the consistency criteria suggested by the Japanese Ministry of Health, Labour and Welfare guidance to decide whether the overall results from the multiregional clinical trial obtained via the proposed interval estimation were also applied. A real example is used to illustrate the proposed method. The results of simulation studies indicate that the proposed method can correctly determine the required sample size and evaluate the assurance probability of the consistency criteria.

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