Empirical Bayes models of Poisson clinical trials and sample size determination

2009 ◽  
pp. n/a-n/a ◽  
Author(s):  
Boris G. Zaslavsky
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Empirical Bayes ◽  
Sample Size Determination ◽  
Size Determination

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.

Sample Size in the Planning and Interpretation of Clinical Trials

1987 ◽  
Vol 58 (04) ◽  
pp. 953-956 ◽  
Author(s):  
Mark N levine ◽  
Jack Hirsh
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
False Negative ◽  
Sample Size Determination ◽  
Type Ii ◽  
Size Determination ◽  
Type Ii Error ◽  
Treatment Groups ◽  
Negative Conclusion

AbstractAn understanding of sample size determination is important in both planning and interpreting the results of clinical trials. A Type II error occurs when it is concluded that there is no difference between treatment groups, when in truth there is a difference. Such a false negative conclusion results from too few patients in a trial. In this review the principles of estimating sample size before a trial is commenced and evaluating the results of a negative completed trial are reviewed. Clinically relevant examples are used to illustrate these concepts.

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