scholarly journals Point‐biserial correlation: Interval estimation, hypothesis testing, meta‐analysis, and sample size determination

2019 ◽  
Vol 73 (S1) ◽  
pp. 113-144 ◽  
Author(s):  
Douglas G. Bonett
Keyword(s):  
Hypothesis Testing ◽  
Sample Size ◽  
Meta Analysis ◽  
Interval Estimation ◽  
Sample Size Determination ◽  
Size Determination ◽  
Point Biserial Correlation ◽  
Biserial Correlation ◽  
Correlation Interval

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.

An approach for sample size determination of average bioequivalence based on interval estimation

2017 ◽  
Vol 36 (7) ◽  
pp. 1068-1082
Author(s):  
Chieh Chiang ◽  
Chin-Fu Hsiao
Keyword(s):  
Sample Size ◽  
Interval Estimation ◽  
Sample Size Determination ◽  
Size Determination ◽  
Average Bioequivalence

scholarly journals Hypothesis testing, power and sample size determination for between group comparisons in fMRI experiments

2009 ◽  
Vol 6 (2) ◽  
pp. 133-146 ◽  
Author(s):  
Dulal K. Bhaumik ◽  
Anindya Roy ◽  
Nicole A. Lazar ◽  
Kush Kapur ◽  
Subhash Aryal ◽  
...  
Keyword(s):  
Hypothesis Testing ◽  
Sample Size ◽  
Sample Size Determination ◽  
Size Determination ◽  
Testing Power ◽  
Group Comparisons
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