Sample size estimation for comparing two or more treatment groups in clinical trials

1991 ◽  
Vol 10 (1) ◽  
pp. 33-43 ◽  
Author(s):  
Simon J. Day ◽  
David F. Graham
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Sample Size Estimation ◽  
Size Estimation ◽  
Treatment Groups

Sample Size Estimation of Multiregional Clinical Trials with Heterogeneous Variability Across Regions

2014 ◽  
Vol 24 (2) ◽  
pp. 254-271 ◽  
Author(s):  
Yuh-Jenn Wu ◽  
Te-Sheng Tan ◽  
Shein-Chung Chow ◽  
Chin-Fu Hsiao
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Sample Size Estimation ◽  
Size Estimation

scholarly journals Sample size estimation for clinical trials with tau‐PET SUVR as the primary outcome in dominantly inherited Alzheimer’s disease

2021 ◽  
Vol 17 (S9) ◽  
Author(s):  
Guoqiao Wang ◽  
Yan Li ◽  
Chengjie Xiong ◽  
Tammie L.S. Benzinger ◽  
Brian A. Gordon ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Sample Size ◽  
Primary Outcome ◽  
Sample Size Estimation ◽  
Size Estimation ◽  
Tau Pet

scholarly journals Modelling and sample size reestimation for longitudinal count data with incomplete follow up

2017 ◽  
Vol 28 (1) ◽  
pp. 117-133 ◽  
Author(s):  
Thomas Asendorf ◽  
Robin Henderson ◽  
Heinz Schmidli ◽  
Tim Friede
Keyword(s):  
Multiple Sclerosis ◽  
Clinical Trials ◽  
Sample Size ◽  
Count Data ◽  
Negative Binomial ◽  
Sample Size Estimation ◽  
Type I ◽  
Size Estimation ◽  
Recent Trial ◽  
Longitudinal Count Data

We consider modelling and inference as well as sample size estimation and reestimation for clinical trials with longitudinal count data as outcomes. Our approach is general but is rooted in design and analysis of multiple sclerosis trials where lesion counts obtained by magnetic resonance imaging are important endpoints. We adopt a binomial thinning model that allows for correlated counts with marginal Poisson or negative binomial distributions. Methods for sample size planning and blinded sample size reestimation for randomised controlled clinical trials with such outcomes are developed. The models and approaches are applicable to data with incomplete observations. A simulation study is conducted to assess the effectiveness of sample size estimation and blinded sample size reestimation methods. Sample sizes attained through these procedures are shown to maintain the desired study power without inflating the type I error. Data from a recent trial in patients with secondary progressive multiple sclerosis illustrate the modelling approach.

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