A simulation study used to design the sequential monitoring plan for a clinical trial

1995 ◽  
Vol 14 (20) ◽  
pp. 2227-2237 ◽  
Author(s):  
Maria Mori Brooks ◽  
Al Hallstrom ◽  
Monika Peckova
Keyword(s):  
Clinical Trial ◽  
Simulation Study ◽  
Monitoring Plan

scholarly journals Quantifying the Impact of Natural Immunity on Rotavirus Vaccine Efficacy Estimates: A Clinical Trial in Dhaka, Bangladesh (PROVIDE) and a Simulation Study

2018 ◽  
Vol 217 (6) ◽  
pp. 861-868 ◽  
Author(s):  
Elizabeth T Rogawski ◽  
James A Platts-Mills ◽  
E Ross Colgate ◽  
Rashidul Haque ◽  
K Zaman ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Simulation Study ◽  
Vaccine Efficacy ◽  
Natural Immunity ◽  
Rotavirus Vaccine ◽  
The Impact

scholarly journals Recursively Imputed Survival Trees for Extrapolating Survival Beyond Clinical Trial Follow UP: A Simulation Study

2018 ◽  
Vol 21 ◽  
pp. S223
Author(s):  
H Beyhaghi ◽  
K Hassmiller Lich ◽  
Y Cui ◽  
MR Kosorok
Keyword(s):  
Clinical Trial ◽  
Simulation Study ◽  
Survival Trees

scholarly journals Comparison of rheumatoid arthritis clinical trial outcome measures: A simulation study

2003 ◽  
Vol 48 (11) ◽  
pp. 3031-3038 ◽  
Author(s):  
Jennifer J. Anderson ◽  
James A. Bolognese ◽  
David T. Felson
Keyword(s):  
Rheumatoid Arthritis ◽  
Clinical Trial ◽  
Outcome Measures ◽  
Simulation Study ◽  
Trial Outcome ◽  
Clinical Trial Outcome

scholarly journals Methods for detection of clusters of observations with an outlying correlation coefficient value

2020 ◽  
Author(s):  
Lieven Desmet ◽  
David Venet ◽  
Laura Trotta ◽  
Tomasz Burzykowski ◽  
Marc Buyse
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Correlation Coefficient ◽  
Simulation Study ◽  
Real Life ◽  
Correlation Coefficients ◽  
Multicenter Clinical Trial ◽  
Bivariate Data ◽  
Statistical Monitoring ◽  
Central Statistical

AbstractMultivariate datasets with a clustered structure are the natural framework for, e.g., multicentre clinical trials. We propose a number of methods aimed at detecting clusters with outlying correlation coefficients. While the methods can be used in a variety of settings, we focus mainly on their application to central statistical monitoring of clinical trials. In particular, we consider the issue of detecting centers (or other clusters of patients such as regions) with outlying correlation coefficients for bivariate data in a multicenter clinical trial. It appears that, in that context, the proposed methods perform well, as we show by using a simulation study and a number of real life datasets.

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