Sample size calculation for multicenter randomized trial: Taking the center effect into account

2007 ◽  
Vol 28 (4) ◽  
pp. 451-458 ◽  
Author(s):  
Emilie Vierron ◽  
Bruno Giraudeau
Keyword(s):  
Randomized Trial ◽  
Sample Size ◽  
Sample Size Calculation ◽  
Multicenter Randomized Trial ◽  
Center Effect

Sample size calculations for group randomized trials with unequal group sizes through Monte Carlo simulations

2016 ◽  
Vol 27 (9) ◽  
pp. 2569-2580 ◽  
Author(s):  
Yang Shi ◽  
Ji-Hyun Lee
Keyword(s):  
Monte Carlo ◽  
Randomized Trial ◽  
Sample Size ◽  
Randomized Trials ◽  
Intraclass Correlation ◽  
Sample Size Calculation ◽  
Model Framework ◽  
Mixed Effect ◽  
Group Randomized Trials ◽  
Group Randomized Trial

Group randomized trial design is common in cancer prevention and health promotion research with intervention development. Several methods have been developed to handle the design and analytical issues for group randomized trial including the intraclass correlation coefficient. The widely used methods for the sample size calculation for the group randomized trial assume equal sizes across groups. In practice this assumption often fails and group randomized trial studies suffer from considerably lower statistical power than as planned. A few studies have developed sample size calculation methods for unequal group sizes, but most of them are limited to continuous outcomes. In this study, we develop a method for sample size calculation for group randomized trial studies with unequal group sizes based on Monte Carlo simulation in the mixed effect model framework. This approach incorporates the variation of group sizes and can be applied to group randomized trials with different types of outcomes. Further, it is easy to implement and can be applied to most commonly used group randomized trial designs such as pre-and-post cross-sectional and cohort study designs. We demonstrate the application of the proposed approach to two-arm group randomized trial studies with continuous and binary outcomes through simulations and analysis of a real group randomized trial dataset.

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