Paper Info
Title
Sample size requirements to detect an intervention by time interaction in longitudinal cluster randomized clinical trials with random slopes.
Abstract
In longitudinal cluster randomized clinical trials (cluster-RCT), subjects are nested within a higher level unit such as clinics and are evaluated for outcome repeatedly over the study period. This study design results in a three level hierarchical data structure. When the primary goal is to test the hypothesis that an intervention has an effect on the rate of change in the outcome over time and the between-subject variation in slopes is substantial, the subject-specific slopes are often modeled as random coefficients in a mixed-effects linear model. In this paper, we propose approaches for determining the samples size for each level of a 3-level hierarchical trial design based on ordinary least squares (OLS) estimates for detecting a difference in mean slopes between two intervention groups when the slopes are modeled as random. Notably, the sample size is not a function of the variances of either the second or the third level random intercepts and depends on the number of second and third level data units only through their product. Simulation results indicate that the OLS-based power and sample sizes are virtually identical to the empirical maximum likelihood based estimates even with varying cluster sizes. Sample sizes for random versus fixed slope models are also compared. The effects of the variance of the random slope on the sample size determinations are shown to be enormous. Therefore, when between-subject variations in outcome trends are anticipated to be significant, sample size determinations based on a fixed slope model can result in a seriously underpowered study.
Year
DOI
Venue
2013
10.1016/j.csda.2012.11.016
Computational Statistics & Data Analysis
Keywords
Field
DocType
level hierarchical data structure,clinical trial,effect size,sample size determination,longitudinal cluster rct,random slope,higher level unit,random coefficient,sample size,longitudinal cluster,three level data,level random intercept,fixed slope model,power,between-subject variation,sample size requirement,time interaction,level data unit,biomedical research,bioinformatics
Econometrics,Momentum (technical analysis),Linear model,Ordinary least squares,Randomized controlled trial,Maximum likelihood,Statistics,Hierarchical database model,Sample size determination,Mathematics
Journal
Volume
ISSN
Citations 
60
0167-9473
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Moonseong Heo120.74
Xiaonan Xue241.41
Mimi Y Kim300.34