Abstract
Based on maximum likelihood estimates obtained from mixed-effects linear models, closed-form power functions are derived to detect two-way and three-way interactions that involve longitudinal course of outcome over time in clinical trials. Sample size estimates are shown to decrease with increasing within-subject correlations. It is further shown that when clinical trial designs are balanced in group sizes, the sample size required to detect an effect size for a three-way interaction is exactly fourfold that required to detect the same effect size of a two-way interaction. Furthermore, this fourfold relationship virtually holds for unbalanced allocations of subjects if one factor is balanced in the three-way interaction model. Simulations are presented that verify the sample size estimates for two-way and three-way interactions.
Original language | English (US) |
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Pages (from-to) | 787-802 |
Number of pages | 16 |
Journal | Journal of Biopharmaceutical Statistics |
Volume | 20 |
Issue number | 4 |
DOIs | |
State | Published - Jul 2010 |
Keywords
- Clinical trials
- Effect size
- Power function
- Sample size requirements
- Three-way interaction
- Two-way interaction
ASJC Scopus subject areas
- Statistics and Probability
- Pharmacology
- Pharmacology (medical)