Testing multiple outcomes in repeated measures designs.

Equivalence and noninferiority testing in regression models and repeated-measures designs.

Equivalence and noninferiority designs are useful when the superiority of one intervention over another is neither expected nor required. Equivalence trials test whether a difference between groups falls within a prespecified equivalence region, whereas noninferiority trials test whether a preferred intervention is either better or at least not worse than the comparator, with worse being define...

HE Plots for Repeated Measures Designs

Hypothesis error (HE) plots, introduced in Friendly (2007), provide graphical methods to visualize hypothesis tests in multivariate linear models, by displaying hypothesis and error covariation as ellipsoids and providing visual representations of effect size and significance. These methods are implemented in the heplots for R (Fox, Friendly, and Monette 2009a) and SAS (Friendly 2006), and appl...

Selecting a sample size for studies with repeated measures

Many researchers favor repeated measures designs because they allow the detection of within-person change over time and typically have higher statistical power than cross-sectional designs. However, the plethora of inputs needed for repeated measures designs can make sample size selection, a critical step in designing a successful study, difficult. Using a dental pain study as a driving example...

Aligned Rank Tests As Robust Alternatives For Testing Interactions In Multiple Group Repeated Measures Designs With Heterogeneous Covariances

Rank - based comparisons of treatments with a control for repeated measures designs

In this paper the problem of comparing several treatments with a control in a one-way repeated measures design is considered. Multiple testing procedures based on rank transformation data are proposed for determining which treatments are more effective than the control. The results of a Monte Carlo level and power study are presented.