A Comparison of Procedures for the Analysis of Multivariate Repeated Measurements

Lix, Lisa M.; Lloyd, Anita

doi:10.22237/jmasm/1193889840

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…However, if the covariance structure is incorrectly specified, then bias will exist in the type I error rate and erroneous inferences may result. As a reviewer pointed out (see also Fitzmaurice, Laird, & Ware, 2004; Lix & Lloyd, 2006), this is the classic tradeoff between bias and precision.…”

Section: Discussionmentioning

confidence: 99%

Power Differences Between the Modified Brown-Forsythe and Mixed-Model Approaches in Repeated Measures Designs

2007

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Abstract. This article compares the sensibility of the modified Brown-Forsythe (MBF) approach developed by Vallejo and Ato (2006) and a modified empirical generalized least squares (EGLS) method adjusted by the Kenward-Roger solution available in the SAS Institute's (2002) Proc Mixed program to detect the presence of an interaction effect under departures from covariance homogeneity and multivariate normality. Although none of the approaches demonstrated superior performance in all situations, our results indicate that the so-called EGLS method, based on the Akaike's Information Criterion or based on always assuming a unstructured between-subjects heterogeneous covariance pattern, was the most powerful alternative. Results also indicate that little power can be achieved with the EGLS method if the covariance matrix is specified correctly.

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Section: Discussionmentioning

confidence: 99%

Power Differences Between the Modified Brown-Forsythe and Mixed-Model Approaches in Repeated Measures Designs

2007

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“…An application with data from an educational survey can be seen in Filiz ( 2003 ). Three different methods to solve the lack of variance homogeneity are studied in Lix and Lloyd ( 2007 ). Finally, a new statistic based on DMM is developed in Hirunkasi and Chongcharoen ( 2011 ) for the tricky scenario where the dimension of the response variable is greater than the number of observations.…”

Section: Model Set Upmentioning

confidence: 99%

Basis expansion approaches for functional analysis of variance with repeated measures

Acal

Aguilera

2022

Adv Data Anal Classif

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The methodological contribution in this paper is motivated by biomechanical studies where data characterizing human movement are waveform curves representing joint measures such as flexion angles, velocity, acceleration, and so on. In many cases the aim consists of detecting differences in gait patterns when several independent samples of subjects walk or run under different conditions (repeated measures). Classic kinematic studies often analyse discrete summaries of the sample curves discarding important information and providing biased results. As the sample data are obviously curves, a Functional Data Analysis approach is proposed to solve the problem of testing the equality of the mean curves of a functional variable observed on several independent groups under different treatments or time periods. A novel approach for Functional Analysis of Variance (FANOVA) for repeated measures that takes into account the complete curves is introduced. By assuming a basis expansion for each sample curve, two-way FANOVA problem is reduced to Multivariate ANOVA for the multivariate response of basis coefficients. Then, two different approaches for MANOVA with repeated measures are considered. Besides, an extensive simulation study is developed to check their performance. Finally, two applications with gait data are developed.

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“…In cases where the covariance matrix is misspecified, ELGS yields higher Type I error rates and wrong inferences. In this case, it is necessary to maintain a compromise between bias and precision (for more information, check Fitzmaurice, Laird, & Ware, 2004; Lix & Lloyd, 2006).…”

Section: Description Of the Procedures To Be Compared In This Studymentioning

confidence: 99%

Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs

et al. 2017

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Abstract. Low precision of the inferences of data analyzed with univariate or multivariate models of the Analysis of Variance (ANOVA) in repeated-measures design is associated to the absence of normality distribution of data, nonspherical covariance structures and free variation of the variance and covariance, the lack of knowledge of the error structure underlying the data, and the wrong choice of covariance structure from different selectors. In this study, levels of statistical power presented the Modified Brown Forsythe (MBF) and two procedures with the Mixed-Model Approaches (the Akaike’s Criterion, the Correctly Identified Model [CIM]) are compared. The data were analyzed using Monte Carlo simulation method with the statistical package SAS 9.2, a split-plot design, and considering six manipulated variables. The results show that the procedures exhibit high statistical power levels for within and interactional effects, and moderate and low levels for the between-groups effects under the different conditions analyzed. For the latter, only the Modified Brown Forsythe shows high level of power mainly for groups with 30 cases and Unstructured (UN) and Autoregressive Heterogeneity (ARH) matrices. For this reason, we recommend using this procedure since it exhibits higher levels of power for all effects and does not require a matrix type that underlies the structure of the data. Future research needs to be done in order to compare the power with corrected selectors using single-level and multilevel designs for fixed and random effects.

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A Comparison of Procedures for the Analysis of Multivariate Repeated Measurements

Cited by 5 publications

References 38 publications

Power Differences Between the Modified Brown-Forsythe and Mixed-Model Approaches in Repeated Measures Designs

Power Differences Between the Modified Brown-Forsythe and Mixed-Model Approaches in Repeated Measures Designs

Basis expansion approaches for functional analysis of variance with repeated measures

Power of Modified Brown-Forsythe and Mixed-Model Approaches in Split-Plot Designs

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