Applying mixed-effects modeling to single-subject designs: An introduction

2019 ◽  
Vol 111 (2) ◽  
pp. 192-206 ◽  
Author(s):  
William B. DeHart ◽  
Brent A. Kaplan
Keyword(s):  
Mixed Effects ◽  
Single Subject ◽  
Mixed Effects Modeling ◽  
Single Subject Designs

scholarly journals The ANTs Longitudinal Cortical Thickness Pipeline

2017 ◽  
Author(s):  
Nicholas J. Tustison ◽  
Andrew J. Holbrook ◽  
Brian B. Avants ◽  
Jared M. Roberts ◽  
Philip A. Cook ◽  
...  
Keyword(s):  
Confidence Intervals ◽  
Cortical Thickness ◽  
Mixed Effects ◽  
Single Subject ◽  
Multiple Time ◽  
Supporting Evidence ◽  
Cross Sectional ◽  
Mixed Effects Modeling ◽  
Multiple Time Points ◽  
Modeling Strategy

AbstractLongitudinal studies of development and disease in the human brain have motivated the acquisition of large neuroimaging data sets and the concomitant development of robust methodological and statistical tools for quantifying neurostructural changes. Longitudinal-specific strategies for acquisition and processing have potentially significant benefits including more consistent estimates of intra-subject measurements while retaining predictive power. In this work, we introduce the open-source Advanced Normalization Tools (ANTs) registration-based cortical thickness longitudinal processing pipeline and its application to the first phase of the Alzheimer’s Disease Neuroimaging Initiative (ADNI-1) comprising over 600 subjects with multiple time points from baseline to 36 months. We demonstrate in these data that the single-subject template construction and same orientation processing results in a simultaneous minimization of residual variability and maximization of between-subject variability immediately estimable from a longitudinal mixed-effects modeling strategy. It is known from the statistical literature that optimizing these dual criteria leads to greater scientific interpretability in terms of tighter confidence intervals in calculated mean trends, smaller prediction intervals, and narrower confidence intervals for determining cross-sectional effects. This strategy is evaluated over the entire cortex, as defined by the Desikan-Killiany-Tourville labeling protocol, where comparisons are made with the cross-sectional and longitudinal FreeSurfer processing streams. Subsequent linear mixed effects modeling for identifying diagnostic groupings within the ADNI cohort is provided as supporting evidence for the utility of the proposed ANTs longitudinal framework which provides unbiased structural neuroimage processing and competitive to superior power for longitudinal structural change detection.

Extremism Leads to Ostracism

2020 ◽  
Vol 51 (3) ◽  
pp. 149-156
Author(s):  
Andrew H. Hales ◽  
Kipling D. Williams
Keyword(s):  
Mixed Effects ◽  
Social Connection ◽  
Mixed Effects Modeling ◽  
Activist Groups

Abstract. Ostracism has been shown to increase openness to extreme ideologies and groups. We investigated the consequences of this openness-to-extremity from the perspective of potential ostracizers. Does openness-to-extremity increase one’s prospects of being ostracized by others who are not affiliated with the extreme group? Participants rated willingness to ostracize 40 targets who belong to activist groups that vary in the type of goals/cause they support (prosocial vs. antisocial), and the extremity of their actions (moderate vs. extreme). Mixed-effects modeling showed that people are more willing to ostracize targets whose group engages in extreme actions. This effect was unexpectedly stronger for groups pursuing prosocial causes. It appears openness-to-extremity entails interpersonal cost, and could increase reliance on the extreme group for social connection.

scholarly journals Mixed-effects modeling for analyzing land use change in the Brazilian Pantanal subregion of Cáceres

2019 ◽  
Vol 13 ◽  
pp. 408-414 ◽  
Author(s):  
Edinéia A.S. Galvanin ◽  
Raquel Menezes ◽  
Murilo H.X. Pereira ◽  
Sandra M.A.S. Neves
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Mixed Effects ◽  
Mixed Effects Modeling ◽  
Brazilian Pantanal

Rating the methodological quality of single-subject designs andn-of-1 trials: Introducing the Single-Case Experimental Design (SCED) Scale

2008 ◽  
Vol 18 (4) ◽  
pp. 385-401 ◽  
Author(s):  
Robyn L Tate ◽  
Skye Mcdonald ◽  
Michael Perdices ◽  
Leanne Togher ◽  
Regina Schultz ◽  
...  
Keyword(s):  
Experimental Design ◽  
Methodological Quality ◽  
Single Case ◽  
Single Subject ◽  
Single Subject Designs

scholarly journals Analyzing randomized controlled interventions: Three notes for applied linguists

2015 ◽  
Vol 5 (1) ◽  
pp. 135-152 ◽  
Author(s):  
Jan Vanhove
Keyword(s):  
Repeated Measures ◽  
Intervention Studies ◽  
Mixed Effects ◽  
Analysis Of Covariance ◽  
Cluster Randomization ◽  
Repeated Measures Anova ◽  
Randomized Controlled ◽  
Mixed Effects Modeling ◽  
Statistical Formalism ◽  
Relevant Covariates

I discuss three common practices that obfuscate or invalidate the statistical analysis of randomized controlled interventions in applied linguistics. These are (a) checking whether randomization produced groups that are balanced on a number of possibly relevant covariates, (b) using repeated measures ANOVA to analyze pretest-posttest designs, and (c) using traditional significance tests to analyze interventions in which whole groups were assigned to the conditions (cluster randomization). The first practice is labeled superfluous, and taking full advantage of important covariates regardless of balance is recommended. The second is needlessly complicated, and analysis of covariance is recommended as a more powerful alternative. The third produces dramatic inferential errors, which are largely, though not entirely, avoided when mixed-effects modeling is used. This discussion is geared towards applied linguists who need to design, analyze, or assess intervention studies or other randomized controlled trials. Statistical formalism is kept to a minimum throughout.

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