scholarly journals The potential of the multivariate multilevel model for analysing correlated multiple outcomes: a simulation study

Trials ◽  
2015 ◽  
Vol 16 (S2) ◽  
Author(s):  
Victoria Vickerstaff ◽  
Gareth Ambler ◽  
Rumana Z Omar
Keyword(s):  
Simulation Study ◽  
Multilevel Model ◽  
Multiple Outcomes ◽  
Multivariate Multilevel Model

scholarly journals Detecting Selection Bias in Meta-Analyses with Multiple Outcomes: A Simulation Study

2019 ◽  
Vol 89 (1) ◽  
pp. 125-144 ◽  
Author(s):  
Belén Fernández-Castilla ◽  
Lies Declercq ◽  
Laleh Jamshidi ◽  
S. Natasha Beretvas ◽  
Patrick Onghena ◽  
...  
Keyword(s):  
Selection Bias ◽  
Simulation Study ◽  
Multiple Outcomes ◽  
Meta Analyses

scholarly journals Variable selection for propensity score models when estimating treatment effects on multiple outcomes: a simulation study

2012 ◽  
Vol 22 (1) ◽  
pp. 77-85 ◽  
Author(s):  
Richard Wyss ◽  
Cynthia J. Girman ◽  
Robert J. LoCasale ◽  
M. Alan Brookhart ◽  
Til Stürmer
Keyword(s):  
Propensity Score ◽  
Variable Selection ◽  
Simulation Study ◽  
Treatment Effects ◽  
Multiple Outcomes ◽  
Selection For

A multilevel Multitrait–Multimethod Analysis of Self– and Peer–Reported Daily Affective Experiences

2011 ◽  
Vol 25 (5) ◽  
pp. 398-408 ◽  
Author(s):  
Wiebke Bleidorn ◽  
Anna–Lena Peters
Keyword(s):  
Multilevel Model ◽  
Discriminant Validity ◽  
Levels Of Analysis ◽  
Positive And Negative Affect ◽  
Convergent And Discriminant Validity ◽  
Multimethod Approach ◽  
Affective Experiences ◽  
Using Data ◽  
Multivariate Multilevel Model ◽  
Peer Reports

We examined the psychometric properties of an experience–sampling measure of affect (PANAS) using data from self– and peer reports. A multivariate multilevel model was used to assess the reliability of the latent PANAS scales at the within– and between–person level. Findings suggest satisfying internal consistencies for self– and peer reports of affective experiences at both levels of analysis. Convergent and discriminant validity of the two affect scales were examined by means of a multilevel multitrait–multimethod approach (MLM–MTMM) indicating distinct findings at the within– and between–person level. These findings provide further insights into the structural relations between the two PANAS scales: Whereas positive and negative affect were unrelated at the between–person level; they were negatively correlated at the within–person level. Copyright © 2010 John Wiley & Sons, Ltd.

Happiness and Health in Europe: A Multivariate Multilevel Model

2014 ◽  
Vol 10 (2) ◽  
pp. 237-252 ◽  
Author(s):  
Adi Cilik Pierewan ◽  
Gindo Tampubolon
Keyword(s):  
Multilevel Model ◽  
Multivariate Multilevel Model

scholarly journals Signs and customer behaviors at vape shops: Multivariate multilevel model analysis

2020 ◽  
Vol 12 ◽  
pp. 100299
Author(s):  
Jimi Huh ◽  
Leah Meza ◽  
Ellen Galstyan ◽  
Artur Galimov ◽  
Sheila Yu ◽  
...  
Keyword(s):  
Multilevel Model ◽  
Model Analysis ◽  
Multivariate Multilevel Model ◽  
Vape Shops

scholarly journals Multivariate nonparametric chart for influenza epidemic monitoring

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Liu Liu ◽  
Jin Yue ◽  
Xin Lai ◽  
Jianping Huang ◽  
Jian Zhang
Keyword(s):  
Control Chart ◽  
Process Monitoring ◽  
Simulation Study ◽  
Process Variable ◽  
Influenza Epidemic ◽  
Multiple Outcomes ◽  
Multivariate Process ◽  
Ewma Control Chart ◽  
Epidemic Monitoring ◽  
Variable Distribution

AbstractControl chart methods have been received much attentions in biosurvillance studies. The correlation between charting statistics or regions could be considerably important in monitoring the states of multiple outcomes or regions. In addition, the process variable distribution is unknown in most situations. In this paper, we propose a new nonparametric strategy for multivariate process monitoring when the distribution of a process variable is unknown. We discuss the EWMA control chart based on rank methods for a multivariate process, and the approach is completely nonparametric. A simulation study demonstrates that the proposed method is efficient in detecting shifts for multivariate processes. A real Japanese influenza data example is given to illustrate the performance of the proposed method.

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