Regression analysis of intelligent education based on linear mixed effect model

2021 ◽  
Author(s):  
Haiyan Zhou ◽  
Sai Jiang ◽  
Xiaojing Liu
Keyword(s):  
Regression Analysis ◽  
Linear Mixed Effect Model ◽  
Mixed Effect Model ◽  
Mixed Effect ◽  
Linear Mixed Effect ◽  
Effect Model

scholarly journals Prediction of the herd somatic cell count of the following month using a linear mixed effect model

2010 ◽  
Vol 93 (1) ◽  
pp. 234-241 ◽  
Author(s):  
J.J. Lievaart ◽  
H.W. Barkema ◽  
J. van den Broek ◽  
J.A.P. Heesterbeek ◽  
W.D.J. Kremer
Keyword(s):  
Somatic Cell ◽  
Cell Count ◽  
Somatic Cell Count ◽  
Linear Mixed Effect Model ◽  
Mixed Effect Model ◽  
Mixed Effect ◽  
Linear Mixed Effect ◽  
Effect Model

scholarly journals Nintedanib in idiopathic and secondary pleuroparenchymal fibroelastosis

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Mouhamad Nasser ◽  
Salim Si-Mohamed ◽  
Ségolène Turquier ◽  
Julie Traclet ◽  
Kaïs Ahmad ◽  
...  
Keyword(s):  
Drug Therapy ◽  
Lung Function ◽  
Linear Mixed Effect Model ◽  
Mixed Effect Model ◽  
Mixed Effect ◽  
Lung Volumes ◽  
Pleuroparenchymal Fibroelastosis ◽  
Linear Mixed Effect ◽  
Effect Model ◽  
Relative Decline

Abstract Background Pleuroparenchymal fibroelastosis (PPFE) has a variable disease course with dismal prognosis in the majority of patients with no validated drug therapy. This study is to evaluate the effect of nintedanib in patients with idiopathic and secondary PPFE. Patients admitted to a tertiary care center (2010–2019) were included into this retrospective analysis if they had a multidisciplinary diagnosis of PPFE, had been followed-up for 3 months or more, and had lung function tests and chest CTs available for review. Changes in pulmonary function tests were assessed using non-parametric tests and linear mixed effect model. Lung volumes were measured with lobar segmentation using chest CT. Results Out of 21 patients with PPFE, nine had received nintedanib, six had received another treatment and another six patients were monitored without drug therapy. Annual FVC (% of predicted) relative decline was − 13.6 ± 13.4%/year before nintedanib and − 1.6 ± 6.02%/year during nintedanib treatment (p = 0.014), whereas no significant change in FVC% relative decline was found in patients receiving another treatment (− 13.25 ± 34 before vs − 16.61 ± 36.2%/year during treatment; p = 0.343). Using linear mixed effect model, the slope in FVC was − 0.97%/month (95% CI: − 1.42; − 0.52) before treatment and − 0.50%/month (95% CI: − 0.88; 0.13) on nintedanib, with a difference between groups of + 0.47%/month (95% CI: 0.16; 0.78), p = 0.004. The decline in the upper lung volumes measured by CT was − 233 mL/year ± 387 mL/year before nintedanib and − 149 mL/year ± 173 mL/year on nintedanib (p = 0.327). Nintedanib tolerability was unremarkable. Conclusion In patients with PPFE, nintedanib treatment might be associated with slower decline in lung function, paving the way for prospective, controlled studies.

Using the Linear Mixed-Effect Model Framework to Estimate Generalizability Variance Components in R

Methodology ◽  
2018 ◽  
Vol 14 (3) ◽  
pp. 133-142 ◽  
Author(s):  
Zhehan Jiang
Keyword(s):  
Variance Components ◽  
Classical Test Theory ◽  
Test Theory ◽  
Linear Mixed Effect Model ◽  
Mixed Effect Model ◽  
Model Framework ◽  
Mixed Effect ◽  
Linear Mixed Effect ◽  
Mixed Effect Models ◽  
Effect Model

Abstract. Extending from classical test theory, G theory allows more sources of variations to be investigated and therefore provides the accuracy of generalizing observed scores to a broader universe. However, G theory has been used less due to the absence of analytic facilities for this purpose in popular statistical software packages. Besides, there is rarely a systematic G theory introduction in the linear mixed-effect model context, which is a widely taught technique in statistical analysis curricula. The present paper fits G theory into linear mixed-effect models and estimates the variance components via the well-known lme4 package in R. Concrete examples, modeling procedures, and R syntax are illustrated so that practitioners may use G theory for their studies. Realizing the G theory estimation in R provides more flexible features than other platforms, such that users need not rely on specialized software such as GENOVA and urGENOVA.

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