2022
DOI: 10.1177/09622802221085864
|View full text |Cite
|
Sign up to set email alerts
|

Generalized quasi-linear mixed-effects model

Abstract: The generalized linear mixed model (GLMM) is one of the most common method in the analysis of longitudinal and clustered data in biological sciences. However, issues of model complexity and misspecification can occur when applying the GLMM. To address these issues, we extend the standard GLMM to a nonlinear mixed-effects model based on quasi-linear modeling. An estimation algorithm for the proposed model is provided by extending the penalized quasi-likelihood and the restricted maximum likelihood which are kno… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1

Citation Types

0
4
0

Year Published

2022
2022
2025
2025

Publication Types

Select...
7

Relationship

0
7

Authors

Journals

citations
Cited by 7 publications
(4 citation statements)
references
References 42 publications
0
4
0
Order By: Relevance
“…[35][36][37][38] The P2/P1 ratios were analyzed using a mixed linear model with random effects in four groups: MV patients (COVID-19 and non-COVID-19), nonmechanically ventilated COVID-19 patients, and healthy volunteers. [39][40][41] The P2/P1 ratio was obtained from the average of all valid pulses each minute; all results outside 0.5 to 1.8 were considered artifactual and excluded.…”
Section: Discussionmentioning
confidence: 99%
“…[35][36][37][38] The P2/P1 ratios were analyzed using a mixed linear model with random effects in four groups: MV patients (COVID-19 and non-COVID-19), nonmechanically ventilated COVID-19 patients, and healthy volunteers. [39][40][41] The P2/P1 ratio was obtained from the average of all valid pulses each minute; all results outside 0.5 to 1.8 were considered artifactual and excluded.…”
Section: Discussionmentioning
confidence: 99%
“…The prognosis was compared using the Kaplan-Meier method (K-M) to display the decreased survival rate free from composite endpoints, and the difference was analyzed using the log-rank test. In addition, the generalized linear mixed-effects models (GLMMs) [14] were constructed to evaluate the association of repeatedly measured eGFR and proteinuria between two cohorts and among the different follow-up periods, and compared using analysis of variance (ANO-VA). The GLMM could better illustrate the fixed effect of the independent variable on the dependent variable when there are random effects such as individual differences.…”
Section: Discussionmentioning
confidence: 99%
“…To account for the interdependence of TEPS within each patient, we used generalized mixed models. 22 To analyze the association with…”
Section: Discussionmentioning
confidence: 99%
“…We analyzed the relationship between the PM and TEPS at each lumbar level. To account for the interdependence of TEPS within each patient, we used generalized mixed models 22 . To analyze the association with TEPS at T1, we used a univariable and multivariable linear mixed model and adjusted for relevant confounders: age, sex, body mass index (BMI), diabetes mellitus, hypertension, and smoking status.…”
Section: Methodsmentioning
confidence: 99%