Sufficient Sample Size and Power in Multilevel Ordinal Logistic Regression Models
Joint Authors
Ali, Sabz
Ali, Amjad
Khan, Sajjad Ahmad
Hussain, Sundas
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-09-22
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
For most of the time, biomedical researchers have been dealing with ordinal outcome variable in multilevel models where patients are nested in doctors.
We can justifiably apply multilevel cumulative logit model, where the outcome variable represents the mild, severe, and extremely severe intensity of diseases like malaria and typhoid in the form of ordered categories.
Based on our simulation conditions, Maximum Likelihood (ML) method is better than Penalized Quasilikelihood (PQL) method in three-category ordinal outcome variable.
PQL method, however, performs equally well as ML method where five-category ordinal outcome variable is used.
Further, to achieve power more than 0.80, at least 50 groups are required for both ML and PQL methods of estimation.
It may be pointed out that, for five-category ordinal response variable model, the power of PQL method is slightly higher than the power of ML method.
American Psychological Association (APA)
Ali, Sabz& Ali, Amjad& Khan, Sajjad Ahmad& Hussain, Sundas. 2016. Sufficient Sample Size and Power in Multilevel Ordinal Logistic Regression Models. Computational and Mathematical Methods in Medicine،Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1100186
Modern Language Association (MLA)
Ali, Sabz…[et al.]. Sufficient Sample Size and Power in Multilevel Ordinal Logistic Regression Models. Computational and Mathematical Methods in Medicine No. 2016 (2016), pp.1-8.
https://search.emarefa.net/detail/BIM-1100186
American Medical Association (AMA)
Ali, Sabz& Ali, Amjad& Khan, Sajjad Ahmad& Hussain, Sundas. Sufficient Sample Size and Power in Multilevel Ordinal Logistic Regression Models. Computational and Mathematical Methods in Medicine. 2016. Vol. 2016, no. 2016, pp.1-8.
https://search.emarefa.net/detail/BIM-1100186
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1100186