Sufficient sample size and power in multilevel ordinal logistic regression models
type: Item
instance of: scholarly article
title: Sufficient sample size and power in multilevel ordinal logistic regression models
zbMATH Open document ID: 1367.92007
DOI: 10.1155/2016/7329158
author: Sabz Ali
author: Amjad Ali
author: Sajjad Ahmad Khan
author: Sundas Hussain
published in: Computational \& Mathematical Methods in Medicine
publication date: 2017-08-03T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 92B15
Mathematics Subject Classification ID: 62J05
Mathematics Subject Classification ID: 62P10
zbMATH DE Number: 6756156
zbMATH Keywords: multilevel ordinal logistic regression model
zbMATH Keywords: maximum likelihood method
zbMATH Keywords: penalized quasilikelihood
Wikidata QID: Q37322836
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/7329158
OpenAlex ID: W2521871405
cites work: Q4530353
cites work: Q4382643
cites work: Q3997739
cites work: Q4933448
cites work: Q3998435
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DBLP publication ID: journals/cmmm/AliAKH16
description: scientific article; zbMATH DE number 6756156