Generating correlated discrete ordinal data using R and SAS IML

Correlated ordinal data are common in many areas of research. The data may arise from longitudinal studies in biology, medical, or clinical fields. The prominent characteristic of these data is that the within-subject observations are correlated, whilst between-subject observations are independent....

Full description

Saved in:
Bibliographic Details
Published inComputer methods and programs in biomedicine Vol. 104; no. 3; pp. e122 - e132
Main Authors Ibrahim, Noor Akma, Suliadi, Suliadi
Format Journal Article
LanguageEnglish
Published Ireland Elsevier Ireland Ltd 01.12.2011
Subjects
Algorithms
Correlated ordinal data
Data Interpretation, Statistical
Generating data
Internal Medicine
Other
Programming Languages
R language
SAS IML
Simulation study
R language
Generating data
Simulation study
Correlated ordinal data
SAS IML
Online AccessGet full text

Cover

More Information
Summary:Correlated ordinal data are common in many areas of research. The data may arise from longitudinal studies in biology, medical, or clinical fields. The prominent characteristic of these data is that the within-subject observations are correlated, whilst between-subject observations are independent. Many methods have been proposed to analyze correlated ordinal data. One way to evaluate the performance of a proposed model or the performance of small or moderate size data sets is through simulation studies. It is thus important to provide a tool for generating correlated ordinal data to be used in simulation studies. In this paper, we describe a macro program on how to generate correlated ordinal data based on R language and SAS IML.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ObjectType-Undefined-3
ISSN:0169-2607
1872-7565
1872-7565
DOI:10.1016/j.cmpb.2011.06.003