Marginalized models for longitudinal ordinal data with nonignorable dropout

2019 ◽  
Vol 30 (2) ◽  
pp. 479-490
Author(s):  
Keumbaik lee
Keyword(s):  
Ordinal Data ◽  
Nonignorable Dropout ◽  
Marginalized Models

scholarly journals Flexible marginalized models for bivariate longitudinal ordinal data

Biostatistics ◽  
2013 ◽  
Vol 14 (3) ◽  
pp. 462-476 ◽  
Author(s):  
K. Lee ◽  
M. J. Daniels ◽  
Y. Joo
Keyword(s):  
Ordinal Data ◽  
Marginalized Models

A Multivariate Approach to Analyze Ordinal Data Collected in Clinical Trials of Analgesic Agents

1979 ◽  
Vol 18 (03) ◽  
pp. 175-179
Author(s):  
E. Mabubini ◽  
M. Rainisio ◽  
V. Mandelli
Keyword(s):  
Clinical Trials ◽  
Multivariate Analysis ◽  
Analgesic Effect ◽  
Ordinal Data ◽  
Ordinal Scale ◽  
Multivariate Approach ◽  
Controlled Clinical Trials ◽  
Second Stage ◽  
Analgesic Agents ◽  
Relevant Factors

After pointing out the drawbacks of the approach commonly used to analyze the data collected in controlled clinical trials carried out to evaluate the analgesic effect of potential agents, the authors suggest a procedure suitable for analyzing data coded according to an ordinal scale. In the first stage a multivariate analysis is carried out on the codec! data and the projection of each result in the space of the most relevant factors is obtained. In the second stage the whole set of these values is processed by distribution-free tests. The procedure has been applied to data previously published by VENTAITBIDDA et al. [18].

Computing Min and Max Scorings for Two-Sample Ordinal Data.

1996 ◽  
Author(s):  
Jyn R. Whitaker ◽  
Michael D. Whitaker
Keyword(s):  
Ordinal Data
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