scholarly journals Measure of departure from average marginal homogeneity for the analysis of collapsed ordinal square contingency tables

2021 ◽  
Vol 58 (1) ◽  
pp. 81-94
Author(s):  
Mana Aizawa ◽  
Kouji Yamamoto ◽  
Sadao Tomizawa

Summary In clinical research, collected data are often classified into ordered categories using a set threshold to evaluate efficacy and safety of treatment. Data can be summarized as a shift table, which displays the change in the frequency of subjects across specified categories from the baseline to post-baseline. Although ordered categories are sometimes recombined into three categories, the combined patterns vary. To consider various collapsed patterns comprehensively, this paper proposes a new measure that represents the degree of departure from average marginal homogeneity, and can distinguish between two kinds of marginal inhomogeneity. Additionally, applications of the proposed measure to clinical data are discussed.

1993 ◽  
Vol 43 (1-2) ◽  
pp. 123-126 ◽  
Author(s):  
Sadao Tomizawa

For square contingency tables, with ordered categories, this short note decomposes the marginal homogeneity (MH) model into an extended MH model and the model of equality of expectation of monotonic function of row and column variables. This decomposition is a generalization of the decomposition of the MH model which was earlier considered by Tomizawa (1991, Cal. Statist. Assoc. Bull., 41, 201-207).


2016 ◽  
Vol 36 (2) ◽  
Author(s):  
Kouji Yamamoto ◽  
Sadao Tomizawa

For the analysis of square contingency tables with ordered categories, Tomizawa et al. (2003) considered a measure to represent the degree of departure from marginal homogeneity (MH). Tomizawa (1993) considered an extended marginal homogeneity (EMH) model. This paper (i) proposes a measure to represent the degree of departure from EMH, (ii) proposes a measure from equality of marginal means (E), and (iii) gives a theorem that the value of measure for MH is equal to the sum of the value of measure for EMH and that for E.


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