scholarly journals ORTH: R and SAS software for regression models of correlated binary data based on orthogonalized residuals and alternating logistic regressions

2014 ◽  
Vol 113 (2) ◽  
pp. 557-568 ◽  
Author(s):  
Kunthel By ◽  
Bahjat F. Qaqish ◽  
John S. Preisser ◽  
Jamie Perin ◽  
Richard C. Zink
Keyword(s):  
Binary Data ◽  
Regression Models ◽  
Correlated Binary Data ◽  
Logistic Regressions

Latent Model for Correlated Binary Data with Diagnostic Error

Biometrics ◽  
1999 ◽  
Vol 55 (4) ◽  
pp. 1232-1235 ◽  
Author(s):  
Joanna H. Shih ◽  
Paul S. Albert
Keyword(s):  
Binary Data ◽  
Diagnostic Error ◽  
Correlated Binary Data

Exact methods of testing the homogeneity of prevalences for correlated binary data

2017 ◽  
Vol 87 (15) ◽  
pp. 3021-3039
Author(s):  
Xiaobin Liu ◽  
Zhengyu Yang ◽  
Song Liu ◽  
Chang-Xing Ma
Keyword(s):  
Binary Data ◽  
Exact Methods ◽  
Correlated Binary Data

scholarly journals Aberrant Crypt Foci and Semiparametric Modeling of Correlated Binary Data

Biometrics ◽  
2008 ◽  
Vol 64 (2) ◽  
pp. 490-500 ◽  
Author(s):  
Tatiyana V. Apanasovich ◽  
David Ruppert ◽  
Joanne R. Lupton ◽  
Natasa Popovic ◽  
Nancy D. Turner ◽  
...  
Keyword(s):  
Binary Data ◽  
Aberrant Crypt Foci ◽  
Correlated Binary Data ◽  
Semiparametric Modeling

scholarly journals Homogeneity Test of Many-to-One Risk Differences for Correlated Binary Data under Optimal Algorithms

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-29
Author(s):  
Keyi Mou ◽  
Zhiming Li
Keyword(s):  
Binary Data ◽  
Estimation Error ◽  
Control Group ◽  
Homogeneity Test ◽  
Optimal Algorithms ◽  
Score Statistic ◽  
Correlated Binary Data ◽  
Treatment Groups ◽  
Multiple Treatment ◽  
Rate Score

In clinical studies, it is important to investigate the effectiveness of different therapeutic designs, especially, multiple treatment groups to one control group. The paper mainly studies homogeneity test of many-to-one risk differences from correlated binary data under optimal algorithms. Under Donner’s model, several algorithms are compared in order to obtain global and constrained MLEs in terms of accuracy and efficiency. Further, likelihood ratio, score, and Wald-type statistics are proposed to test whether many-to-one risk differences are equal based on optimal algorithms. Monte Carlo simulations show the performance of these algorithms through the total averaged estimation error, SD, MSE, and convergence rate. Score statistic is more robust and has satisfactory power. Two real examples are given to illustrate our proposed methods.

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