scholarly journals Regression analysis for differentially misclassified correlated binary outcomes

2014 ◽  
Vol 64 (3) ◽  
pp. 433-449 ◽  
Author(s):  
Li Tang ◽  
Robert H. Lyles ◽  
Caroline C. King ◽  
Joseph W. Hogan ◽  
Yungtai Lo
Keyword(s):  
Regression Analysis ◽  
Binary Outcomes ◽  
Correlated Binary Outcomes

Modeling Correlated Binary Outcomes with Time-Dependent Covariates

2021 ◽  
Vol 11 (4) ◽  
pp. 715-738 ◽  
Author(s):  
Trent L. Lalonde ◽  
Anh Q. Nguyen ◽  
Jianqiong Yin ◽  
Kyle Irimata ◽  
Jeffrey R. Wilson
Keyword(s):  
Time Dependent ◽  
Binary Outcomes ◽  
Time Dependent Covariates ◽  
Correlated Binary Outcomes

scholarly journals A Unifying Framework for Marginalised Random-Intercept Models of Correlated Binary Outcomes

2013 ◽  
Vol 82 (2) ◽  
pp. 275-295 ◽  
Author(s):  
Bruce J. Swihart ◽  
Brian S. Caffo ◽  
Ciprian M. Crainiceanu
Keyword(s):  
Binary Outcomes ◽  
Random Intercept ◽  
Correlated Binary Outcomes

scholarly journals Decision-making with multiple correlated binary outcomes in clinical trials

2020 ◽  
Vol 29 (11) ◽  
pp. 3265-3277
Author(s):  
Xynthia Kavelaars ◽  
Joris Mulder ◽  
Maurits Kaptein
Keyword(s):  
Decision Making ◽  
Clinical Trials ◽  
Decision Criterion ◽  
Error Rates ◽  
Binary Outcomes ◽  
Compensatory Mechanism ◽  
Type I ◽  
Positive Effects ◽  
Multiple Outcome ◽  
Correlated Binary Outcomes

Clinical trials often evaluate multiple outcome variables to form a comprehensive picture of the effects of a new treatment. The resulting multidimensional insight contributes to clinically relevant and efficient decision-making about treatment superiority. Common statistical procedures to make these superiority decisions with multiple outcomes have two important shortcomings, however: (1) Outcome variables are often modeled individually, and consequently fail to consider the relation between outcomes; and (2) superiority is often defined as a relevant difference on a single, on any, or on all outcome(s); and lacks a compensatory mechanism that allows large positive effects on one or multiple outcome(s) to outweigh small negative effects on other outcomes. To address these shortcomings, this paper proposes (1) a Bayesian model for the analysis of correlated binary outcomes based on the multivariate Bernoulli distribution; and (2) a flexible decision criterion with a compensatory mechanism that captures the relative importance of the outcomes. A simulation study demonstrates that efficient and unbiased decisions can be made while Type I error rates are properly controlled. The performance of the framework is illustrated for (1) fixed, group sequential, and adaptive designs; and (2) non-informative and informative prior distributions.

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