Significance Testing for Correlated Binary Outcome Data

Biometrics ◽  
1988 ◽  
Vol 44 (2) ◽  
pp. 505 ◽  
Author(s):  
Bernard Rosner ◽  
Roy C. Milton
Keyword(s):  
Outcome Data ◽  
Significance Testing ◽  
Binary Outcome

Extending McNemar's Test: Estimation and Inference When Paired Binary Outcome Data Are Misclassified

Biometrics ◽  
2005 ◽  
Vol 61 (1) ◽  
pp. 287-294 ◽  
Author(s):  
Robert H. Lyles ◽  
John M. Williamson ◽  
Hung-Mo Lin ◽  
Charles M. Heilig
Keyword(s):  
Outcome Data ◽  
Binary Outcome ◽  
Mcnemar’S Test ◽  
Mcnemar's Test

scholarly journals Synthesis of Binary Outcome Data on the Log-Odds and Absolute Risk Scales – A Case Study in Chronic Hepatitis C

2016 ◽  
Vol 19 (7) ◽  
pp. A361-A362
Author(s):  
L Nagy ◽  
G Kay ◽  
C Parker ◽  
A Padhiar ◽  
JM O'Rourke ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis C ◽  
Chronic Hepatitis C ◽  
Absolute Risk ◽  
Outcome Data ◽  
Binary Outcome ◽  
Log Odds

scholarly journals Modeling missing binary outcome data in a successful web-based smokeless tobacco cessation program

Addiction ◽  
2010 ◽  
Vol 105 (6) ◽  
pp. 1005-1015 ◽  
Author(s):  
Keith Smolkowski ◽  
Brian G. Danaher ◽  
John R. Seeley ◽  
Derek B. Kosty ◽  
Herbert H. Severson
Keyword(s):  
Smokeless Tobacco ◽  
Tobacco Cessation ◽  
Outcome Data ◽  
Binary Outcome ◽  
Web Based

scholarly journals A Comparison of Some Approximate Confidence Intervals for a Single Proportion for Clustered Binary Outcome Data

2016 ◽  
Vol 12 (2) ◽  
Author(s):  
Krishna K. Saha ◽  
Daniel Miller ◽  
Suojin Wang
Keyword(s):  
Biomedical Applications ◽  
Profile Likelihood ◽  
Interval Estimation ◽  
Estimating Equation ◽  
Outcome Data ◽  
Biomedical Data ◽  
Ratio Estimator ◽  
Binary Outcome ◽  
Complex Survey ◽  
Complex Survey Data

AbstractInterval estimation of the proportion parameter in the analysis of binary outcome data arising in cluster studies is often an important problem in many biomedical applications. In this paper, we propose two approaches based on the profile likelihood and Wilson score. We compare them with two existing methods recommended for complex survey data and some other methods that are simple extensions of well-known methods such as the likelihood, the generalized estimating equation of Zeger and Liang and the ratio estimator approach of Rao and Scott. An extensive simulation study is conducted for a variety of parameter combinations for the purposes of evaluating and comparing the performance of these methods in terms of coverage and expected lengths. Applications to biomedical data are used to illustrate the proposed methods.

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