An efficient and exact approach for detecting trends with binary endpoints
Document Type
Article
Publication Date
1-29-2012
Publication Title
Statistics in Medicine
Volume
31
Issue
2
First page number:
155
Last page number:
164
Abstract
Lloyd (Aust. Nz. J. Stat., 50, 329-345, 2008) developed an exact testing approach to control for nuisance parameters, which was shown to be advantageous in testing for differences between two population proportions. We utilized this approach to obtain unconditional tests for trends in 2 × K contingency tables. We compare the unconditional procedure with other unconditional and conditional approaches based on the well-known Cochran-Armitage test statistic. We give an example to illustrate the approach, and provide a comparison between the methods with regards to type I error and power. The proposed procedure is preferable because it is less conservative and has superior power properties.
Keywords
Clinical trials; Clinical trials—Methodology; Clinical trials--Statistical methods; Clinical Trials as Topic/methods; Clinical Trials as Topic/statistics & numerical data; Cochran–Armitage test; Confidence intervals; Contingency tables; Data Interpretation; Statistical; Dose-Response Relationship; Drug; Drugs--Dose-response relationship; Exact tests; E + M p-value; Humans; Men; Models; Statistical; Nuisance parameters; Translating and interpreting--Data processing; Unconditional test; Women
Disciplines
Medicine and Health Sciences | Statistical Methodology | Statistical Models | Statistics and Probability | Vital and Health Statistics
Language
English
Permissions
Use Find in Your Library, contact the author, or interlibrary loan to garner a copy of the item. Publisher policy does not allow archiving the final published version. If a post-print (author's peer-reviewed manuscript) is allowed and available, or publisher policy changes, the item will be deposited.
Repository Citation
Shan, G.,
Ma, C.,
Hutson, A. D.,
Wilding, G. E.
(2012).
An efficient and exact approach for detecting trends with binary endpoints.
Statistics in Medicine, 31(2),
155-164.