The McNemar test for binary matched-pairs data: mid-p and asymptotic are better than exact conditional

被引:266
作者
Fagerland, Morten W. [1 ]
Lydersen, Stian [2 ]
Laake, Petter [3 ]
机构
[1] Oslo Univ Hosp, Unit Biostat & Epidemiol, Oslo, Norway
[2] Norwegian Univ Sci & Technol, Reg Ctr Child & Youth Mental Hlth & Child Welfare, N-7034 Trondheim, Norway
[3] Univ Oslo, Dept Biostat, Oslo, Norway
关键词
Matched pairs; Dependent proportions; Paired proportions; Quasi-exact; CONFIDENCE-INTERVALS; ASSOCIATION; PROPORTIONS;
D O I
10.1186/1471-2288-13-91
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background: Statistical methods that use the mid-p approach are useful tools to analyze categorical data, particularly for small and moderate sample sizes. Mid-p tests strike a balance between overly conservative exact methods and asymptotic methods that frequently violate the nominal level. Here, we examine a mid-p version of the McNemar exact conditional test for the analysis of paired binomial proportions. Methods: We compare the type I error rates and power of the mid-p test with those of the asymptotic McNemar test (with and without continuity correction), the McNemar exact conditional test, and an exact unconditional test using complete enumeration. We show how the mid-p test can be calculated using eight standard software packages, including Excel. Results: The mid-p test performs well compared with the asymptotic, asymptotic with continuity correction, and exact conditional tests, and almost as good as the vastly more complex exact unconditional test. Even though the mid-p test does not guarantee preservation of the significance level, it did not violate the nominal level in any of the 9595 scenarios considered in this article. It was almost as powerful as the asymptotic test. The exact conditional test and the asymptotic test with continuity correction did not perform well for any of the considered scenarios. Conclusions: The easy-to-calculate mid-p test is an excellent alternative to the complex exact unconditional test. Both can be recommended for use in any situation. We also recommend the asymptotic test if small but frequent violations of the nominal level is acceptable.
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