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| = Confidence interval for paired binomial proportions = | |
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| = Confidence interval for paired binomial proportions = | McNemar's test is commonly used to test whether two correlated proportions differ. Unfortunately this test does not tprovide a confidence interval for the difference in proportions. |
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| The classical Wald statistic which is used for constructing confidence intervals produces too narrow a confidence interval when the difference in proportions is close to zero or one (Newcombe (1998)). Newcombe, instead, suggests using a modification of Wilson's (1927) method based on a single proportion. | The classical Wald statistic which is used for constructing confidence intervals for proportions produces too narrow a confidence interval when the difference in proportions is close to zero or one (Newcombe (1998)). Newcombe, instead, suggests using a modification of Wilson's (1927) method based on a single proportion. Agresti & Kin (2005) also find Wilson's method produces good coverage and also suggest an improved confidence interval may be obtained by a simple modification of the Wald statistic. These are all included in this EXCEL [[attachment:paired_pci.xls|spreadsheet.]] |
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| An EXCEL spreadsheet carrying out this calculation (with others) TO BE ADDED HERE! | * [[FAQ/BinomialCofidence/2gpp/Rcode| Some R code for the Agresti-Min approach above is also available]] |
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| __Reference__ | __References__ [[attachment:agresti.pdf|Agresti A and Min Y (2005) Simple improved confidence intervals for comparing matched proportions.]] ''Statistics in Medicine'' '''24(5)''' 729-740. |
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Lee S & Lee S-C (2007) An improved confidence interval for the population proportion in a double sampling scheme subject to false-positive misclassification, ''Journal of the Korean Statistical Society'' '''36''' 275–284. (reference for agrestic-oull method used in above spreadsheet). |
Confidence interval for paired binomial proportions
McNemar's test is commonly used to test whether two correlated proportions differ. Unfortunately this test does not tprovide a confidence interval for the difference in proportions.
The classical Wald statistic which is used for constructing confidence intervals for proportions produces too narrow a confidence interval when the difference in proportions is close to zero or one (Newcombe (1998)). Newcombe, instead, suggests using a modification of Wilson's (1927) method based on a single proportion. Agresti & Kin (2005) also find Wilson's method produces good coverage and also suggest an improved confidence interval may be obtained by a simple modification of the Wald statistic. These are all included in this EXCEL spreadsheet.
References
Agresti A and Min Y (2005) Simple improved confidence intervals for comparing matched proportions. Statistics in Medicine 24(5) 729-740.
Newcombe RG (1998) Improved confidence intervals for the difference between binomial proportions based on paired data. Statistics in Medicine 17 2635-2650.
Lee S & Lee S-C (2007) An improved confidence interval for the population proportion in a double sampling scheme subject to false-positive misclassification, Journal of the Korean Statistical Society 36 275–284. (reference for agrestic-oull method used in above spreadsheet).