Bliese, Paul D LTC USAMH [EMAIL PROTECTED] writes:
Does anyone know of another version of the Mcnemar test that provides:
1. Odds Ratios
2. 95% Confidence intervals of the Odds Ratios
3. Sample probability
4. 95% Confidence intervals of the sample probability
Obviously the Odds Ratios and Sample probabilities are easy to calculate
from the contingency table, but I would appreciate any help on how to
calculate the confidence intervals.
Below is a simple example of the test, and the corresponding output with
the function mcnemar.test.
xtabs(~PLC50.T1+PLC50.T2,data=LANCET.DAT)
PLC50.T2
PLC50.T1 0 1
0 464 22
1 6 1
mcnemar.test(xtabs(~PLC50.T1+PLC50.T2,data=LANCET.DAT))
McNemar's Chi-squared test with continuity correction
data: xtabs(~PLC50.T1 + PLC50.T2, data = LANCET.DAT)
McNemar's chi-squared = 8.0357, df = 1, p-value = 0.004586
What is the sample probability in this context? The odds ratio is a
simple functional of the off-diagonal elements, and the conditional
distribution of those given their sum is just a binomial, so you can
use prop.test or binom.test to get estimate and confidence intervals
for the probability parameter and convert that to odds.
E.g.
prop.test(6,28)
1-sample proportions test with continuity correction
data: 6 out of 28, null probability 0.5
X-squared = 8.0357, df = 1, p-value = 0.004586
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
0.09027927 0.41462210
sample estimates:
p
0.2142857
ci.p - prop.test(22,28)$conf
ci.odds - ci.p/(1-ci.p)
ci.odds
[1] 1.411835 10.076740
attr(,conf.level)
[1] 0.95
ci.p - binom.test(22,28)$conf
ci.odds - ci.p/(1-ci.p)
ci.odds
[1] 1.441817 11.053913
attr(,conf.level)
[1] 0.95
--
O__ Peter Dalgaard Blegdamsvej 3
c/ /'_ --- Dept. of Biostatistics 2200 Cph. N
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907
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