I recently came across a paper that uses the McNemar test to evaluate the effectiveness of an intervention to improve adherence to treatment. The study used a pre/post test design whereby the adherence behaviour for both the control and intervention group was measured using the same instrument and expressed as continuous data. It then compares the relative improvement (post / pre) between the two groups. I've never seen the McNemar test used for this before, but am not sufficiently familiar with statistics. Is it really an appropriate method? Wouldn't a (paired) t-test or something like that make more sense?
Solved – Is McNemar’s Test appropriate for evaluating pre-post differences in intervention studies with a control arm
hypothesis testingmcnemar-test
Related Solutions
Sorry, it's an old issue, I came across this by chance.
There is a mistake in your code for the mcnemar test. Try with:
n <- 100
do.one <- function(n) {
id <- rep(1:n, each=2)
case <- rep(0:1, times=n)
rs <- rbinom(n*2, 1, 0.5)
c(
'pclogit' = coef(summary(clogit(case ~ rs + strata(id))))[5],
'pmctest' = mcnemar.test(table(rs[case == 0], rs[case == 1]))$p.value
)
}
out <- replicate(1000, do.one(n))
I compared the results I get with my function with this quick look-up table I found from StatsToDo (https://www.statstodo.com/SSizMcNemar_Tab.php#). I didn't try all combinations, but for the handful I did try, the sample size estimates were the exact same. I am pretty confident that the function is correct. Feel free though to add further comments
Best Answer
Within either the treatment or the control group the McNemar test seems perfectly appropriate to test marginal homogeneity of the pre/post vs adherent/non-adherent contingency table - it's a matched-pair design for dichotomous variables. I can't see how you'd use it to compare treatments vs controls, as they're unmatched, presumably. It's not clear from your description what tests were actually carried out in this study.