I am interested in the modeling of binary response data in paired observations. We aim to make inference about the effectiveness of a pre-post intervention in a group, potentially adjusting for several covariates and determining whether there is effect modification by a group that received particularly different training as part of an intervention.

Given data of the following form:

`id phase resp 1 pre 1 1 post 0 2 pre 0 2 post 0 3 pre 1 3 post 0 `

And a $2 times 2$ contingency table of paired response information:

begin{array}{cc|cc}

& & mbox{Pre} & \

& & mbox{Correct} & mbox{Incorrect} \ hline

mbox{Post} & mbox{Correct} & a & b&\

& mbox{Incorrect} & c& d&\

end{array}

We're interested in the test of hypothesis: $mathcal{H}_0: theta_c = 1$.

McNemar's Test gives: $Q = frac{(b-c)^2}{b+c} sim chi^2_1$ under $mathcal{H}_0$ (asymptotically). This is intuitive because, under the null, we would expect an equal proportion of the discordant pairs ($b$ and $c$) to be favoring a positive effect ($b$) or a negative effect ($c$). With the probability of positive case definition defined $p =frac{b}{b+c}$ and $n=b+c$. The odds of observing a positive discordant pair is $frac{p}{1-p}=frac{b}{c}$.

On the other hand, conditional logistic regression uses a different approach to test the same hypothesis, by maximizing the conditional likelihood:

$$mathcal{L}(X ; beta) = prod_{j=1}^n frac{exp(beta X_{j,2})}{exp(beta X_{j,1}) + exp(beta X_{j,2})}$$

where $exp(beta) = theta_c$.

So, what's the relationship between these tests? How can one do a simple test of the contingency table presented earlier? Looking at calibration of p-values from clogit and McNemar's approaches under the null, you'd think they were completely unrelated!

`library(survival) n <- 100 do.one <- function(n) { id <- rep(1:n, each=2) ph <- rep(0:1, times=n) rs <- rbinom(n*2, 1, 0.5) c( 'pclogit' = coef(summary(clogit(rs ~ ph + strata(id))))[5], 'pmctest' = mcnemar.test(table(ph,rs))$p.value ) } out <- replicate(1000, do.one(n)) plot(t(out), main='Calibration plot of pvalues for McNemar and Clogit tests', xlab='p-value McNemar', ylab='p-value conditional logistic regression') `

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#### Best Answer

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)) `

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