I'm trying to compare the prevalence of a specific lesion (binary) at the symptomatic side to the asymptomatic side within a group of patients.
I've already performed a McNemar test to compare the prevalence at the symptomatic versus asymptomatic side within patients.
However, I'm asked to also perform a conditional logistic regression. I'm not sure if my syntax is correct with respect to the stratification:
summary(clogit(ds$symp ~ ds$asymp, strata(ds$ID), data=ds, method = "exact"))
Question: Does R compare both sides of the patient (symptomatic vs asymptomatic) within the patient(s)? Or do I have to duplicate manually the patient ID (one ID for the symptomatic side AND one ID for the asymptomatic side)?
An example:
ID symp asymp
1 0 0
2 1 0
3 0 0
4 0 0
5 1 0
6 1 1
7 0 0
8 0 0
9 0 1
10 0 0
As an example: patient 2 has a lesion at the symptomatic side and patient 9 only at the asymptomatic side. Patients 6 at both sides.
A Exact McNemar test shows:
test <- table(df$symp, df$asymp)
compare <- exact2x2(test, paired = TRUE, alternative = "two.sided", tsmethod = "central")
print(compare)
Exact McNemar test (with central confidence intervals)
data: test
b = 1, c = 2, p-value = 1
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.00847498 9.60452988
sample estimates:
odds ratio
0.5
However, a conditional logistic regression model shows:
> summary(clogit(df$symp ~ df$asymp, strata(df$ID), data=df, method = "exact"))
Call:
coxph(formula = Surv(rep(1, 10L), df$symp) ~ df$asymp, data = df,
method = "exact")
n= 10, number of events= 3
coef exp(coef) se(coef) z Pr(>|z|)
df$symp 0.973 2.646 1.524 0.638 0.523
exp(coef) exp(-coef) lower .95 upper .95
df$asymp 2.646 0.378 0.1334 52.46
Rsquare= 0.039 (max possible= 0.616 )
Likelihood ratio test= 0.4 on 1 df, p=0.528
Wald test = 0.41 on 1 df, p=0.5232
Score (logrank) test = 0.43 on 1 df, p=0.5127
Or should I duplicate patients and use the syntax as described above?
ID side lesion
1 symp 0
1 asymp 1
2 symp 0
2 asymp 0
3 symp 1
3 asymp 0
4 symp 0
4 asymp 0
5 symp 1
5 asymp 1
6 symp 1
6 asymp 0
