# Why 95%CI of OR in tbl_regression does not match p-value (and is different from 95%CI from logistic.display)?

I performed multivariate logistic regression with this dataset: https://justpaste.it/61vgo

#build model
library(gtsummary)
library(epiDisplay)
sep = "\t") #after copying data
fit1 <- glm(outcome ~ A + B, family=binomial, data=dataB)

#get output
logistic.display(fit1)
tbl_regression(fit1, exponentiate = T)


As you can see below, 95% CI are not the same between both output (logistic.display from epiDisplay and tbl_regression from gtsummary, see below), and moreover, tbl_regression display 95%CI that does not match with p-value from Wald test (pvalue should be < 0.05 if 95%CI does not contain 1) ...

Do you know why? Which one is the good one?

Logistic.display

tbl_regression

You are comparing Wald p-values with confidence intervals based on profile likelihood, this is the reason why the results don't match.

If you were to estimate Wald confidence intervals, they would include 0 and match your Wald p-value results.

confint.default(fit1)
2.5 %      97.5 %
(Intercept) -4.505810889 7.827309627
A           -0.008111892 0.794005695
B           -0.420888942 0.001519585


On the other hand, you already got the LR-test p-values (0.01, 0.034) in your call to logistic.display(), which also match the results of the 95% CIs based on profile likelihood.

• Ok thanks ! I understood that when the sample is big enough, Wald pvalues and Likelihood pvalues merge. Am I right? So which one to choose (pvalues and IC) when sample is sample? Moreover, why tbl_regression select pvalue from Wald and IC from Likelihood? Apr 16 at 16:15