I have this df

df
# A tibble: 248 × 2
   asignado     mxsitam
   <chr>        <chr>  
 1 Control      No     
 2 Control      No     
 3 Intervencion No     
 4 Intervencion Si     
 5 Intervencion Si     
 6 Intervencion Si     
 7 Control      No     
 8 Intervencion Si     
 9 Control      Si     
10 Control      Si     
# ℹ 238 more rows

I want to use add_difference() and also calculate the p-value of the result obtained.

This is the code.

aticamama %>%
  select(c("asignado",
           mxsitam)) %>%
  mutate(mxsitam= as.integer(if_else(mxsitam== "No", 0,1))) %>%
  tbl_summary(by= "asignado",
              missing = "always",
              digits = list(all_categorical() ~ c(0,1)),
              statistic = list(all_categorical() ~ "{n} ({p})"),
              missing_text= "Casos perdidos",
              percent= "column") %>% 
  add_overall() %>%
  modify_header(label = "") %>%
  add_difference() 

This is the output

enter image description here

As you can see my diference is -6,9% and my p-value is 0,5.
But when I use prop.test() to calculate my CI it gaves me another p value.

aticamama$variable1 <- factor(aticamama$asignado)
aticamama$variable2 <- factor(aticamama$mxsitam)

tabla_contingencia <- table(aticamama$variable1, aticamama$variable2)
tabla_contingencia
> tabla_contingencia
   
    No Si
  0 92 33
  1 82 41

resultado_prueba <- prop.test(tabla_contingencia)

resultado_prueba
> resultado_prueba

    2-sample test for equality of proportions with continuity correction

data:  tabla_contingencia
X-squared = 1,1116, df = 1, p-value = 0,2917
alternative hypothesis: two.sided
95 percent confidence interval:
 -0,05236089  0,19102756
sample estimates:
   prop 1    prop 2 
0,7360000 0,6666667 

Now it shows that my p-value is 0,2917. Why?
Also, why with add_p() it doesn’t give me a CI?

Why my p-value isn’t the same with prop.test() and gtsummary()