I got the same results between imputed vs non-imputed data??

Hi all,

I am using the MICE package to handle missing data in my logistic regression (I have missing data for my binary predictor, pls see the complete data below). However, I got the completely same results between with vs without MICE (which is strange). Am I doing anything wrong in terms of my coding below?

data_1$SmokingNA <- ifelse(data_1$Smoking.Status == "", NA, data_1$Smoking.Status) ## replacing empty cells with NA

m <- glm(disease ~ SmokingNA, data=data_1, family=binomial) ## non-imputed logistic regression
imp1 <- mice(data_1, m = 5) ## MICE
fitm <- with(imp1, glm(disease ~ SmokingNA, data=data_1, family=binomial)) ## Imputed logistic regression

## both show the same results (coefficient, p-value, standard error, etc)

Below is the data (Predictor: smoking status; Outcome: disease):

structure(list(Smoking.Status = c("smoking", "smoking", "smoking", 
"smoking", "smoking", "non-smoking", "smoking", "non-smoking", 
"non-smoking", "non-smoking", "smoking", "non-smoking", "non-smoking", 
"smoking", "non-smoking", "smoking", "smoking", "non-smoking", 
"non-smoking", "", "", "", "", "", "", "", "non-smoking", "", 
"", "non-smoking", "smoking", "non-smoking", "non-smoking", "smoking", 
"non-smoking", "non-smoking", "non-smoking", "non-smoking", "non-smoking", 
"", "non-smoking", "smoking", "non-smoking", "non-smoking", "smoking", 
"non-smoking", "smoking"), disease = structure(c(1L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 
1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 
2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("none", 
"disease"), class = "factor")), row.names = c(NA, 47L), class = "data.frame")

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