CODEWITHSUNDEEP
gtsummary
oxalis
oxalis

Reputation: 1

Confidence Interval with tbl_regression

I've used the glm function to perform an univariate regression:

data_glm<-data.frame(c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0),c("ctl","ctl","ctl","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt","ctl","ctl","ctl","ctl","ctl","ctl","ctl","ctl","ctl","ctl","ctl","ctl","trt","trt","trt","trt","trt","trt","trt","trt","trt","trt"))
colnames(data_glm) <- c("response", "arm")

glm.fit <- glm(response ~ arm, data = data_glm, family = binomial(link = "logit"))
summary(glm.fit)

R Output

Call:
glm(formula = response ~ arm, family = binomial(link = "logit"), 
    data = data_glm)

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)   -1.3863     0.6455  -2.148 0.031742 *  
arm trt   2.6391     0.7384   3.574 0.000352 ***

I would like to use tbl_regression to summarize these results. Unfortunately the resulting CI are not the Wald CI, for example for ARM: trt 14.0 (3.65 to 71.0) with the following code:

 tbl_regression(glm.fit, exponentiate = TRUE)

Is there an option to get the Wald CI?

Thanks, LM

Upvotes: 0

Views: 842

Answers (1)

Mike
Mike

Reputation: 4370

If you want the wald CI you can use the example on the gtsummary help page to walk you through this. Extra help here: https://www.danieldsjoberg.com/gtsummary/articles/gallery.html#wald-ci.

here is the code on how to get wald ci.

my_tidy <- function(x, exponentiate =  FALSE, conf.level = 0.95, ...) {
  dplyr::bind_cols(
    broom::tidy(x, exponentiate = exponentiate, conf.int = FALSE),
    # calculate the confidence intervals, and save them in a tibble
    stats::confint.default(x) %>%
      tibble::as_tibble() %>%
      rlang::set_names(c("conf.low", "conf.high"))  )
}

lm(age ~ grade + marker, trial) %>%
  tbl_regression(tidy_fun = my_tidy)

Upvotes: 1

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