add_ci.md
In gtsummary: Presentation-Ready Data Summary and Analytic Result Tables

add_ci() works

Code
  tbl1 %>% as.data.frame()
Output
    **Characteristic** **Drug A**, N = 98 (**95% CI**)
  1     Tumor Response             28 (29%) (21%, 40%)
  2                Age            46 (37, 59) (44, 50)
    **Drug B**, N = 102 (**95% CI**) **p-value**
  1              33 (34%) (25%, 44%)         0.5
  2             48 (39, 56) (45, 50)         0.7

Code
  res %>% as.data.frame()
Output
    **Characteristic** **Drug A**, N = 98 **95% CI** **Drug B**, N = 102
  1     Tumor Response           28 (29%)   21%, 40%            33 (34%)
  2                Age        46 (37, 59)     44, 50         48 (39, 56)
    **95% CI** **p-value**
  1   25%, 44%         0.5
  2     45, 50         0.7

Code
  res %>% as.data.frame()
Output
        **Characteristic** **N = 200** **95% CI**
  1         Tumor Response    61 (32%)   25 to 39
  2 Chemotherapy Treatment        <NA>       <NA>
  3                 Drug A    98 (49%)   42 to 56
  4                 Drug B   102 (51%)   44 to 58

add_ci() works on a subset of variables

Code
  trial %>% tbl_summary(include = c(response, age)) %>% add_ci(include = age) %>%
    as_tibble()
Message
  x `add_ci()` added mean CI for "age"; however, no mean is shown in the `tbl_summary()` table.
Output
  # A tibble: 4 x 3
    `**Characteristic**` `**N = 200**` `**95% CI**`
    <chr>                <chr>         <chr>       
  1 Tumor Response       61 (32%)      <NA>        
  2 Unknown              7             <NA>        
  3 Age                  47 (38, 57)   45, 49      
  4 Unknown              11            <NA>

add_ci() column order respects add_overall(last=TRUE)

Code
  trial %>% tbl_summary(by = trt, include = "stage") %>% add_overall(last = TRUE) %>%
    add_ci() %>% as_tibble(col_label = FALSE)
Output
  # A tibble: 5 x 7
    label   stat_1   ci_stat_1 stat_2   ci_stat_2 stat_0   ci_stat_0
    <chr>   <chr>    <chr>     <chr>    <chr>     <chr>    <chr>    
  1 T Stage <NA>     <NA>      <NA>     <NA>      <NA>     <NA>     
  2 T1      28 (29%) 20%, 39%  25 (25%) 17%, 34%  53 (27%) 21%, 33% 
  3 T2      25 (26%) 17%, 35%  29 (28%) 20%, 38%  54 (27%) 21%, 34% 
  4 T3      22 (22%) 15%, 32%  21 (21%) 13%, 30%  43 (22%) 16%, 28% 
  5 T4      23 (23%) 16%, 33%  27 (26%) 18%, 36%  50 (25%) 19%, 32%

add_ci() works with tbl_svysummary

Code
  svyres %>% as_tibble()
Output
  # A tibble: 8 x 5
    `**Characteristic**` `**No**, N = 1,692` `**95% CI**` `**Yes**, N = 4,502`
    <chr>                <chr>               <chr>        <chr>               
  1 api00                631 (556, 710)      547, 722     654 (551, 722)      
  2 hsg                  20 (12)             13, 28       22 (17)             
  3 stype                <NA>                <NA>         <NA>                
  4 E                    1,083 (64%)         43%, 81%     3,791 (84%)         
  5 H                    237 (14%)           6.6%, 27%    237 (5.3%)          
  6 M                    372 (22%)           8.7%, 46%    474 (11%)           
  7 yr.rnd               34 (2.0%)           0.32%, 12%   271 (6.0%)          
  8 logical              1,659 (98%)         88%, 100%    4,231 (94%)         
  # i 1 more variable: `**95% CI**` <chr>

Code
  res %>% as_tibble()
Output
  # A tibble: 8 x 5
    `**Characteristic**` `**No**, N = 1,692` `**95% CI**` `**Yes**, N = 4,502`
    <chr>                <chr>               <chr>        <chr>               
  1 api00                631 (556, 710)      547, 722     654 (551, 722)      
  2 hsg                  20 (12)             13, 27       22 (17)             
  3 stype                <NA>                <NA>         <NA>                
  4 E                    1,083 (64%)         46%, 82%     3,791 (84%)         
  5 H                    237 (14%)           4.8%, 23%    237 (5.3%)          
  6 M                    372 (22%)           5.0%, 39%    474 (11%)           
  7 yr.rnd               34 (2.0%)           0.37%, 10%   271 (6.0%)          
  8 logical              1,659 (98%)         90%, 100%    4,231 (94%)         
  # i 1 more variable: `**95% CI**` <chr>