Hi,
I am using the excellent Example 3 in the "Stata Customizable Tables" manual to help me build a table with frequency (percent) for categorical variables, and mean (sd) for continuous variables. Some of my continuous variables are, however, very skewed (age data for infants). For those variables I would like to report age in months as median (IQR). I would like to format the IQR as (p25 – p75). I think that what I need to do is combine both p25 and p75 into a single level of the dimension result, but I'm not sure how to do that.
I am aware that
does this. I have used and loved table1_mc, but I really want to learn the collect system.
For this post, below are data for age in months and sex. What I'm after is:
Here are my data for sex and age (months):
----------------------- copy starting from the next line -----------------------
------------------ copy up to and including the previous line ------------------
Here is my code for what I'm after using mean and SD:
Kind regards,
Ryan
I am using the excellent Example 3 in the "Stata Customizable Tables" manual to help me build a table with frequency (percent) for categorical variables, and mean (sd) for continuous variables. Some of my continuous variables are, however, very skewed (age data for infants). For those variables I would like to report age in months as median (IQR). I would like to format the IQR as (p25 – p75). I think that what I need to do is combine both p25 and p75 into a single level of the dimension result, but I'm not sure how to do that.
I am aware that
Code:
table1_mc
For this post, below are data for age in months and sex. What I'm after is:
| Male | Female | |
| Age (months) | median (p25 – p75) | median (p25 – p75) |
----------------------- copy starting from the next line -----------------------
Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input float age_adm_m byte sex
15.342916 1
54.80082 1
19.74538 1
75.663246 1
19.81109 1
46.45585 1
32.29569 1
3.3839836 2
67.15401 2
14.554415 1
69.650925 2
83.58111 1
15.014374 1
46.75154 2
36.172485 1
35.876797 1
7.096509 1
39.85216 1
7.523614 1
43.6961 1
3.12115 1
36.99384 2
55.78645 2
30.12731 2
52.46817 2
3.613963 1
17.87269 2
31.507187 2
30.58727 1
18.431211 1
43.63039 2
15.967146 2
50.7269 1
32.492813 2
16.689938 1
18.89117 1
30.45585 2
3.581109 2
19.876797 1
82.95688 1
71.29363 2
62.0616 2
30.45585 1
51.44969 2
14.52156 2
11.498973 1
1.4784395 2
28.64887 2
51.58111 1
72.24641 2
31.802876 1
42.48049 1
2.1026695 2
127.5729 1
40.21355 2
8.936345 1
3.876797 2
30.390144 1
44.71458 2
11.17043 1
10.61191 1
39.09651 1
14.52156 2
78.91581 1
16.328543 1
42.21766 1
11.039015 1
80.16427 1
150.70226 2
3.022587 1
59.07187 1
38.40657 1
57.49487 1
59.00616 2
19.58111 2
2.792608 2
79.50719 2
122.71047 2
92.09035 1
2.562628 2
46.02875 1
95.77002 2
34.49692 2
6.702259 1
48 2
43.13758 2
125.40452 2
. 1
76.38604 1
11.334702 1
43.23614 1
59.59753 1
55.88501 1
6.537988 1
82.16838 1
43.00616 1
54.17659 2
25.23203 1
54.2423 1
17.87269 1
end
label values sex sex_lbl
label def sex_lbl 1 "Male", modify
label def sex_lbl 2 "Female", modify
Here is my code for what I'm after using mean and SD:
Code:
table (var) (shortsite), statistic(fvfrequency sex) statistic(fvpercent sex) nototals append
collect style header result, level(hide)
collect style row stack, nobinder spacer
collect style cell border_block, border(right, pattern(nil))
collect layout (sex[1]) (shortsite#result)
collect style cell result[fvpercent], sformat("%s%%")
Ryan
