Make demographics table

Make demographics table

Usage

make_demographics_table(
  data,
  strata = "Gender",
  vars = c("Age at visit", "Primary Race/Ethnicity", "FXTAS Stage", "CGG")
)

Arguments

data

a data.frame containing the variables specified by strata and vars

strata

names of column variable, specified as character

vars

names of row variables, specified as character

Value

a flextable::flextable

Examples

test_data_v1 |> make_demographics_table()
#> 5 missing rows in the "FX*" column have been removed.
#> 2 missing rows in the "FX*" column have been removed.
 
Male
Female
M vs. F
(all CGG combined)
Characteristic
CGG <55  
N = 431
CGG ≥ 55  
N = 2051
CGG <55  
N = 131
CGG ≥ 55  
N = 451
p-value2
Age at visit
0.8483
Mean (SD)
62.3 (10.64)
62.6 (10.25)
65.3 (9.40)
62.4 (12.01)
Median [Min, Max]
62 [41, 85]
63 [40, 92]
68 [48, 80]
63 [41, 89]
Missing
0
1
Primary Race/Ethnicity
1.0004
White
31 (82%)
168 (90%)
13 (100%)
37 (84%)
Hispanic
3 (7.9%)
7 (3.8%)
0 (0%)
3 (6.8%)
Black
0 (0%)
3 (1.6%)
0 (0%)
1 (2.3%)
Other
4 (11%)
8 (4.3%)
0 (0%)
3 (6.8%)
Missing
5
19
0
1
FXTAS Stage
0.4794
0
13 (34%)
72 (39%)
3 (27%)
11 (26%)
1
5 (13%)
27 (14%)
3 (27%)
5 (12%)
2
9 (24%)
35 (19%)
1 (9.1%)
11 (26%)
3
8 (21%)
40 (21%)
3 (27%)
10 (23%)
4
2 (5.3%)
11 (5.9%)
1 (9.1%)
5 (12%)
5
1 (2.6%)
2 (1.1%)
0 (0%)
1 (2.3%)
Missing
5
18
2
2
CGG
0.3953
Mean (SD)
89.1 (111.77)
80.5 (60.21)
80.6 (36.05)
82.5 (115.49)
Median [Min, Max]
77 [20, 780]
79 [20, 845]
85 [28, 141]
69 [20, 800]
Missing
0
2
0
1
1n (%)
2p-values represent tests for sex differences in distributions of characteristics, all CGG repeat levels.
3p-value for significance of sex difference by Wilcoxon rank sum test
4p-value for significance of sex difference by Fisher's exact test