Nothing
Code
tbl_summary(trial, by = grade) %>% add_p() %>% as.data.frame()
Output
**Characteristic** **I**, N = 68 **II**, N = 68 **III**, N = 64
1 Chemotherapy Treatment <NA> <NA> <NA>
2 Drug A 35 (51%) 32 (47%) 31 (48%)
3 Drug B 33 (49%) 36 (53%) 33 (52%)
4 Age 47 (37, 56) 49 (37, 57) 47 (38, 58)
5 Unknown 2 6 3
6 Marker Level (ng/mL) 1.01 (0.26, 1.61) 0.37 (0.14, 1.11) 0.62 (0.29, 1.68)
7 Unknown 2 5 3
8 T Stage <NA> <NA> <NA>
9 T1 17 (25%) 23 (34%) 13 (20%)
10 T2 18 (26%) 17 (25%) 19 (30%)
11 T3 18 (26%) 11 (16%) 14 (22%)
12 T4 15 (22%) 17 (25%) 18 (28%)
13 Tumor Response 21 (31%) 19 (30%) 21 (33%)
14 Unknown 1 5 1
15 Patient Died 33 (49%) 36 (53%) 43 (67%)
16 Months to Death/Censor 24.0 (18.2, 24.0) 22.2 (13.1, 24.0) 19.7 (16.1, 24.0)
**p-value**
1 0.9
2 <NA>
3 <NA>
4 0.8
5 <NA>
6 0.019
7 <NA>
8 0.6
9 <NA>
10 <NA>
11 <NA>
12 <NA>
13 >0.9
14 <NA>
15 0.080
16 0.060
Code
tbl
Output
# A tibble: 22 x 4
`**Characteristic**` `**0**, N = 19` `**1**, N = 13` `**p-value**`
<chr> <chr> <chr> <chr>
1 mpg 17.3 (15.0, 19.2) 22.8 (21.0, 30.4) 0.002
2 cyl <NA> <NA> 0.009
3 4 3 (16%) 8 (62%) <NA>
4 6 4 (21%) 3 (23%) <NA>
5 8 12 (63%) 2 (15%) <NA>
6 disp 276 (196, 360) 120 (79, 160) <0.001
7 hp 175 (117, 193) 109 (66, 113) 0.046
8 drat 3.15 (3.07, 3.70) 4.08 (3.85, 4.22) <0.001
9 wt 3.52 (3.44, 3.84) 2.32 (1.94, 2.78) <0.001
10 qsec 17.82 (17.18, 19.17) 17.02 (16.46, 18.61) 0.3
# i 12 more rows
Code
trial %>% tbl_summary(by = trt) %>% add_p() %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Age 46 (37, 59) 48 (39, 56) 0.7
2 Unknown 7 4 <NA>
3 Marker Level (ng/mL) 0.84 (0.24, 1.57) 0.52 (0.19, 1.20) 0.085
4 Unknown 6 4 <NA>
5 T Stage <NA> <NA> 0.9
6 T1 28 (29%) 25 (25%) <NA>
7 T2 25 (26%) 29 (28%) <NA>
8 T3 22 (22%) 21 (21%) <NA>
9 T4 23 (23%) 27 (26%) <NA>
10 Grade <NA> <NA> 0.9
11 I 35 (36%) 33 (32%) <NA>
12 II 32 (33%) 36 (35%) <NA>
13 III 31 (32%) 33 (32%) <NA>
14 Tumor Response 28 (29%) 33 (34%) 0.5
15 Unknown 3 4 <NA>
16 Patient Died 52 (53%) 60 (59%) 0.4
17 Months to Death/Censor 23.5 (17.4, 24.0) 21.2 (14.6, 24.0) 0.14
Code
tbl_summary(trial, by = trt, include = -response) %>% add_p(group = response) %>%
as.data.frame()
Message
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Age 46 (37, 59) 48 (39, 56) >0.9
2 Unknown 7 4 <NA>
3 Marker Level (ng/mL) 0.84 (0.24, 1.57) 0.52 (0.19, 1.20) 0.2
4 Unknown 6 4 <NA>
5 T Stage <NA> <NA> 0.9
6 T1 28 (29%) 25 (25%) <NA>
7 T2 25 (26%) 29 (28%) <NA>
8 T3 22 (22%) 21 (21%) <NA>
9 T4 23 (23%) 27 (26%) <NA>
10 Grade <NA> <NA> 0.8
11 I 35 (36%) 33 (32%) <NA>
12 II 32 (33%) 36 (35%) <NA>
13 III 31 (32%) 33 (32%) <NA>
14 Patient Died 52 (53%) 60 (59%) 0.3
15 Months to Death/Censor 23.5 (17.4, 24.0) 21.2 (14.6, 24.0) 0.042
Code
tbl_summary(trial, by = grade, type = all_continuous() ~ "continuous2") %>%
add_p() %>% as_tibble()
Output
# A tibble: 19 x 5
`**Characteristic**` `**I**, N = 68` `**II**, N = 68` `**III**, N = 64`
<chr> <chr> <chr> <chr>
1 Chemotherapy Treatment <NA> <NA> <NA>
2 Drug A 35 (51%) 32 (47%) 31 (48%)
3 Drug B 33 (49%) 36 (53%) 33 (52%)
4 Age <NA> <NA> <NA>
5 Median (IQR) 47 (37, 56) 49 (37, 57) 47 (38, 58)
6 Unknown 2 6 3
7 Marker Level (ng/mL) <NA> <NA> <NA>
8 Median (IQR) 1.01 (0.26, 1.61) 0.37 (0.14, 1.11) 0.62 (0.29, 1.68)
9 Unknown 2 5 3
10 T Stage <NA> <NA> <NA>
11 T1 17 (25%) 23 (34%) 13 (20%)
12 T2 18 (26%) 17 (25%) 19 (30%)
13 T3 18 (26%) 11 (16%) 14 (22%)
14 T4 15 (22%) 17 (25%) 18 (28%)
15 Tumor Response 21 (31%) 19 (30%) 21 (33%)
16 Unknown 1 5 1
17 Patient Died 33 (49%) 36 (53%) 43 (67%)
18 Months to Death/Censor <NA> <NA> <NA>
19 Median (IQR) 24.0 (18.2, 24.0) 22.2 (13.1, 24.0) 19.7 (16.1, 24.0)
# i 1 more variable: `**p-value**` <chr>
Code
tbl
Output
# A tibble: 28 x 4
`**Characteristic**` `**0**, N = 19` `**1**, N = 13` `**p-value**`
<chr> <chr> <chr> <chr>
1 mpg <NA> <NA> 0.002
2 Median (IQR) 17.3 (15.0, 19.2) 22.8 (21.0, 30.4) <NA>
3 cyl <NA> <NA> 0.009
4 4 3 (16%) 8 (62%) <NA>
5 6 4 (21%) 3 (23%) <NA>
6 8 12 (63%) 2 (15%) <NA>
7 disp <NA> <NA> <0.001
8 Median (IQR) 276 (196, 360) 120 (79, 160) <NA>
9 hp <NA> <NA> 0.046
10 Median (IQR) 175 (117, 193) 109 (66, 113) <NA>
# i 18 more rows
Code
trial %>% tbl_summary(by = trt, type = all_continuous() ~ "continuous2") %>%
add_p() %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Age <NA> <NA> 0.7
2 Median (IQR) 46 (37, 59) 48 (39, 56) <NA>
3 Unknown 7 4 <NA>
4 Marker Level (ng/mL) <NA> <NA> 0.085
5 Median (IQR) 0.84 (0.24, 1.57) 0.52 (0.19, 1.20) <NA>
6 Unknown 6 4 <NA>
7 T Stage <NA> <NA> 0.9
8 T1 28 (29%) 25 (25%) <NA>
9 T2 25 (26%) 29 (28%) <NA>
10 T3 22 (22%) 21 (21%) <NA>
11 T4 23 (23%) 27 (26%) <NA>
12 Grade <NA> <NA> 0.9
13 I 35 (36%) 33 (32%) <NA>
14 II 32 (33%) 36 (35%) <NA>
15 III 31 (32%) 33 (32%) <NA>
16 Tumor Response 28 (29%) 33 (34%) 0.5
17 Unknown 3 4 <NA>
18 Patient Died 52 (53%) 60 (59%) 0.4
19 Months to Death/Censor <NA> <NA> 0.14
20 Median (IQR) 23.5 (17.4, 24.0) 21.2 (14.6, 24.0) <NA>
Code
tbl_summary(trial, by = trt, include = -response, type = all_continuous() ~
"continuous2") %>% add_p(group = response) %>% as.data.frame()
Message
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Age <NA> <NA> >0.9
2 Median (IQR) 46 (37, 59) 48 (39, 56) <NA>
3 Unknown 7 4 <NA>
4 Marker Level (ng/mL) <NA> <NA> 0.2
5 Median (IQR) 0.84 (0.24, 1.57) 0.52 (0.19, 1.20) <NA>
6 Unknown 6 4 <NA>
7 T Stage <NA> <NA> 0.9
8 T1 28 (29%) 25 (25%) <NA>
9 T2 25 (26%) 29 (28%) <NA>
10 T3 22 (22%) 21 (21%) <NA>
11 T4 23 (23%) 27 (26%) <NA>
12 Grade <NA> <NA> 0.8
13 I 35 (36%) 33 (32%) <NA>
14 II 32 (33%) 36 (35%) <NA>
15 III 31 (32%) 33 (32%) <NA>
16 Patient Died 52 (53%) 60 (59%) 0.3
17 Months to Death/Censor <NA> <NA> 0.042
18 Median (IQR) 23.5 (17.4, 24.0) 21.2 (14.6, 24.0) <NA>
Code
tbl
Output
# A tibble: 22 x 4
`**Characteristic**` `**0**, N = 19` `**1**, N = 13` `**p-value**`
<chr> <chr> <chr> <chr>
1 mpg 17.3 (15.0, 19.2) 22.8 (21.0, 30.4) 0.001
2 cyl <NA> <NA> 0.013
3 4 3 (16%) 8 (62%) <NA>
4 6 4 (21%) 3 (23%) <NA>
5 8 12 (63%) 2 (15%) <NA>
6 disp 276 (196, 360) 120 (79, 160) <0.001
7 hp 175 (117, 193) 109 (66, 113) 0.2
8 drat 3.15 (3.07, 3.70) 4.08 (3.85, 4.22) <0.001
9 wt 3.52 (3.44, 3.84) 2.32 (1.94, 2.78) <0.001
10 qsec 17.82 (17.18, 19.17) 17.02 (16.46, 18.61) 0.3
# i 12 more rows
Code
tbl
Output
# A tibble: 22 x 4
`**Characteristic**` `**0**, N = 19` `**1**, N = 13` `**p-value**`
<chr> <chr> <chr> <chr>
1 mpg 17.3 (15.0, 19.2) 22.8 (21.0, 30.4) 0.001
2 cyl <NA> <NA> 0.009
3 4 3 (16%) 8 (62%) <NA>
4 6 4 (21%) 3 (23%) <NA>
5 8 12 (63%) 2 (15%) <NA>
6 disp 276 (196, 360) 120 (79, 160) <0.001
7 hp 175 (117, 193) 109 (66, 113) 0.046
8 drat 3.15 (3.07, 3.70) 4.08 (3.85, 4.22) <0.001
9 wt 3.52 (3.44, 3.84) 2.32 (1.94, 2.78) <0.001
10 qsec 17.82 (17.18, 19.17) 17.02 (16.46, 18.61) 0.3
# i 12 more rows
Code
trial[c("response", "trt")] %>% tbl_summary(by = trt) %>% add_p(test = response ~
"my_mcnemar") %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Tumor Response 28 (29%) 33 (34%) <0.001
2 Unknown 3 4 <NA>
Code
trial[c("response", "trt")] %>% tbl_summary(by = trt) %>% add_p(test = response ~
"my_mcnemar2") %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Tumor Response 28 (29%) 33 (34%) <0.001
2 Unknown 3 4 <NA>
Code
tbl_mcnemar %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Tumor Response 28 (29%) 33 (34%) <0.001
2 Unknown 3 4 <NA>
Code
tbl_test.args %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Age 46 (37, 59) 48 (39, 56) 0.8
2 Age 46 (37, 59) 48 (39, 56) 0.8
3 Age 46 (37, 59) 48 (39, 56) 0.7
4 Age 46 (37, 59) 48 (39, 56) 0.7
5 Age 46 (37, 59) 48 (39, 56) 0.7
6 Age 46 (37, 59) 48 (39, 56) 0.8
7 Tumor Response 28 (29%) 33 (34%) 0.6
8 Tumor Response 28 (29%) 33 (34%) 0.5
9 Tumor Response 28 (29%) 33 (34%) 0.5
10 Tumor Response 28 (29%) 33 (34%) 0.5
11 Tumor Response 28 (29%) 33 (34%) 0.3
12 Tumor Response 28 (29%) 33 (34%) <0.001
13 Tumor Response 28 (29%) 33 (34%) <0.001
Code
tbl_groups %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Grade <NA> <NA> 0.9
2 I 35 (36%) 33 (32%) <NA>
3 II 32 (33%) 36 (35%) <NA>
4 III 31 (32%) 33 (32%) <NA>
5 Grade <NA> <NA> >0.9
6 I 35 (36%) 33 (32%) <NA>
7 II 32 (33%) 36 (35%) <NA>
8 III 31 (32%) 33 (32%) <NA>
9 Tumor Response 28 (29%) 33 (34%) 0.5
10 Tumor Response 28 (29%) 33 (34%) 0.4
11 Age 46 (37, 59) 48 (39, 56) 0.6
12 Age 46 (37, 59) 48 (39, 56) 0.3
13 Age 46 (37, 59) 48 (39, 56) 0.6
14 Age 46 (37, 59) 48 (39, 56) 0.3
Code
tbl_groups %>% as.data.frame()
Output
**Characteristic** **Drug A**, N = 98 **Drug B**, N = 102 **p-value**
1 Age 46 (37, 59) 48 (39, 56) 0.8
Code
t1 %>% as.data.frame()
Output
**Characteristic** **0**, N = 19 **1**, N = 13 **p-value**
1 has_banana <NA> <NA> <NA>
2 Yes 0 (NA%) 0 (NA%) <NA>
3 No 0 (NA%) 0 (NA%) <NA>
4 Unknown 19 13 <NA>
5 mpg 0 (NA%) 0 (NA%) <NA>
6 Unknown 19 13 <NA>
7 hp NA (NA, NA) NA (NA, NA) <NA>
8 Unknown 19 13 <NA>
Code
tbl
Output
# A tibble: 1 x 6
label stat_1 stat_2 estimate ci p.value
<chr> <chr> <chr> <chr> <chr> <chr>
1 Age 47.011 47.449 -0.03 -0.32, 0.25 0.8
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