library(tidyverse)
library(gtsummary)
library(dplyr)
library(tidyr)
data <- ("trial.csv")
trial %>%
  select(age, grade, trt) %>%
  tbl_summary()
Characteristic N = 2001
Age 47 (38, 57)
    Unknown 11
Grade
    I 68 (34%)
    II 68 (34%)
    III 64 (32%)
Chemotherapy Treatment
    Drug A 98 (49%)
    Drug B 102 (51%)
1 Median (Q1, Q3); n (%) 
trial %>%
  tbl_summary(by = trt) %>%
  add_p() %>%
  add_overall()
Characteristic Overall
N = 2001		 Drug A
N = 981		 Drug B
N = 1021		 p-value2
Age 47 (38, 57) 46 (37, 60) 48 (39, 56) 0.7
    Unknown 11 7 4
Marker Level (ng/mL) 0.64 (0.22, 1.41) 0.84 (0.23, 1.60) 0.52 (0.18, 1.21) 0.085
    Unknown 10 6 4
T Stage


0.9
    T1 53 (27%) 28 (29%) 25 (25%)
    T2 54 (27%) 25 (26%) 29 (28%)
    T3 43 (22%) 22 (22%) 21 (21%)
    T4 50 (25%) 23 (23%) 27 (26%)
Grade


0.9
    I 68 (34%) 35 (36%) 33 (32%)
    II 68 (34%) 32 (33%) 36 (35%)
    III 64 (32%) 31 (32%) 33 (32%)
Tumor Response 61 (32%) 28 (29%) 33 (34%) 0.5
    Unknown 7 3 4
Patient Died 112 (56%) 52 (53%) 60 (59%) 0.4
Months to Death/Censor 22.4 (15.9, 24.0) 23.5 (17.4, 24.0) 21.2 (14.5, 24.0) 0.14
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test 
model <- glm( factor(death) ~ trt + age, data = trial, family = binomial)
summary(model)

Call:
glm(formula = factor(death) ~ trt + age, family = binomial, data = trial)

Coefficients:
            Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.47456    0.52918  -0.897    0.370
trtDrug B    0.30448    0.29390   1.036    0.300
age          0.01056    0.01033   1.022    0.307

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 260.48  on 188  degrees of freedom
Residual deviance: 258.32  on 186  degrees of freedom
  (11 observations deleted due to missingness)
AIC: 264.32

Number of Fisher Scoring iterations: 4
tbl_regression(model)
Characteristic log(OR) 95% CI p-value
Chemotherapy Treatment


    Drug A — —
    Drug B 0.30 -0.27, 0.88 0.3
Age 0.01 -0.01, 0.03 0.3
Abbreviations: CI = Confidence Interval, OR = Odds Ratio 
exp(cbind(OR = coef(model), confint(model)))
                   OR     2.5 %   97.5 %
(Intercept) 0.6221615 0.2173766 1.748544
trtDrug B   1.3559218 0.7628798 2.419382
age         1.0106203 0.9904519 1.031601
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