gtsummary output checks

Linear Model

# 
mod_lm <- lm(hp ~ am, data = mtcars)
tbl_regression(mod_lm)

Characteristic	Beta	95% CI¹	p-value
am	-33	-83, 16	0.2
¹ CI = Confidence Interval

broom::tidy(mod_lm, conf.int = TRUE)

## # A tibble: 2 x 7
##   term        estimate std.error statistic  p.value conf.low conf.high
##   <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
## 1 (Intercept)    160.       15.5     10.3  2.12e-11    129.      192. 
## 2 am             -33.4      24.3     -1.37 1.80e- 1    -83.1      16.3

Survival Model

mod_survreg <- survreg(Surv(time, status) ~ age + ph.ecog, data = lung)
tbl_regression(mod_survreg)

Characteristic	Beta	95% CI¹	p-value
age	-0.01	-0.02, 0.01	0.3
ph.ecog	-0.33	-0.49, -0.16	<0.001
¹ CI = Confidence Interval

broom::tidy(mod_survreg, conf.int = TRUE)

## # A tibble: 4 x 7
##   term        estimate std.error statistic  p.value conf.low conf.high
##   <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
## 1 (Intercept)  6.83      0.429       15.9  3.74e-57   5.99     7.67   
## 2 age         -0.00772   0.00688     -1.12 2.62e- 1  -0.0212   0.00576
## 3 ph.ecog     -0.326     0.0858      -3.80 1.42e- 4  -0.495   -0.158  
## 4 Log(scale)  -0.304     0.0619      -4.90 9.48e- 7  NA       NA

Logistic Model

mod_logistic <- glm(response ~ age + stage, trial, family = binomial)
tbl_regression(mod_logistic, exponentiate = TRUE)

Characteristic	OR¹	95% CI¹	p-value
Age, yrs	1.02	1.00, 1.04	0.091
T Stage
T1	—	—
T2	0.58	0.24, 1.37	0.2
T3	0.94	0.39, 2.28	0.9
T4	0.79	0.33, 1.90	0.6
¹ OR = Odds Ratio, CI = Confidence Interval

broom::tidy(mod_logistic, exponentiate = TRUE, conf.int = TRUE)

## # A tibble: 5 x 7
##   term        estimate std.error statistic p.value conf.low conf.high
##   <chr>          <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>
## 1 (Intercept)    0.226    0.620     -2.40   0.0166   0.0645     0.742
## 2 age            1.02     0.0115     1.69   0.0909   0.997      1.04 
## 3 stageT2        0.582    0.440     -1.23   0.219    0.242      1.37 
## 4 stageT3        0.942    0.450     -0.132  0.895    0.386      2.28 
## 5 stageT4        0.794    0.448     -0.516  0.606    0.325      1.90

Poisson Model

mod_poisson <- glm(count ~ age + trt,
  trial %>% dplyr::mutate(count = sample.int(20, size = nrow(trial), replace = TRUE)),
  family = poisson
)
tbl_regression(mod_poisson, exponentiate = TRUE)

Characteristic	IRR¹	95% CI¹	p-value
Age, yrs	1.00	1.00, 1.00	0.5
Chemotherapy Treatment
Drug A	—	—
Drug B	0.91	0.83, 0.99	0.025
¹ IRR = Incidence Rate Ratio, CI = Confidence Interval

broom::tidy(mod_poisson, exponentiate = TRUE, conf.int = TRUE)

## # A tibble: 3 x 7
##   term        estimate std.error statistic   p.value conf.low conf.high
##   <chr>          <dbl>     <dbl>     <dbl>     <dbl>    <dbl>     <dbl>
## 1 (Intercept)   12.1     0.0782     31.9   1.71e-223   10.4      14.1  
## 2 age            0.999   0.00154    -0.730 4.66e-  1    0.996     1.00 
## 3 trtDrug B      0.906   0.0440     -2.25  2.47e-  2    0.831     0.988

Linear Mixed Model

mod_lmer <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
tbl_regression(mod_lmer)

## Registered S3 methods overwritten by 'broom.mixed':
##   method         from 
##   augment.lme    broom
##   augment.merMod broom
##   glance.lme     broom
##   glance.merMod  broom
##   glance.stanreg broom
##   tidy.brmsfit   broom
##   tidy.gamlss    broom
##   tidy.lme       broom
##   tidy.merMod    broom
##   tidy.rjags     broom
##   tidy.stanfit   broom
##   tidy.stanreg   broom

Characteristic	Beta	95% CI¹	p-value
Days	10	7.4, 13
¹ CI = Confidence Interval

broom.mixed::tidy(mod_lmer)

## # A tibble: 6 x 6
##   effect   group    term                  estimate std.error statistic
##   <chr>    <chr>    <chr>                    <dbl>     <dbl>     <dbl>
## 1 fixed    <NA>     (Intercept)           251.          6.82     36.8 
## 2 fixed    <NA>     Days                   10.5         1.55      6.77
## 3 ran_pars Subject  sd__(Intercept)        24.7        NA        NA   
## 4 ran_pars Subject  sd__Days                5.92       NA        NA   
## 5 ran_pars Subject  cor__(Intercept).Days   0.0656     NA        NA   
## 6 ran_pars Residual sd__Observation        25.6        NA        NA

Generalized Linear Mixed Model

mod_glmer <- glmer(am ~ hp + factor(cyl) + (1 | gear), mtcars, family = binomial)

tbl_regression(mod_glmer, exponentiate = TRUE)

Characteristic	OR¹	95% CI¹	p-value
hp	1.00	0.94, 1.08	>0.9
factor(cyl)
4	—	—
6	0.27	0.01, 12.1	0.5
8	0.12	0.00, 15727	0.7
¹ OR = Odds Ratio, CI = Confidence Interval

broom.mixed::tidy(mod_glmer, exponentiate = TRUE, conf.int = TRUE)

## # A tibble: 5 x 9
##   effect  group term    estimate std.error statistic p.value  conf.low conf.high
##   <chr>   <chr> <chr>      <dbl>     <dbl>     <dbl>   <dbl>     <dbl>     <dbl>
## 1 fixed   <NA>  (Inter~    1.85     8.27      0.138    0.890   2.93e-4  11704.  
## 2 fixed   <NA>  hp         1.00     0.0357    0.0855   0.932   9.36e-1      1.08
## 3 fixed   <NA>  factor~    0.273    0.528    -0.671    0.502   6.13e-3     12.1 
## 4 fixed   <NA>  factor~    0.119    0.714    -0.354    0.723   8.95e-7  15727.  
## 5 ran_pa~ gear  sd__(I~    4.32    NA        NA       NA      NA           NA

tbl_lme4 <- tbl_regression(mod_glmer, exponentiate = TRUE,
                               conf.level = 0.90)

a <- coef(mod_glmer)[[1]] %>% {.[1, 2:ncol(.)]} %>% purrr::map_dbl(exp) 

b <- tbl_lme4$table_body %>% 
  dplyr::pull(estimate) %>% 
  na.omit()

all.equal(unname(a), as.vector(b))

## [1] TRUE

Linear Model with Interaction

mod_lm_interaction <- lm(age ~ trt * grade * response, data = trial)
tbl_regression(mod_lm_interaction)

Characteristic	Beta	95% CI¹	p-value
Chemotherapy Treatment
Drug A	—	—
Drug B	-0.61	-9.4, 8.2	0.9
Grade
I	—	—
II	0.14	-8.3, 8.6	>0.9
III	4.5	-4.9, 14	0.3
Tumor Response	4.8	-6.9, 16	0.4
Chemotherapy Treatment * Grade
Drug B * II	4.2	-8.4, 17	0.5
Drug B * III	-2.9	-16, 9.9	0.7
Chemotherapy Treatment * Tumor Response
Drug B * Tumor Response	1.3	-14, 17	0.9
Grade * Tumor Response
II * Tumor Response	-4.4	-21, 13	0.6
III * Tumor Response	-0.56	-17, 16	>0.9
Chemotherapy Treatment * Grade * Tumor Response
Drug B * II * Tumor Response	1.3	-22, 24	>0.9
Drug B * III * Tumor Response	-5.3	-28, 17	0.6
¹ CI = Confidence Interval

broom::tidy(mod_lm_interaction, conf.int = TRUE)

## # A tibble: 12 x 7
##    term                 estimate std.error statistic  p.value conf.low conf.high
##    <chr>                   <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
##  1 (Intercept)            44.7        2.92   15.3    6.52e-34    39.0      50.5 
##  2 trtDrug B              -0.615      4.44   -0.138  8.90e- 1    -9.38      8.15
##  3 gradeII                 0.144      4.27    0.0337 9.73e- 1    -8.28      8.56
##  4 gradeIII                4.48       4.77    0.940  3.49e- 1    -4.93     13.9 
##  5 response                4.78       5.93    0.806  4.21e- 1    -6.92     16.5 
##  6 trtDrug B:gradeII       4.17       6.37    0.655  5.14e- 1    -8.41     16.8 
##  7 trtDrug B:gradeIII     -2.95       6.51   -0.452  6.52e- 1   -15.8       9.91
##  8 trtDrug B:response      1.27       7.92    0.160  8.73e- 1   -14.4      16.9 
##  9 gradeII:response       -4.36       8.67   -0.502  6.16e- 1   -21.5      12.8 
## 10 gradeIII:response      -0.563      8.19   -0.0688 9.45e- 1   -16.7      15.6 
## 11 trtDrug B:gradeII:r~    1.29      11.6     0.111  9.12e- 1   -21.7      24.3 
## 12 trtDrug B:gradeIII:~   -5.25      11.4    -0.461  6.46e- 1   -27.7      17.2

Cox PH with Hmisc Labels

lung2 <- lung
Hmisc::label(lung2$sex) <- "Gender"
Hmisc::label(lung2$age) <- "AGE"
cox_hmisclbl <- coxph(Surv(time, status) ~ age + sex, data = lung2)
tbl_regression(cox_hmisclbl, exponentiate = TRUE)

Characteristic	HR¹	95% CI¹	p-value
AGE	1.02	1.00, 1.04	0.065
Gender	0.60	0.43, 0.83	0.002
¹ HR = Hazard Ratio, CI = Confidence Interval

broom::tidy(cox_hmisclbl, exponentiate = TRUE, conf.int = TRUE)

## # A tibble: 2 x 7
##   term  estimate std.error statistic p.value conf.low conf.high
##   <chr>    <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl>
## 1 age      1.02    0.00922      1.85 0.0646     0.999     1.04 
## 2 sex      0.599   0.167       -3.06 0.00218    0.431     0.831