Clinical Reporting with {gtsummary}

Daniel D. Sjoberg

Introduction

Daniel D. Sjoberg

danieldsjoberg.com

@ddsjoberg.bsky.social

linkedin.com/in/ddsjoberg

github.com/ddsjoberg

How it started

  • Began to address reproducible issues while working in academia

  • Goal was to build a package to summarize study results with code that was both simple and customizable

  • First release in May 2019

How it’s going

  • The stats

    • 1,300,000+ installations from CRAN
    • 1100+ GitHub stars
    • 300+ contributors
    • 50+ code contributors

  • Won the 2021 American Statistical Association (ASA) Innovation in Programming Award

  • Won the 2024 Posit Pharma Table Contest

{gtsummary} overview

  • Create tabular summaries with sensible defaults but highly customizable
  • Types of summaries:
    • “Table 1”-types
    • Cross-tabulation
    • Regression models
    • Survival data
    • Survey data
    • Custom tables
  • Report statistics from {gtsummary} tables inline in R Markdown
  • Stack and/or merge any table type
  • Use themes to standardize across tables
  • Choose from different print engines

Example Dataset

  • The trial data set is included with {gtsummary}

  • Simulated data set of baseline characteristics for 200 patients who receive Drug A or Drug B

  • Variables were assigned labels using the labelled package

library(gtsummary)
library(tidyverse)
head(trial) |> gt::gt()
Chemotherapy Treatment Age Marker Level (ng/mL) T Stage Grade Tumor Response Patient Died Months to Death/Censor
Drug A 23 0.160 T1 II 0 0 24.00
Drug B 9 1.107 T2 I 1 0 24.00
Drug A 31 0.277 T1 II 0 0 24.00
Drug A NA 2.067 T3 III 1 1 17.64
Drug A 51 2.767 T4 III 1 1 16.43
Drug B 39 0.613 T4 I 0 1 15.64

Example Dataset

This presentation will use a subset of the variables.

sm_trial <-
  trial |> 
  select(trt, age, grade, response)
Variable Label
trt Chemotherapy Treatment
age Age
grade Grade
response Tumor Response

tbl_summary()

Basic tbl_summary()

sm_trial |> 
  select(-trt) |>  
  tbl_summary()
Characteristic N = 2001
Age 47 (38, 57)
    Unknown 11
Grade
    I 68 (34%)
    II 68 (34%)
    III 64 (32%)
Tumor Response 61 (32%)
    Unknown 7
1 Median (Q1, Q3); n (%)

  • Four types of summaries: continuous, continuous2, categorical, and dichotomous

  • Statistics are median (IQR) for continuous, n (%) for categorical/dichotomous

  • Variables coded 0/1, TRUE/FALSE, Yes/No treated as dichotomous

  • Lists NA values under “Unknown”

  • Label attributes are printed automatically

Customize tbl_summary() output

tbl_summary(
  sm_trial,
  by = trt,
)
Characteristic Drug A
N = 981		 Drug B
N = 1021
Age 46 (37, 60) 48 (39, 56)
    Unknown 7 4
Grade

    I 35 (36%) 33 (32%)
    II 32 (33%) 36 (35%)
    III 31 (32%) 33 (32%)
Tumor Response 28 (29%) 33 (34%)
    Unknown 3 4
1 Median (Q1, Q3); n (%)
  • by: specify a column variable for cross-tabulation

Customize tbl_summary() output

tbl_summary(
  sm_trial,
  by = trt,
  type = age ~ "continuous2",
)
Characteristic Drug A
N = 981		 Drug B
N = 1021
Age

    Median (Q1, Q3) 46 (37, 60) 48 (39, 56)
    Unknown 7 4
Grade

    I 35 (36%) 33 (32%)
    II 32 (33%) 36 (35%)
    III 31 (32%) 33 (32%)
Tumor Response 28 (29%) 33 (34%)
    Unknown 3 4
1 n (%)

  • by: specify a column variable for cross-tabulation

  • type: specify the summary type

Customize tbl_summary() output

tbl_summary(
  sm_trial,
  by = trt,
  type = age ~ "continuous2",
  statistic = 
    list(
      age ~ c("{mean} ({sd})", 
              "{min}, {max}"), 
      response ~ "{n} / {N} ({p}%)"
    ),
)
Characteristic Drug A
N = 981		 Drug B
N = 1021
Age

    Mean (SD) 47 (15) 47 (14)
    Min, Max 6, 78 9, 83
    Unknown 7 4
Grade

    I 35 (36%) 33 (32%)
    II 32 (33%) 36 (35%)
    III 31 (32%) 33 (32%)
Tumor Response 28 / 95 (29%) 33 / 98 (34%)
    Unknown 3 4
1 n (%); n / N (%)

  • by: specify a column variable for cross-tabulation

  • type: specify the summary type

  • statistic: customize the reported statistics

Customize tbl_summary() output

tbl_summary(
  sm_trial,
  by = trt,
  type = age ~ "continuous2",
  statistic = 
    list(
      age ~ c("{mean} ({sd})", 
              "{min}, {max}"), 
      response ~ "{n} / {N} ({p}%)"
    ),
  label = 
    grade ~ "Pathologic tumor grade",
)
Characteristic Drug A
N = 981		 Drug B
N = 1021
Age

    Mean (SD) 47 (15) 47 (14)
    Min, Max 6, 78 9, 83
    Unknown 7 4
Pathologic tumor grade

    I 35 (36%) 33 (32%)
    II 32 (33%) 36 (35%)
    III 31 (32%) 33 (32%)
Tumor Response 28 / 95 (29%) 33 / 98 (34%)
    Unknown 3 4
1 n (%); n / N (%)

  • by: specify a column variable for cross-tabulation

  • type: specify the summary type

  • statistic: customize the reported statistics

  • label: change or customize variable labels

Customize tbl_summary() output

tbl_summary(
  sm_trial,
  by = trt,
  type = age ~ "continuous2",
  statistic = 
    list(
      age ~ c("{mean} ({sd})", 
              "{min}, {max}"), 
      response ~ "{n} / {N} ({p}%)"
    ),
  label = 
    grade ~ "Pathologic tumor grade",
  digits = age ~ 1
)
Characteristic Drug A
N = 981		 Drug B
N = 1021
Age

    Mean (SD) 47.0 (14.7) 47.4 (14.0)
    Min, Max 6.0, 78.0 9.0, 83.0
    Unknown 7 4
Pathologic tumor grade

    I 35 (36%) 33 (32%)
    II 32 (33%) 36 (35%)
    III 31 (32%) 33 (32%)
Tumor Response 28 / 95 (29%) 33 / 98 (34%)
    Unknown 3 4
1 n (%); n / N (%)

  • by: specify a column variable for cross-tabulation

  • type: specify the summary type

  • statistic: customize the reported statistics

  • label: change or customize variable labels

  • digits: specify the number of decimal places for rounding

{gtsummary} + formulas

Named list are OK too! label = list(age = "Patient Age")

Add-on functions in {gtsummary}

tbl_summary() objects can also be updated using related functions.

  • add_*() add additional column of statistics or information, e.g. p-values, q-values, overall statistics, treatment differences, N obs., and more

  • modify_*() modify table headers, spanning headers, footnotes, and more

  • bold_*()/italicize_*() style labels, variable levels, significant p-values

Update tbl_summary() with add_*()

sm_trial |>
  tbl_summary(
    by = trt
  ) |> 
  add_p() |> 
  add_q(method = "fdr")
Characteristic Drug A
N = 981		 Drug B
N = 1021		 p-value2 q-value3
Age 46 (37, 60) 48 (39, 56) 0.7 0.9
    Unknown 7 4

Grade

0.9 0.9
    I 35 (36%) 33 (32%)

    II 32 (33%) 36 (35%)

    III 31 (32%) 33 (32%)

Tumor Response 28 (29%) 33 (34%) 0.5 0.9
    Unknown 3 4

1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test
3 False discovery rate correction for multiple testing

  • add_p(): adds a column of p-values

  • add_q(): adds a column of p-values adjusted for multiple comparisons through a call to p.adjust()

Update tbl_summary() with add_*()

sm_trial |>
  tbl_summary(
    by = trt,
    missing = "no"
  ) |> 
  add_overall()
Characteristic Overall
N = 2001		 Drug A
N = 981		 Drug B
N = 1021
Age 47 (38, 57) 46 (37, 60) 48 (39, 56)
Grade


    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%)
1 Median (Q1, Q3); n (%)
  • add_overall(): adds a column of overall statistics

Update tbl_summary() with add_*()

sm_trial |>
  tbl_summary(
    by = trt,
    missing = "no"
  ) |> 
  add_overall() |> 
  add_n()
Characteristic N Overall
N = 2001		 Drug A
N = 981		 Drug B
N = 1021
Age 189 47 (38, 57) 46 (37, 60) 48 (39, 56)
Grade 200


    I
68 (34%) 35 (36%) 33 (32%)
    II
68 (34%) 32 (33%) 36 (35%)
    III
64 (32%) 31 (32%) 33 (32%)
Tumor Response 193 61 (32%) 28 (29%) 33 (34%)
1 Median (Q1, Q3); n (%)
  • add_overall(): adds a column of overall statistics
  • add_n(): adds a column with the sample size

Update tbl_summary() with add_*()

sm_trial |>
  tbl_summary(
    by = trt,
    missing = "no"
  ) |> 
  add_overall() |> 
  add_n() |> 
  add_stat_label(
    label = all_categorical() ~ "No. (%)"
  )
Characteristic N Overall
N = 200		 Drug A
N = 98		 Drug B
N = 102
Age, Median (Q1, Q3) 189 47 (38, 57) 46 (37, 60) 48 (39, 56)
Grade, No. (%) 200


    I
68 (34%) 35 (36%) 33 (32%)
    II
68 (34%) 32 (33%) 36 (35%)
    III
64 (32%) 31 (32%) 33 (32%)
Tumor Response, No. (%) 193 61 (32%) 28 (29%) 33 (34%)
  • add_overall(): adds a column of overall statistics
  • add_n(): adds a column with the sample size
  • add_stat_label(): adds a description of the reported statistic

Update with bold_*()/italicize_*()

sm_trial |>
  tbl_summary(
    by = trt
  ) |>
  add_p() |> 
  bold_labels() |> 
  italicize_levels() |> 
  bold_p(t = 0.8)
Characteristic Drug A
N = 981		 Drug B
N = 1021		 p-value2
Age 46 (37, 60) 48 (39, 56) 0.7
    Unknown 7 4
Grade

0.9
    I 35 (36%) 33 (32%)
    II 32 (33%) 36 (35%)
    III 31 (32%) 33 (32%)
Tumor Response 28 (29%) 33 (34%) 0.5
    Unknown 3 4
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test
  • bold_labels(): bold the variable labels
  • italicize_levels(): italicize the variable levels
  • bold_p(): bold p-values according a specified threshold

Update tbl_summary() with modify_*()

tbl <-
  sm_trial |> 
  tbl_summary(by = trt, 
              missing = "no") |>
  modify_header(
      stat_1 ~ "**Group A**",
      stat_2 ~ "**Group B**"
  ) |> 
  modify_spanning_header(
    all_stat_cols() ~ "**Drug**") |> 
  modify_footnote(
    all_stat_cols() ~ 
      paste("median (IQR) for continuous;",
            "n (%) for categorical")
  )
tbl
Characteristic
Drug
Group A1 Group B1
Age 46 (37, 60) 48 (39, 56)
Grade

    I 35 (36%) 33 (32%)
    II 32 (33%) 36 (35%)
    III 31 (32%) 33 (32%)
Tumor Response 28 (29%) 33 (34%)
1 median (IQR) for continuous; n (%) for categorical
  • Use show_header_names() to see the internal header names available for use in modify_header()

Column names

all_stat_cols() selects columns "stat_1" and "stat_2"

For example, the code below would set the first header to “Drug A, N = 98” and the second to “Drug B, N = 102”.

modify_header(all_stat_cols() ~ "{level}, N = {n}")

Update tbl_summary() with add_*()

trial |>
  select(trt, marker, response) |>
  tbl_summary(
    by = trt,
    statistic = list(marker ~ "{mean} ({sd})",
                     response ~ "{p}%"),
    missing = "no"
  ) |> 
  add_difference()
Characteristic Drug A
N = 981		 Drug B
N = 1021		 Difference2 95% CI2 p-value2
Marker Level (ng/mL) 1.02 (0.89) 0.82 (0.83) 0.20 -0.05, 0.44 0.12
Tumor Response 29% 34% -4.2% -18%, 9.9% 0.6
Abbreviation: CI = Confidence Interval
1 Mean (SD); %
2 Welch Two Sample t-test; 2-sample test for equality of proportions with continuity correction
  • add_difference(): mean and rate differences between two groups. Can also be adjusted differences

Update tbl_summary() with add_*()

sm_trial |>
  tbl_summary(
    by = trt,
    missing = "no"
  ) |> 
  add_stat(...)

  • Customize statistics presented with add_stat()

  • Added statistics can be placed on the label or the level rows

  • Added statistics may be a single column or multiple

Add-on functions in {gtsummary}

And many more!

See the documentation at http://www.danieldsjoberg.com/gtsummary/reference/index.html

And a detailed tbl_summary() vignette at http://www.danieldsjoberg.com/gtsummary/articles/tbl_summary.html

Cross-tabulation with tbl_cross()

tbl_cross() is a wrapper for tbl_summary() for n x m tables

sm_trial |>
  tbl_cross(
    row = trt, 
    col = grade,
    percent = "row",
    margin = "row"
  ) |>
  add_p(source_note = TRUE) |>
  bold_labels()
Grade
I II III
Chemotherapy Treatment


    Drug A 35 (36%) 32 (33%) 31 (32%)
    Drug B 33 (32%) 36 (35%) 33 (32%)
Total 68 (34%) 68 (34%) 64 (32%)
Pearson’s Chi-squared test, p=0.9

Continuous Summaries with tbl_continuous()

tbl_continuous() summarizes a continuous variable by 1, 2, or more categorical variables

sm_trial |>
  tbl_continuous(
    variable = age,
    by = trt,
    include = grade
  )
Characteristic Drug A
N = 981		 Drug B
N = 1021
Grade

    I 46 (36, 60) 48 (42, 55)
    II 45 (31, 55) 51 (42, 58)
    III 52 (42, 61) 45 (36, 52)
1 Age: Median (Q1, Q3)

Survey data with tbl_svysummary()

survey::svydesign(
  ids = ~1, 
  data = as.data.frame(Titanic), 
  weights = ~Freq
) |>
  tbl_svysummary(
    by = Survived,
    include = c(Class, Sex)
  ) |>
  add_p() |>
  modify_spanning_header(
    all_stat_cols() ~ "**Survived**")
Characteristic
Survived
p-value2
No
N = 1,4901		 Yes
N = 7111
Class

0.7
    1st 122 (8.2%) 203 (29%)
    2nd 167 (11%) 118 (17%)
    3rd 528 (35%) 178 (25%)
    Crew 673 (45%) 212 (30%)
Sex

0.048
    Male 1,364 (92%) 367 (52%)
    Female 126 (8.5%) 344 (48%)
1 n (%)
2 Pearson’s X^2: Rao & Scott adjustment

Survival outcomes with tbl_survfit()

library(survival)
fit <- survfit(Surv(ttdeath, death) ~ trt, trial)

tbl_survfit(
  fit, 
  times = c(12, 24),
  label_header = "**{time} Month**"
) |>
  add_p()
Characteristic 12 Month 24 Month p-value1
Chemotherapy Treatment

0.2
    Drug A 91% (85%, 97%) 47% (38%, 58%)
    Drug B 86% (80%, 93%) 41% (33%, 52%)
1 Log-rank test

Nested summaries with tbl_hierarchical()

ADAE_subset <- cards::ADAE |> dplyr::filter(AESOC %in% unique(cards::ADAE$AESOC)[1:5], AETERM %in% unique(cards::ADAE$AETERM)[1:5], startsWith(TRTA, "X"))

tbl_hierarchical(
  data = ADAE_subset,
  variables = c(AESOC, AETERM),
  by = TRTA,
  denominator = cards::ADSL |> mutate(TRTA = ARM),
  id = USUBJID
)
Primary System Organ Class
    Reported Term for the Adverse Event		 Xanomeline High Dose
N = 841		 Xanomeline Low Dose
N = 841
CARDIAC DISORDERS 3 (3.6%) 0 (0%)
    ATRIOVENTRICULAR BLOCK SECOND DEGREE 3 (3.6%) 0 (0%)
GASTROINTESTINAL DISORDERS 4 (4.8%) 5 (6.0%)
    DIARRHOEA 4 (4.8%) 5 (6.0%)
GENERAL DISORDERS AND ADMINISTRATION SITE CONDITIONS 25 (30%) 24 (29%)
    APPLICATION SITE ERYTHEMA 15 (18%) 12 (14%)
    APPLICATION SITE PRURITUS 22 (26%) 22 (26%)
SKIN AND SUBCUTANEOUS TISSUE DISORDERS 14 (17%) 15 (18%)
    ERYTHEMA 14 (17%) 15 (18%)
1 n (%)

tbl_regression()

Traditional model summary()

m1 <- 
  glm(
    response ~ age + stage,
    data = trial,
    family = binomial(link = "logit")
  )

Looks messy and it’s not easy to digest

Basic tbl_regression()

tbl_regression(m1)
Characteristic log(OR) 95% CI p-value
Age 0.02 0.00, 0.04 0.091
T Stage


    T1 — —
    T2 -0.54 -1.4, 0.31 0.2
    T3 -0.06 -0.95, 0.82 0.9
    T4 -0.23 -1.1, 0.64 0.6
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

  • Displays p-values for covariates

  • Shows reference levels for categorical variables

  • Model type recognized as logistic regression with odds ratio appearing in header

Customize tbl_regression() output

tbl_regression(
  m1,
  exponentiate = TRUE
) |> 
  add_global_p() |>
  add_glance_table(
    include = c(nobs,
                logLik,
                AIC,
                BIC)
  )
Characteristic OR 95% CI p-value
Age 1.02 1.00, 1.04 0.087
T Stage

0.6
    T1 — —
    T2 0.58 0.24, 1.37
    T3 0.94 0.39, 2.28
    T4 0.79 0.33, 1.90
No. Obs. 183

Log-likelihood -112

AIC 234

BIC 250

Abbreviations: CI = Confidence Interval, OR = Odds Ratio

  • Display odds ratio estimates and confidence intervals

  • Add global p-values

  • Add various model statistics

Supported models in tbl_regression()

betareg::betareg(), biglm::bigglm(), brms::brm(), cmprsk::crr(), fixest::feglm(), fixest::femlm(), fixest::feNmlm(), fixest::feols(), gam::gam(), geepack::geeglm(), glmmTMB::glmmTMB(), glmtoolbox::glmgee(), lavaan::lavaan(), lfe::felm(), lme4::glmer.nb(), lme4::glmer(), lme4::lmer(), logitr::logitr(), MASS::glm.nb(), MASS::polr(), mgcv::gam(), mice::mira, mmrm::mmrm(), multgee::nomLORgee(), multgee::ordLORgee(), nnet::multinom(), ordinal::clm(), ordinal::clmm(), parsnip::model_fit, plm::plm(), pscl::hurdle(), pscl::zeroinfl(), rstanarm::stan_glm(), stats::aov(), stats::glm(), stats::lm(), stats::nls(), survey::svycoxph(), survey::svyglm(), survey::svyolr(), survival::cch(), survival::clogit(), survival::coxph(), survival::survreg(), tidycmprsk::crr(), VGAM::vgam(), VGAM::vglm()

Custom tidiers can be written and passed to tbl_regression() using the tidy_fun= argument.

Univariate models with tbl_uvregression()

tbl_uvreg <- 
  sm_trial |> 
  tbl_uvregression(
    method = glm,
    y = response,
    method.args = 
      list(family = binomial),
    exponentiate = TRUE
  )
tbl_uvreg
Characteristic N OR 95% CI p-value
Chemotherapy Treatment 193


    Drug A
— —
    Drug B
1.21 0.66, 2.24 0.5
Age 183 1.02 1.00, 1.04 0.10
Grade 193


    I
— —
    II
0.95 0.45, 2.00 0.9
    III
1.10 0.52, 2.29 0.8
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

  • Specify model method, method.args, and the response variable

  • Arguments and helper functions like exponentiate, bold_*(), add_global_p() can also be used with tbl_uvregression()

Break

inline_text()

{gtsummary} reporting with inline_text()

  • Tables are important, but we often need to report results in-line.

  • Any statistic reported in a {gtsummary} table can be extracted and reported in-line in an R Markdown document with the inline_text() function.

  • The pattern of what is reported can be modified with the pattern= argument.

  • Default is pattern = "{estimate} ({conf.level*100}% CI {conf.low}, {conf.high}; {p.value})" for regression summaries.

{gtsummary} reporting with inline_text()

Characteristic N OR 95% CI p-value
Chemotherapy Treatment 193


    Drug A
— —
    Drug B
1.21 0.66, 2.24 0.5
Age 183 1.02 1.00, 1.04 0.10
Grade 193


    I
— —
    II
0.95 0.45, 2.00 0.9
    III
1.10 0.52, 2.29 0.8
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

In Code: The odds ratio for age is `r inline_text(tbl_uvreg, variable = age)`

In Report: The odds ratio for age is 1.02 (95% CI 1.00, 1.04; p=0.10)

tbl_merge()/tbl_stack()

tbl_merge() for side-by-side tables

A univariable table:

tbl_uvsurv <- 
  trial |> 
  select(age, grade, death, ttdeath) |> 
  tbl_uvregression(
    method = coxph,
    y = Surv(ttdeath, death),
    exponentiate = TRUE
  ) |> 
  add_global_p()
tbl_uvsurv
Characteristic N HR 95% CI p-value
Age 189 1.01 0.99, 1.02 0.3
Grade 200

0.075
    I
— —
    II
1.28 0.80, 2.05
    III
1.69 1.07, 2.66
Abbreviations: CI = Confidence Interval, HR = Hazard Ratio

A multivariable table:

tbl_mvsurv <- 
  coxph(
    Surv(ttdeath, death) ~ age + grade, 
    data = trial
  ) |> 
  tbl_regression(
    exponentiate = TRUE
  ) |> 
  add_global_p() 
tbl_mvsurv
Characteristic HR 95% CI p-value
Age 1.01 0.99, 1.02 0.3
Grade

0.041
    I — —
    II 1.20 0.73, 1.97
    III 1.80 1.13, 2.87
Abbreviations: CI = Confidence Interval, HR = Hazard Ratio

tbl_merge() for side-by-side tables

tbl_merge(
  list(tbl_uvsurv, tbl_mvsurv),
  tab_spanner = c("**Univariable**", "**Multivariable**")
)
Characteristic
Univariable
Multivariable
N HR 95% CI p-value HR 95% CI p-value
Age 189 1.01 0.99, 1.02 0.3 1.01 0.99, 1.02 0.3
Grade 200

0.075

0.041
    I
— —
— —
    II
1.28 0.80, 2.05
1.20 0.73, 1.97
    III
1.69 1.07, 2.66
1.80 1.13, 2.87
Abbreviations: CI = Confidence Interval, HR = Hazard Ratio

tbl_stack() to combine vertically

A univariable table:

tbl_uvsurv2 <-
  coxph(Surv(ttdeath, death) ~ trt, 
        data = trial) |>
  tbl_regression(
    show_single_row = trt,
    label = trt ~ "Drug B vs A",
    exponentiate = TRUE
  )
tbl_uvsurv2
Characteristic HR 95% CI p-value
Drug B vs A 1.25 0.86, 1.81 0.2
Abbreviations: CI = Confidence Interval, HR = Hazard Ratio

A multivariable table:

tbl_mvsurv2 <-
  coxph(Surv(ttdeath, death) ~ 
          trt + grade + stage + marker, 
        data = trial) |>
  tbl_regression(
    show_single_row = trt,
    label = trt ~ "Drug B vs A",
    exponentiate = TRUE, 
    include = "trt"
  )
tbl_mvsurv2
Characteristic HR 95% CI p-value
Drug B vs A 1.30 0.88, 1.92 0.2
Abbreviations: CI = Confidence Interval, HR = Hazard Ratio

tbl_stack() to combine vertically

list(tbl_uvsurv2, tbl_mvsurv2) |>
  tbl_stack(
    group_header = 
      c("Unadjusted", "Adjusted")
  )
Characteristic HR 95% CI p-value
Unadjusted
Drug B vs A 1.25 0.86, 1.81 0.2
Adjusted
Drug B vs A 1.30 0.88, 1.92 0.2
Abbreviations: CI = Confidence Interval, HR = Hazard Ratio

tbl_strata() for stratified tables

sm_trial |>
  mutate(grade = paste("Grade", grade)) |>
  tbl_strata(
    strata = grade,
    ~tbl_summary(.x, by = trt, missing = "no") |>
      modify_header(all_stat_cols() ~ "**{level}**")
  )
Characteristic
Grade I
Grade II
Grade III
Drug A1 Drug B1 Drug A1 Drug B1 Drug A1 Drug B1
Age 46 (36, 60) 48 (42, 55) 45 (31, 55) 51 (42, 58) 52 (42, 61) 45 (36, 52)
Tumor Response 8 (23%) 13 (41%) 7 (23%) 12 (36%) 13 (43%) 8 (24%)
1 Median (Q1, Q3); n (%)

Define custom function tbl_cmh()

Define custom function tbl_cmh()

{gtsummary} themes

{gtsummary} theme basics

  • A theme is a set of customization preferences that can be easily set and reused.

  • Themes control default settings for existing functions

  • Themes control more fine-grained customization not available via arguments or helper functions

  • Easily use one of the available themes, or create your own

{gtsummary} default theme

reset_gtsummary_theme()
m1 |>
  tbl_regression(
    exponentiate = TRUE
  ) |>
  modify_caption(
    "Default Theme"
  )
Default Theme
Characteristic OR 95% CI p-value
Age 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
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

{gtsummary} theme_gtsummary_journal()

reset_gtsummary_theme()
theme_gtsummary_journal(journal = "jama")

m1 |>
  tbl_regression(
    exponentiate = TRUE
  ) |>
  modify_caption(
    "Journal Theme (JAMA)"
  )
Journal Theme (JAMA)
Characteristic OR (95% CI) p-value
Age 1.02 (1.00 to 1.04) 0.091
T Stage

    T1 —
    T2 0.58 (0.24 to 1.37) 0.22
    T3 0.94 (0.39 to 2.28) 0.89
    T4 0.79 (0.33 to 1.90) 0.61
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

Contributions welcome!

{gtsummary} theme_gtsummary_language()

reset_gtsummary_theme()
theme_gtsummary_language(language = "zh-tw")

m1 |>
  tbl_regression(
    exponentiate = TRUE
  ) |>
  modify_caption(
    "Language Theme (Chinese)"
  )
Language Theme (Chinese)
特色 OR 95% CI P 值
Age 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
缩写注释: CI=信賴區間, OR=勝算比

Language options:

  • German
  • English
  • Spanish
  • French
  • Gujarati
  • Hindi
  • Icelandic
  • Japanese
  • Korean
  • Marathi
  • Dutch
  • Norwegian
  • Portuguese
  • Swedish
  • Chinese Simplified
  • Chinese Traditional

{gtsummary} theme_gtsummary_compact()

reset_gtsummary_theme()
theme_gtsummary_compact()

tbl_regression(m1, exponentiate = TRUE) |>
  modify_caption("Compact Theme")
Compact Theme
Characteristic OR 95% CI p-value
Age 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
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

Reduces padding and font size

{gtsummary} set_gtsummary_theme()

  • set_gtsummary_theme() to use a custom theme.

  • See the {gtsummary} + themes vignette for examples

http://www.danieldsjoberg.com/gtsummary/articles/themes.html

{gtsummary} print engines

{gtsummary} print engines

{gtsummary} print engines

Use any print engine to customize table

library(gt)
trial |>
  select(age, grade) |>
  tbl_summary() |>
  as_gt() |>
  cols_width(label ~ px(300)) |>
  cols_align(columns = stat_0, 
             align = "left")
Characteristic N = 2001
Age 47 (38, 57)
    Unknown 11
Grade
    I 68 (34%)
    II 68 (34%)
    III 64 (32%)
1 Median (Q1, Q3); n (%)

In Closing

{gtsummary} website

http://www.danieldsjoberg.com/gtsummary/

Package Authors/Contributors

Daniel D. Sjoberg

Michael Curry

Emily de le Rua

Joseph Larmarange

Jessica Lavery

Karissa Whiting

Emily C. Zabor

Xing Bai

Esther Drill

Jessica Flynn

Margie Hannum

Stephanie Lobaugh

Shannon Pileggi

Amy Tin

Gustavo Zapata Wainberg

Other Contributors

@ablack3, @ABorakati, @aghaynes, @ahinton-mmc, @aito123, @akarsteve, @akefley, @albertostefanelli, @alexis-catherine, @amygimma, @anaavu, @andrader, @angelgar, @arbet003, @arnmayer, @aspina7, @asshah4, @awcm0n, @barthelmes, @bcjaeger, @BeauMeche, @benediktclaus, @berg-michael, @bhattmaulik, @BioYork, @brachem-christian, @bwiernik, @bx259, @calebasaraba, @CarolineXGao, @ChongTienGoh, @Chris-M-P, @chrisleitzinger, @cjprobst, @clmawhorter, @CodieMonster, @coeus-analytics, @coreysparks, @ctlamb, @davidgohel, @davidkane9, @dax44, @dchiu911, @ddsjoberg, @DeFilippis, @denis-or, @dereksonderegger, @dieuv0, @discoleo, @djbirke, @dmenne, @ElfatihHasabo, @emilyvertosick, @ercbk, @erikvona, @eweisbrod, @feizhadj, @fh-jsnider, @ge-generation, @ghost, @gjones1219, @gorkang, @GuiMarthe, @hass91, @HichemLa, @hughjonesd, @iaingallagher, @ilyamusabirov, @IndrajeetPatil, @IsadoraBM, @j-tamad, @jalavery, @jeanmanguy, @jemus42, @jenifav, @jennybc, @JeremyPasco, @JesseRop, @jflynn264, @jjallaire, @jmbarajas, @jmbarbone, @JoanneF1229, @joelgautschi, @jojosgithub, @JonGretar, @jordan49er, @jthomasmock, @juseer, @jwilliman, @karissawhiting, @kendonB, @kmdono02, @kwakuduahc1, @lamhine, @larmarange, @leejasme, @loukesio, @lspeetluk, @ltin1214, @lucavd, @LuiNov, @maia-sh, @Marsus1972, @matthieu-faron, @mbac, @mdidish, @MelissaAssel, @michaelcurry1123, @mljaniczek, @moleps, @motocci, @msberends, @mvuorre, @myensr, @MyKo101, @oranwutang, @palantre, @Pascal-Schmidt, @pedersebastian, @perlatex, @philsf, @polc1410, @postgres-newbie, @proshano, @raphidoc, @RaviBot, @rich-iannone, @RiversPharmD, @rmgpanw, @roman2023, @ryzhu75, @sachijay, @saifelayan, @sammo3182, @sandhyapc, @sbalci, @sda030, @shannonpileggi, @shengchaohou, @ShixiangWang, @simonpcouch, @slb2240, @slobaugh, @spiralparagon, @StaffanBetner, @Stephonomon, @storopoli, @szimmer, @tamytsujimoto, @TarJae, @themichjam, @THIB20, @tibirkrajc, @tjmeyers, @tldrcharlene, @tormodb, @toshifumikuroda, @UAB-BST-680, @uakimix, @uriahf, @Valja64, @vvm02, @xkcococo, @yonicd, @yoursdearboy, @zabore, @zachariae, @zaddyzad, @zeyunlu, @zhengnow, @zlkrvsm, @zongell-star, and @Zoulf001.

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