Clinical Reporting with {gtsummary}

Daniel D. Sjoberg

Introduction

Acknowledgements

drawing

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA4.0).

Daniel D. Sjoberg

danieldsjoberg.com

ddsjoberg.bsky.social

linkedin.com/in/ddsjoberg

github.com/ddsjoberg

Questions

Please ask questions at any time!

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

    • 1,600,000 installations from CRAN
    • 1,100 GitHub stars
    • 1,000 citations in peer-reviewed articles
    • 350 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:
    • Demographic- or “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

{gtsummary} overview

For our workshop, we will focus on the following summary types as well as themes and print engines.

  • tbl_summary()

  • tbl_hierarchical()

Other functions helpful functions we’re not covering:

  • tbl_hierarchical_count(): similar to tbl_hierarchical() for counts instead of rates

  • tbl_cross(): cross tabulations

  • tbl_continuous(): summarizing continuous variables by 2 categorical variables

  • tbl_wide_summary(): similar to tbl_summary() but statistics are presented in separate columns

  • many more!

tbl_summary()

{pharmaverseadam} Data for Examples

Reduced sizes of adsl, adse, adlb. Created ad_onco with oncologic outcomes.

library(tidyverse)

adsl <- pharmaverseadam::adsl |> 
  filter(SAFFL == "Y") |> 
  mutate(ARM2 = word(ARM), FEMALE = SEX == "F") |> 
  labelled::set_variable_labels(FEMALE = "Female")

adae <- pharmaverseadam::adae |> 
  filter(
    USUBJID %in% adsl$USUBJID,
    AESOC %in% c("CARDIAC DISORDERS", "EYE DISORDERS"),
    AEDECOD %in% c("ATRIAL FLUTTER", "MYOCARDIAL INFARCTION", "EYE ALLERGY", "EYE SWELLING")
  ) |> 
  mutate(ARM2 = word(ARM))

adlb <- pharmaverseadam::adlb |> 
  filter(
    USUBJID %in% adsl$USUBJID, 
    PARAM %in% c("Albumin (g/L)", "Bilirubin (umol/L)" , "Leukocytes (10^9/L)"),
    AVISIT %in% c("Baseline", "Week 12", "Week 24")
  ) |> 
  mutate(ARM2 = word(ARM))

# Construct an oncology outcomes dataset
ad_onco <-
  list(
    # Best Overall Response
    pharmaverseadam::adrs_onco |> 
      filter(PARAMCD == "CBOR") |> 
      select(USUBJID, RECIST_CBOR = AVALC) |> 
      labelled::set_variable_labels(RECIST_CBOR = "Best Overall Response"),
    # Tumor Size
    pharmaverseadam::adtr_onco |> 
      filter(PARAM == "Target Lesions Sum of Diameters by Investigator") |> 
      select(USUBJID, TUMOR_SIZE = AVAL) |> 
      labelled::set_variable_labels(TUMOR_SIZE = "Tumor Size, mm"),
    # Progression-free Survival
    pharmaverseadam::adtte_onco |> 
      filter(PARAMCD == "PFS") |> 
      select(USUBJID, PFS_CNSR = CNSR, PFS_TIME = AVAL) |> 
      mutate(PFS_EVENT = abs(PFS_CNSR - 1)) |> 
      labelled::set_variable_labels(
        PFS_CNSR = "PFS, Censor",
        PFS_EVENT = "Progression",
        PFS_TIME = "PFS Followup Time, days"
      )
  ) |> 
  reduce(full_join, by = "USUBJID") |> 
  inner_join(adsl[c("USUBJID", "AGE", "ETHNIC", "ARM")], by = "USUBJID") |> 
  mutate(ARM2 = word(ARM))

Basic tbl_summary()

library(gtsummary)

adsl |> 
  tbl_summary(
    include = c(AGE, ETHNIC, FEMALE)
  )
Characteristic N = 2541
Age 77 (70, 81)
Ethnicity
    HISPANIC OR LATINO 12 (4.7%)
    NOT HISPANIC OR LATINO 242 (95%)
Female 143 (56%)
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 by default

  • Label attributes are printed automatically

Customize tbl_summary() output

adsl |> 
  tbl_summary(
    include = c(AGE, ETHNIC, FEMALE),
    by = ARM2,
  )
Characteristic Placebo
N = 861		 Xanomeline
N = 1681
Age 76 (69, 82) 77 (71, 81)
Ethnicity

    HISPANIC OR LATINO 3 (3.5%) 9 (5.4%)
    NOT HISPANIC OR LATINO 83 (97%) 159 (95%)
Female 53 (62%) 90 (54%)
1 Median (Q1, Q3); n (%)
  • by: specify a column variable for cross-tabulation

Customize tbl_summary() output

adsl |> 
  tbl_summary(
    include = c(AGE, ETHNIC, FEMALE),
    by = ARM2,
    type = AGE ~ "continuous2",
  )
Characteristic Placebo
N = 861		 Xanomeline
N = 1681
Age

    Median (Q1, Q3) 76 (69, 82) 77 (71, 81)
Ethnicity

    HISPANIC OR LATINO 3 (3.5%) 9 (5.4%)
    NOT HISPANIC OR LATINO 83 (97%) 159 (95%)
Female 53 (62%) 90 (54%)
1 n (%)

  • by: specify a column variable for cross-tabulation

  • type: specify the summary type

Customize tbl_summary() output

adsl |> 
  tbl_summary(
    include = c(AGE, ETHNIC, FEMALE),
    by = ARM2,
    type = AGE ~ "continuous2",
    statistic = 
      list(
        AGE ~ c("{mean} ({sd})", 
                "{min}, {max}"), 
        FEMALE ~ "{n} / {N} ({p}%)"
      ),
  )
Characteristic Placebo
N = 861		 Xanomeline
N = 1681
Age

    Mean (SD) 75 (9) 75 (8)
    Min, Max 52, 89 51, 88
Ethnicity

    HISPANIC OR LATINO 3 (3.5%) 9 (5.4%)
    NOT HISPANIC OR LATINO 83 (97%) 159 (95%)
Female 53 / 86 (62%) 90 / 168 (54%)
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

adsl |> 
  tbl_summary(
    include = c(AGE, ETHNIC, FEMALE),
    by = ARM2,
    type = AGE ~ "continuous2",
    statistic = 
      list(
        AGE ~ c("{mean} ({sd})", 
                "{min}, {max}"), 
        FEMALE ~ "{n} / {N} ({p}%)"
      ),
    label = 
      AGE ~ "Age, years",
  )
Characteristic Placebo
N = 861		 Xanomeline
N = 1681
Age, years

    Mean (SD) 75 (9) 75 (8)
    Min, Max 52, 89 51, 88
Ethnicity

    HISPANIC OR LATINO 3 (3.5%) 9 (5.4%)
    NOT HISPANIC OR LATINO 83 (97%) 159 (95%)
Female 53 / 86 (62%) 90 / 168 (54%)
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

adsl |> 
  tbl_summary(
    include = c(AGE, ETHNIC, FEMALE),
    by = ARM2,
    type = AGE ~ "continuous2",
    statistic = 
      list(
        AGE ~ c("{mean} ({sd})", 
                "{min}, {max}"), 
        FEMALE ~ "{n} / {N} ({p}%)"
      ),
    label = 
      AGE ~ "Age, years",
    digits = AGE ~ list(sd = 1) # report SD(age) to one decimal place
  )
Characteristic Placebo
N = 861		 Xanomeline
N = 1681
Age, years

    Mean (SD) 75 (8.6) 75 (8.1)
    Min, Max 52, 89 51, 88
Ethnicity

    HISPANIC OR LATINO 3 (3.5%) 9 (5.4%)
    NOT HISPANIC OR LATINO 83 (97%) 159 (95%)
Female 53 / 86 (62%) 90 / 168 (54%)
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

This syntax is also used in {cards}, {cardx}, {crane}, and {gt}.

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

{gtsummary} selectors

  • Use the following helpers to select groups of variables: all_continuous(), all_categorical()

  • Use all_stat_cols() to select the summary statistic columns

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

Update tbl_summary() with add_*()

adsl |>
  tbl_summary(
    by = ARM2,
    include = c(AGE, ETHNIC, FEMALE)
  ) |> 
  add_overall(last = TRUE)
Characteristic Placebo
N = 861		 Xanomeline
N = 1681		 Overall
N = 2541
Age 76 (69, 82) 77 (71, 81) 77 (70, 81)
Ethnicity


    HISPANIC OR LATINO 3 (3.5%) 9 (5.4%) 12 (4.7%)
    NOT HISPANIC OR LATINO 83 (97%) 159 (95%) 242 (95%)
Female 53 (62%) 90 (54%) 143 (56%)
1 Median (Q1, Q3); n (%)
  • add_overall(): adds a column of overall statistics

Update tbl_summary() with add_*()

ad_onco |>
  tbl_summary(
    by = ARM2, 
    include = c(TUMOR_SIZE, PFS_EVENT),
    missing = "no"
  ) |> 
  add_n() |> 
  add_p(
    pvalue_fun = \(x) round(x, 2)
  )
Characteristic N Placebo
N = 961		 Xanomeline
N = 1771		 p-value2
Tumor Size, mm 25 78 (38, 96) 67 (26, 92) 0.66
Progression 273 6 (6.3%) 9 (5.1%) 0.69
1 Median (Q1, Q3); n (%)
2 Wilcoxon rank sum test; Pearson’s Chi-squared test

  • add_n(): adds a column non-missing counts

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

Update tbl_summary() with modify_*()

tbl <-
  adsl |> 
  tbl_summary(by = ARM2, include = c("AGE", "ETHNIC", "FEMALE")) |>
  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 76 (69, 82) 77 (71, 81)
Ethnicity

    HISPANIC OR LATINO 3 (3.5%) 9 (5.4%)
    NOT HISPANIC OR LATINO 83 (97%) 159 (95%)
Female 53 (62%) 90 (54%)
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

show_header_names(tbl)
Column Name   Header                 level*             N*          n*          p*             
label         "**Characteristic**"                      254 <int>                              
stat_1        "**Group A**"             Placebo <chr>   254 <int>    86 <int>   0.339 <dbl>    
stat_2        "**Group B**"          Xanomeline <chr>   254 <int>   168 <int>   0.661 <dbl>    
* These values may be dynamically placed into headers (and other locations).
ℹ Review the `modify_header()` (`?gtsummary::modify_header()`) help for examples.



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

Update tbl_summary() with add_*()

ad_onco |>
  tbl_summary(
    by = ARM2,
    include = c(TUMOR_SIZE, PFS_EVENT),
    statistic = list(TUMOR_SIZE = "{mean} ({sd})",
                     PFS_EVENT = "{p}%"),
    missing = "no"
  ) |> 
  add_difference()
Characteristic Placebo
N = 961		 Xanomeline
N = 1771		 Difference2 95% CI2 p-value2
Tumor Size, mm 67 (37) 59 (39) 7.6 -24, 39 0.6
Progression 6.3% 5.1% 1.2% -5.5%, 7.8% >0.9
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_*()

adsl |>
  tbl_summary(
    by = ARM2,
    include = c(AGE, ETHNIC, FEMALE)
  ) |> 
  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

{gtsummary} Exercise

  1. Navigate to Posit Cloud script 06-tables-gtsummary.R.

  2. Create the table outlined in the script.

  3. Add the “completed” sticky note to your laptop when complete.

07:00

{gtsummary} Exercise

tbl <-
  df_gtsummary_exercise |>
  # ensure the age groups print in the correct order
  mutate(AGEGR1 = factor(AGEGR1, levels = c("18-64", ">64"))) |>
  tbl_summary(
    by = TRT01A,
    include = c(AGE, AGEGR1, SEX, RACE, ETHNIC, BMI, HEIGHT, WEIGHT), 
    type = all_continuous() ~ "continuous2", # all continuous variables should be summarized as multi-row
    statistic = all_continuous() ~ c("{mean} ({sd})", "{median} ({p25}, {p75})", "{min}, {max}"), # change the statistics for all continuous variables
    label = list(AGEGR1 = "Age Group"), # add a label for AGEGR1
  ) |>
  # add a header above the 'Xanomeline' treatments. We used `show_header_names()` to know the column names
  modify_spanning_header(c(stat_2, stat_3) ~ "**Active Treatment**")

tbl

{gtsummary} Exercise

Characteristic Placebo
N = 861
Active Treatment
Xanomeline High Dose
N = 721		 Xanomeline Low Dose
N = 961
Age


    Mean (SD) 75 (9) 74 (8) 76 (8)
    Median (Q1, Q3) 76 (69, 82) 76 (70, 79) 78 (71, 82)
    Min, Max 52, 89 56, 88 51, 88
Age Group


    18-64 14 (16%) 11 (15%) 8 (8.3%)
    >64 72 (84%) 61 (85%) 88 (92%)
Sex


    F 53 (62%) 35 (49%) 55 (57%)
    M 33 (38%) 37 (51%) 41 (43%)
Race


    AMERICAN INDIAN OR ALASKA NATIVE 0 (0%) 1 (1.4%) 0 (0%)
    BLACK OR AFRICAN AMERICAN 8 (9.3%) 9 (13%) 6 (6.3%)
    WHITE 78 (91%) 62 (86%) 90 (94%)
Ethnicity


    HISPANIC OR LATINO 3 (3.5%) 3 (4.2%) 6 (6.3%)
    NOT HISPANIC OR LATINO 83 (97%) 69 (96%) 90 (94%)
BMI


    Mean (SD) 23.6 (3.6) 25.3 (3.7) 25.1 (4.4)
    Median (Q1, Q3) 23.2 (21.0, 25.8) 24.6 (23.0, 27.4) 24.6 (22.1, 28.2)
    Min, Max 15.7, 34.0 19.0, 35.5 15.0, 39.8
Height, cm


    Mean (SD) 163 (12) 166 (10) 164 (10)
    Median (Q1, Q3) 163 (154, 171) 165 (157, 173) 163 (157, 170)
    Min, Max 137, 185 146, 191 136, 196
Weight, kg


    Mean (SD) 63 (13) 70 (14) 68 (15)
    Median (Q1, Q3) 60 (54, 74) 69 (57, 80) 67 (56, 78)
    Min, Max 34, 85 47, 107 41, 105
1 n (%)

tbl_hierarchicial()

Adverse Event Reporting (and friends)

Use tbl_hierarchicial() and tbl_hierarchicial_count() for reporting of AEs, Con Meds, and more.

tbl_ae <- adae |> 
  tbl_hierarchical(
    by = "ARM2", 
    variables = c("AESOC", "AEDECOD"),
    id = "USUBJID",
    denominator = adsl
  )
Primary System Organ Class
    Dictionary-Derived Term		 Placebo
N = 861		 Xanomeline
N = 1681
CARDIAC DISORDERS 4 (4.7%) 8 (4.8%)
    ATRIAL FLUTTER 0 (0%) 2 (1.2%)
    MYOCARDIAL INFARCTION 4 (4.7%) 6 (3.6%)
EYE DISORDERS 1 (1.2%) 0 (0%)
    EYE ALLERGY 1 (1.2%) 0 (0%)
    EYE SWELLING 1 (1.2%) 0 (0%)
1 n (%)

tbl_merge()/tbl_stack()

tbl_merge() for side-by-side tables

tbl_n <- 
  tbl_summary(adsl, include = ETHNIC, statistic = ETHNIC ~ "{n}") |> 
  modify_header(all_stat_cols() ~ "**N**") |> # update column header
  remove_footnote_header() # remove footnote
tbl_age <-
  tbl_continuous(adsl, include = ETHNIC, variable = AGE, by = ARM2) |> 
  modify_header(all_stat_cols() ~ "**{level}**") # update header

# combine the tables side by side
list(tbl_n, tbl_age) |> 
  tbl_merge(tab_spanner = FALSE) # suppress default header
Characteristic N Placebo1 Xanomeline1
Ethnicity


    HISPANIC OR LATINO 12 64 (63, 86) 63 (56, 78)
    NOT HISPANIC OR LATINO 242 76 (70, 82) 77 (71, 81)
1 Age: Median (Q1, Q3)

tbl_stack() to combine vertically

tbl_drug_a <- filter(adsl, ARM2 == "Placebo") |> 
  tbl_summary(include = ETHNIC)
tbl_drug_b <- filter(adsl, ARM2 == "Xanomeline") |> 
  tbl_summary(include = ETHNIC)

# stack the two tables 
list(tbl_drug_a, tbl_drug_b) |> 
  tbl_stack(group_header = c("Placebo", "Xanomeline"), quiet = TRUE) |> # optionally include headers for each table
  modify_header(all_stat_cols() ~ "**Summary Statistics**")
Characteristic Summary Statistics1
Placebo
Ethnicity
    HISPANIC OR LATINO 3 (3.5%)
    NOT HISPANIC OR LATINO 83 (97%)
Xanomeline
Ethnicity
    HISPANIC OR LATINO 9 (5.4%)
    NOT HISPANIC OR LATINO 159 (95%)
1 n (%)

Define custom function tbl_cmh()

Define custom function tbl_cmh()

Cobbling Tables Together

  • The {gtsummary} package makes it simple to break complex tables into their simple parts and cobble them together in the end.

  • Moreover, the internal structure of a gtsummary table is super simple:

    • A data frame

    • Instructions to print that data frame to make it cute.

  • Modify the underlying data frame directly with modify_table_body().

tbl_summary(adsl, include = c(AGE, ETHNIC), by = ARM2) |> 
  pluck("table_body")
# A tibble: 4 × 7
  variable var_type    row_type var_label label                  stat_1      stat_2     
  <chr>    <chr>       <chr>    <chr>     <chr>                  <chr>       <chr>      
1 AGE      continuous  label    Age       Age                    76 (69, 82) 77 (71, 81)
2 ETHNIC   categorical label    Ethnicity Ethnicity              <NA>        <NA>       
3 ETHNIC   categorical level    Ethnicity HISPANIC OR LATINO     3 (3.5%)    9 (5.4%)   
4 ETHNIC   categorical level    Ethnicity NOT HISPANIC OR LATINO 83 (97%)    159 (95%)

ARDs

Where are the ARDs?

Where are the ARDs?

  • ARDs are the backbone for all calculations in gtsummary

  • Every gtsummary table saves the ARDs from each calculation

tbl <- tbl_summary(adsl, by = "ARM2", include = "AGE")
gather_ard(tbl)
$tbl_summary
{cards} data frame: 27 x 12
   group1 group1_level variable variable_level stat_name stat_label      stat
1    ARM2      Placebo      AGE                   median     Median        76
2    ARM2      Placebo      AGE                      p25         Q1        69
3    ARM2      Placebo      AGE                      p75         Q3        82
4    ARM2    Xanomeli…      AGE                   median     Median        77
5    ARM2    Xanomeli…      AGE                      p25         Q1        71
6    ARM2    Xanomeli…      AGE                      p75         Q3        81
7    <NA>                   AGE                    label  Variable…       Age
8    <NA>                   AGE                    class  Variable…   numeric
9    <NA>                  ARM2                    label  Variable…      ARM2
10   <NA>                  ARM2                    class  Variable… character
ℹ 17 more rows
ℹ Use `print(n = ...)` to see more rows
ℹ 5 more variables: context, fmt_fun, warning, error, gts_column

ARD + QC

ARDs are wonderful for QCing {gtsummary} tables. 😻

  • ARDs include the formatted and un-formatted numbers that appear in the table.

  • Extract the ARD from the {gtsummary} table.

  • Build fresh ARD from source data, and compare it to the ARD from the table.

ARD + QC: Build and Compare ARDs

ard_demog <- adsl |> 
  cards::ard_stack(
    cards::ard_summary(
      variables = "AGE",
      statistic = 
        AGE ~ cards::continuous_summary_fns(c("median", "p25", "p75"))
    ),
    .by = "ARM2", 
  )

The next step is to simply compare the two ARDs to confirm results. As this is done programmatically, it is quick to repeat as data continues to accrue.

There are numerous ways to compare objects in R. We are currently developing a tool in {cards} that will streamline ARD comparison, with sensible defaults for ARDs. Stay tuned! 📺

ARD-first tables

ARD-first Tables

Similar to functions that accept a data frame, the package exports functions with nearly identical APIs that accept an ARD.

tbl_summary()

tbl_hierarchical()

tbl_continuous()

tbl_wide_summary()
tbl_ard_summary()

tbl_ard_hierarchical()

tbl_ard_continuous()

tbl_ard_wide_summary()

ARD-first Tables

We can use the skills we learned earlier today to create ARDs for gtsummary tables.

library(cards)

ard <- ard_stack(
  data = adsl, 
  ard_summary(variables = AGE),
  ard_tabulate(variables = ETHNIC),
  ard_tabulate_value(variables = FEMALE),
  # add these for best-looking tables
  .attributes = TRUE, 
  .missing = TRUE 
)
ard
{cards} data frame: 38 x 9
   variable variable_level  context stat_name stat_label   stat
1       AGE                 summary         N          N    254
2       AGE                 summary      mean       Mean 75.087
3       AGE                 summary        sd         SD  8.246
4       AGE                 summary    median     Median     77
5       AGE                 summary       p25         Q1     70
6       AGE                 summary       p75         Q3     81
7       AGE                 summary       min        Min     51
8       AGE                 summary       max        Max     89
9    ETHNIC      HISPANIC… tabulate         n          n     12
10   ETHNIC      HISPANIC… tabulate         N          N    254
ℹ 28 more rows
ℹ Use `print(n = ...)` to see more rows
ℹ 3 more variables: fmt_fun, warning, error

ARD-first Tables

We can simply use the ARD from the previous slide, and pass it to tbl_ard_summary() for a summary table.

tbl_ard_summary(ard)
Characteristic Overall1
Age 77.0 (70.0, 81.0)
Ethnicity
    HISPANIC OR LATINO 12 (4.7%)
    NOT HISPANIC OR LATINO 242 (95.3%)
Female 143 (56.3%)
1 Median (Q1, Q3); n (%)

ARD-first Table Shells

adsl |> 
  labelled::set_variable_labels(AGE = "Age, years") |> 
  ard_stack( 
    .by = ARM2,
    ard_tabulate(variables = ETHNIC),
    # add these for best-looking tables
    .attributes = TRUE, 
    .missing = TRUE 
  ) |> 
  cards::update_ard_fmt_fun(stat_names = c("n", "p"), fmt_fun = \(x) "xx") |> 
  tbl_ard_summary(
    by = ARM2,
    type = all_continuous() ~ "continuous2",
    statistic = all_continuous() ~ c("{mean} ({sd})", "{min} - {max}"),
    missing = "no"
  ) |> 
  modify_header(all_stat_cols() ~ "**{level}**  \nN = xx")

ARD-first Table Shells

Characteristic Placebo
N = xx1		 Xanomeline
N = xx1
Ethnicity

    HISPANIC OR LATINO xx (xx%) xx (xx%)
    NOT HISPANIC OR LATINO xx (xx%) xx (xx%)
1 n (%)

{gtsummary} print engines

{gtsummary} print engines

Adopt {gtsummary}

How to Adopt {gtsummary}

Write functions

  • Use {gtsummary} functions to build new functions will be key for adoption.

Create a theme

  • 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

Extension Package:{crane}

The first function we added to {crane} was tbl_roche_summary(): a thin wrapper for gtsummary::tbl_summary().

  • Continuous variables default to continuous2.

  • tbl_summary(missing*) arguments have been changed to tbl_roche_summary(nonmissing*).

    • We highlight non-missing counts over missing counts, which are the default in {gtsummary}

  • Counts represented by 0 (0%) print as 0.

library(crane)

adsl |> 
  dplyr::mutate(ETHNIC = forcats::fct_expand(ETHNIC, "REFUSED")) |> 
  tbl_roche_summary(
    by = ARM2, 
    include = c(AGE, ETHNIC),
    nonmissing = "always"
  )

Extension Package:{crane}

Table 1
Placebo
(N = 86)		 Xanomeline
(N = 168)
Age

    n 86 168
    Mean (SD) 75 (9) 75 (8)
    Median 76 77
    Min - Max 52 - 89 51 - 88
ETHNIC

    n 86 168
    HISPANIC OR LATINO 3 (3.5%) 9 (5.4%)
    NOT HISPANIC OR LATINO 83 (97%) 159 (95%)
    REFUSED 0 0

What else is in {crane}?

Lab values are summarized by visit and include the change from baseline.

This is a simple table that is just a tbl_merge() of the AVAL summary and the CHG summary.

But the general structure appears enough times in our catalog, we make it simple for our programmers to create.

adlb |> 
  dplyr::filter(PARAM == "Albumin (g/L)") |> 
  tbl_baseline_chg(
    by = "ARM2",
    baseline_level = "Baseline",
    denominator = adsl
  )

What else is in {crane}?

What else is in {crane}?

What else is in {crane}?

What else is in {crane}?

A pharma theme with {crane}

Our theme is implemented in crane::theme_gtsummary_roche()

Primary changes include:

  • Sets a custom function for rounding percentages.

  • Round all p-values to four decimal places.

  • Headers default to presenting with bold and include N in parenthesis, e.g. 'Placebo \n (N = 184)'.

  • All tables are printed with {flextable} and we add Roche-specific styling to the table.

  • Update the default font, font size, table borders, cell padding, etc. to meet our guidelines.

What else is in {crane}? 

theme_gtsummary_roche()

adsl |> 
  dplyr::mutate(ETHNIC = forcats::fct_expand(ETHNIC, "REFUSED")) |> 
  tbl_roche_summary(
    by = ARM2, 
    include = c(AGE, ETHNIC),
    nonmissing = "always"
  )

Placebo
(N = 86)

Xanomeline
(N = 168)

Age

n

86

168

Mean (SD)

75 (9)

75 (8)

Median

76

77

Min - Max

52 - 89

51 - 88

ETHNIC

n

86

168

HISPANIC OR LATINO

3 (3.5%)

9 (5.4%)

NOT HISPANIC OR LATINO

83 (96.5%)

159 (94.6%)

REFUSED

0

0

cardinal collaboration

The cardinal initiative is an industry collaborative effort under the pharmaverse.

The site includes examples for building ARDs and tables from the FDA Standard Safety Tables and Figures Integrated Guide using {cards} and {gtsummary}.

Let’s Go!

In Closing

{pharmaverse} 30-day CRAN Downloads

{gtsummary} website

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

Package Authors/Contributors

Daniel D. Sjoberg

Joseph Larmarange

Michael Curry

Jessica Lavery

Karissa Whiting

Emily C. Zabor

Xing Bai

Esther Drill

Jessica Flynn

Margie Hannum

Stephanie Lobaugh

Shannon Pileggi

Amy Tin

Gustavo Zapata Wainberg

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