Instructions:

Recommended: Complete these exercises in the dedicated R in Pharma RStudio Cloud work space, which comes with

all packages pre-installed, and
an Rmarkdown document to fill in.

It may still be helpful to peek here to verify that your tables match the desired output.

Click here to enter R in Pharma RStudio Cloud work space for {gtreg}

Otherwise: Follow along this document, work on your personal computer, and challenge yourself not to peek at the code solutions until you have completed the exercise.

Packages

library(tidyverse)
library(gtsummary)
library(gtreg)
library(labelled)
library(admiral)       # for subject data   
library(admiral.test)  # for ae data

Output display setting

theme_gtsummary_compact()

The Data

For these exercises we will be using STDM and ADaM datasets from the {admiral.test} and {admiral} and packages.

First, prepare a subject level data set named subjects from the admiral_adsl data set with only the variables USUBJID, ACTARM, AGE, SEX, RACE and subjects who screen failed removed.

dat_subjects <- admiral_adsl %>% 
  filter(ACTARM != "Screen Failure" & ACTARM %in% c("Placebo", "Xanomeline High Dose") ) %>% 
  select(USUBJID, ACTARM, AGE, SEX, RACE) %>% 
  mutate(across(RACE, str_to_title)) %>% 
  mutate(
    # convert treatment arm and race to factors for table displays in a specific order
    ACTARM = fct_relevel(ACTARM, "Placebo", "Xanomeline High Dose"),
    RACE = fct_infreq(RACE)
  ) %>% 
  set_variable_labels(
    ACTARM = "Actual treatment arm",
    RACE = "Race"
  )

Next, prepare an adverse event data set named ae from admiral_ae with the only the variables USUBJID, AESOC, AEDECOD, AESEV, AESER, AEREL, AESTDTC.

Join actual treatment arm (ACTARM) from the subjects data frame to the ae data.

dat_ae <- admiral_ae %>% 
  select(USUBJID, AESOC, AEDECOD, AESEV, AESER, AEREL, AESTDTC) %>% 
  left_join(dat_subjects %>% select(USUBJID, ACTARM), by = "USUBJID") %>% 
  # removing subjects  with missing treatment arm as these were the low dose 
  # subjects excluded from the subjects data set
  drop_na(ACTARM) %>% 
  mutate(
    AESEV = fct_relevel(AESEV, "MILD", "MODERATE", "SEVERE"),
    # converting treatment arm to factor
    # note that treatment arm must be stored the same way in the two data sets
    ACTARM = fct_relevel(ACTARM, "Placebo", "Xanomeline High Dose"),
    year = str_sub(AESTDTC, start = 1, end = 4) %>% as.numeric()
   ) %>%
  mutate(across(c(AESOC, AEDECOD, AESEV, AEREL), str_to_title)) %>% 
  # for brevity of output, only look at AEs from a single year and in
  # a few SOC categories
  filter(year == 2014) %>% 
  filter(AESOC %in% c("Eye Disorders", "Nervous System Disorders", "Skin And Subcutaneous Tissue Disorders")) %>%
  labelled::set_variable_labels(
     AESOC    = "Primary System Organ Class", 
     AEDECOD  = "Dictionary-Derived Term", 
     AESEV    = "Severity/Intensity",
     AEREL    = "Causality" 
  )

Exercise 1

Create an adverse event table saved as t1 that summarizes adverse events.

Summarize the adverse events by both treatment arm and severity.
Supply id_df to ensure the subject denominator is correct.

Show the code solution

t1 <- dat_ae %>%
  tbl_ae(
    id_df = dat_subjects,
    id = USUBJID,
    ae = AEDECOD,
    soc = AESOC,
    by = AESEV,
    strata = ACTARM
  ) 

t1

Adverse Event	Placebo, N = 86			Xanomeline High Dose, N = 72
Adverse Event	Mild	Moderate	Severe	Mild	Moderate	Severe
Eye Disorders	1 (1.2)	1 (1.2)	—	—	—	—
Conjunctivitis	—	1 (1.2)	—	—	—	—
Eye Allergy	1 (1.2)	—	—	—	—	—
Eye Pruritus	1 (1.2)	—	—	—	—	—
Eye Swelling	1 (1.2)	—	—	—	—	—
Nervous System Disorders	2 (2.3)	—	—	2 (2.8)	—	1 (1.4)
Burning Sensation	—	—	—	1 (1.4)	—	—
Dizziness	—	—	—	—	—	1 (1.4)
Headache	1 (1.2)	—	—	—	—	—
Parosmia	—	—	—	1 (1.4)	—	—
Psychomotor Hyperactivity	1 (1.2)	—	—	—	—	—
Skin And Subcutaneous Tissue Disorders	9 (10)	2 (2.3)	—	7 (9.7)	5 (6.9)	—
Actinic Keratosis	—	—	—	1 (1.4)	—	—
Alopecia	1 (1.2)	—	—	—	—	—
Drug Eruption	1 (1.2)	—	—	—	—	—
Erythema	3 (3.5)	—	—	4 (5.6)	2 (2.8)	—
Hyperhidrosis	2 (2.3)	—	—	2 (2.8)	—	—
Pruritus	3 (3.5)	—	—	6 (8.3)	3 (4.2)	—
Pruritus Generalised	—	—	—	1 (1.4)	—	—
Rash	1 (1.2)	2 (2.3)	—	—	1 (1.4)	—
Skin Irritation	—	—	—	1 (1.4)	—	—
Skin Odour Abnormal	—	—	—	1 (1.4)	—	—

Exercise 2

Create t1_modify that modifies t1 such that:

Bold the system organ class rows.
Add an overall column to each treatment arm.
Label the overall column as Total.
In the header, place the number of subjects on a new line in each treatment arm.
Add a table caption that states 2014 Disorders Adverse Events (displays at the top of the table).

Show the code solution

t1_modify <- t1 %>% 
  bold_labels() %>%
  add_overall(across = 'by') %>% 
  modify_header(all_overall_cols() ~ "**Total**") %>%
  modify_spanning_header(all_ae_cols(TRUE, TRUE) ~ "**{strata}**  \nN = {n}") %>%
  modify_caption("2014 Disorders Adverse Events")  

t1_modify

2014 Disorders Adverse Events
Adverse Event	Placebo N = 86				Xanomeline High Dose N = 72
Adverse Event	Mild	Moderate	Severe	Total	Mild	Moderate	Severe	Total
Eye Disorders	1 (1.2)	1 (1.2)	—	2 (2.3)	—	—	—	—
Conjunctivitis	—	1 (1.2)	—	1 (1.2)	—	—	—	—
Eye Allergy	1 (1.2)	—	—	1 (1.2)	—	—	—	—
Eye Pruritus	1 (1.2)	—	—	1 (1.2)	—	—	—	—
Eye Swelling	1 (1.2)	—	—	1 (1.2)	—	—	—	—
Nervous System Disorders	2 (2.3)	—	—	2 (2.3)	2 (2.8)	—	1 (1.4)	3 (4.2)
Burning Sensation	—	—	—	—	1 (1.4)	—	—	1 (1.4)
Dizziness	—	—	—	—	—	—	1 (1.4)	1 (1.4)
Headache	1 (1.2)	—	—	1 (1.2)	—	—	—	—
Parosmia	—	—	—	—	1 (1.4)	—	—	1 (1.4)
Psychomotor Hyperactivity	1 (1.2)	—	—	1 (1.2)	—	—	—	—
Skin And Subcutaneous Tissue Disorders	9 (10)	2 (2.3)	—	11 (13)	7 (9.7)	5 (6.9)	—	12 (17)
Actinic Keratosis	—	—	—	—	1 (1.4)	—	—	1 (1.4)
Alopecia	1 (1.2)	—	—	1 (1.2)	—	—	—	—
Drug Eruption	1 (1.2)	—	—	1 (1.2)	—	—	—	—
Erythema	3 (3.5)	—	—	3 (3.5)	4 (5.6)	2 (2.8)	—	6 (8.3)
Hyperhidrosis	2 (2.3)	—	—	2 (2.3)	2 (2.8)	—	—	2 (2.8)
Pruritus	3 (3.5)	—	—	3 (3.5)	6 (8.3)	3 (4.2)	—	9 (12)
Pruritus Generalised	—	—	—	—	1 (1.4)	—	—	1 (1.4)
Rash	1 (1.2)	2 (2.3)	—	3 (3.5)	—	1 (1.4)	—	1 (1.4)
Skin Irritation	—	—	—	—	1 (1.4)	—	—	1 (1.4)
Skin Odour Abnormal	—	—	—	—	1 (1.4)	—	—	1 (1.4)

Exercise 3

Create a descriptive statistics table from subjects named t2 using tbl_reg_summary.

Summarize the variables AGE, SEX and RACE by treatment arm (ACTARM).
Add an overall column to summarize across both treatment arms.
Bold the variable labels.
In the header, place the number of subjects on a new line in each treatment arm.

Show the code solution

t2 <- dat_subjects %>% 
  select(-USUBJID) %>% 
  tbl_reg_summary(
    by = ACTARM
  ) %>% 
  add_overall(last = TRUE) %>% 
  bold_labels() %>% 
  modify_header(
    list(
      all_stat_cols(stat_0 = FALSE) ~ "**{level}**  \nN = {n}",
      stat_0 ~ "**Overall**  \nN = {N}"
    ))

t2

Characteristic	Placebo N = 86¹	Xanomeline High Dose N = 72¹	Overall N = 158¹
Age
N	86	72	158
Mean (SD)	75 (9)	74 (8)	75 (8)
Median (IQR)	76 (69, 82)	76 (70, 79)	76 (69, 81)
Range	52, 89	56, 88	52, 89
N missing	0	0	0
Sex
F	53 (62%)	35 (49%)	88 (56%)
M	33 (38%)	37 (51%)	70 (44%)
Race
White	78 (91%)	62 (86%)	140 (89%)
Black Or African American	8 (9.3%)	9 (12%)	17 (11%)
American Indian Or Alaska Native	0 (0%)	1 (1.4%)	1 (0.6%)
¹ n (%)

Exercise 4

Create a grouped table listing of adverse events named t3.

Display only events of Probable causality.
Sort events by system organ class and term.
Print subject id, term, and whether or not the event was serious.
Group listings by system organ class.
Bold system organ class labels.

Show the code solution

dat_list <- dat_ae %>% 
  filter(AEREL == "Probable") %>% 
  select(USUBJID, AESOC, AEDECOD, AESER) %>% 
  arrange(AESOC, AEDECOD) 

t3 <- dat_list %>% 
  tbl_listing(
    group_by = AESOC
  ) %>% 
  bold_labels() 

t3

Unique Subject Identifier	Dictionary-Derived Term	Serious Event
Nervous System Disorders
01-704-1332	Burning Sensation	N
Skin And Subcutaneous Tissue Disorders
01-704-1074	Erythema	N
01-708-1158	Erythema	N
01-714-1288	Erythema	N
01-716-1177	Erythema	N
01-716-1441	Erythema	N
01-716-1447	Erythema	N
01-704-1074	Pruritus	N
01-704-1332	Pruritus	N
01-704-1332	Pruritus	N
01-716-1447	Pruritus	N
01-717-1109	Pruritus Generalised	N
01-709-1306	Rash	N
01-709-1306	Rash	N
01-709-1306	Rash	N
01-717-1109	Skin Irritation	N

Exercise 5

Create t2_shell, a table shell from the t2 table. This is the exact same format as t2, except with numeric values replaced by xx.

Show the code solution

t2_shell <- dat_subjects %>% 
  select(-USUBJID) %>% 
  tbl_reg_summary(
    digits = everything() ~  style_xxx,
    by = ACTARM
  ) %>% 
  add_overall(last = TRUE) %>% 
  bold_labels() %>% 
  modify_header(
    list(
      all_stat_cols(stat_0 = FALSE) ~ "**{level}**  \nN = xx",
      stat_0 ~ "**Overall**  \nN = xx"
    ))

t2_shell

Characteristic	Placebo N = xx¹	Xanomeline High Dose N = xx¹	Overall N = xx¹
Age
N	xx	xx	xx
Mean (SD)	xx (xx)	xx (xx)	xx (xx)
Median (IQR)	xx (xx, xx)	xx (xx, xx)	xx (xx, xx)
Range	xx, xx	xx, xx	xx, xx
N missing	xx	xx	xx
Sex
F	xx (xx%)	xx (xx%)	xx (xx%)
M	xx (xx%)	xx (xx%)	xx (xx%)
Race
White	xx (xx%)	xx (xx%)	xx (xx%)
Black Or African American	xx (xx%)	xx (xx%)	xx (xx%)
American Indian Or Alaska Native	xx (xx%)	xx (xx%)	xx (xx%)
¹ n (%)

Exercise 6

Export t2_shell to either word or excel.

Show the code solution

t2_shell %>%  
 as_hux_xlsx(
   file = "output/summary_shell.xlsx"
   )

Show the code solution

t2_shell %>%  
  as_flex_table() %>% 
  flextable::save_as_docx(path = "output/summary_shell.docx")

Session info

devtools::session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.2.1 (2022-06-23 ucrt)
 os       Windows 10 x64 (build 19042)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  English_United States.utf8
 ctype    English_United States.utf8
 tz       America/New_York
 date     2022-11-01
 pandoc   2.18 @ C:/Program Files/RStudio/bin/quarto/bin/tools/ (via rmarkdown)

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 [1] C:/Users/pileggis/Documents/gh-personal/gtreg-workshop-rinpharma-2022/renv/library/R-4.2/x86_64-w64-mingw32
 [2] C:/Users/pileggis/AppData/Local/Temp/RtmpO65FZZ/renv-system-library

 P ── Loaded and on-disk path mismatch.

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