Example 4: Adverse Events Table
The fourth example produces an Adverse Events table by severity, with treatment groups on separate pages. The report shows statistics for All Adverse Events and System Organ Class/Preferred Term.
Program
Note the following about this example:
- The
libname()function from the libr package makes it easy to load the entire data library. - Functions from the procs package make the
code look almost like a SAS® program.
- The reporter package is
able to wrap treatment groups to separate pages using the
page_wrap property.
- The reporter package is also able to easily handle page breaks, while still respecting the established margins.
library(sassy)
options("logr.autolog" = TRUE,
"logr.notes" = FALSE,
"logr.on" = TRUE,
"procs.print" = FALSE)
# Get temp directory
tmp <- tempdir()
# Open log
lf <- log_open(file.path(tmp, "example4.log"))
# Get data
dir <- system.file("extdata", package = "sassy")
# Get Data ----------------------------------------------------------------
sep("Prepare Data")
# Create libname for csv data
libname(sdtm, dir, "csv", quiet = TRUE)
put("Filter DM data")
datastep(sdtm$DM,
keep = v(USUBJID, ARM, ARMCD),
where = expression(ARM != "SCREEN FAILURE"), {}) -> dm
put("Get population counts")
proc_freq(dm, tables = ARM,
output = long,
options = v(nopercent, nonobs)) -> arm_pop
put ("Create lookup for AE severity")
sevn <- c(MILD = 1, MODERATE = 2, SEVERE = 3) |> put()
put("Prepare table data")
datastep(sdtm$AE, merge = dm,
merge_by = "USUBJID",
merge_in = v(inA, inB),
keep = v(USUBJID, ARM, AESEV, AESEVN, AESOC, AEDECOD),
where = expression(inB == 1 & inA != 0),
{
AESEVN <- fapply(AESEV, sevn)
}) -> ae_sub
# Prepare Formats ---------------------------------------------------------
sep("Prepare Formats")
fc <- fcat(CNT = "%3d",
PCT = "(%5.1f)",
CAT2 = c(MILD = "Mild",
MODERATE = "Moderate",
SEVERE = "Severe"))
# Perform Calculations ----------------------------------------------------
sep("Perform Calculations")
put("Get SOC Frequencies")
proc_freq(ae_sub,
tables = v(AESOC * AESEV),
by = "ARM") -> ae_soc
put("Combine columns for SOC")
datastep(ae_soc,
format = fc,
rename = list(VAR1 = "VAR", CAT1 = "CAT"),
drop = v(VAR2, CNT, PCT),
{
VARORD <- 1
CNTPCT <- fapply2(CNT, PCT)
CAT2 <- fapply(CAT2)
}) -> ae_soc_c
put("Pivot SOC frequencies")
proc_transpose(ae_soc_c, id = v(BY, CAT2),
var = CNTPCT,
copy = v(VAR, VARORD),
by = CAT) -> ae_soc_t
put("Get PT Frequencies")
proc_freq(ae_sub,
tables = "AEDECOD * AESEV",
by = "ARM",
options = nonobs) -> ae_pt
put("Get unique SOC and PT combinations")
proc_sort(ae_sub, keep = v(AESOC, AEDECOD),
by = v(AESOC, AEDECOD), options = nodupkey) -> evnts
put("Combine columns for PT")
datastep(ae_pt,
format = fc,
rename = list(VAR1 = "VAR", CAT1 = "CAT"),
drop = v(VAR2, CNT, PCT),
{
VARORD <- 2
CNTPCT <- fapply2(CNT, PCT)
CAT2 <- fapply(CAT2)
}) -> ae_pt_c
put("Pivot PT frequencies")
proc_transpose(ae_pt_c, id = v(BY, CAT2),
var = CNTPCT,
copy = v(VAR, VARORD),
by = CAT) -> ae_pt_t
nms <- names(ae_soc_t)
put("Join in SOC")
datastep(ae_pt_t, merge = evnts, rename = c(CAT = "CAT2", AESOC = "CAT"),
merge_by = c(CAT = "AEDECOD"), {
CAT <- toTitleCase(tolower(CAT))
}) -> ae_pt_tj
put("Stack SOC and PT counts")
datastep(ae_soc_t, set = ae_pt_tj,
keep = c("VAR", "CAT", "CAT2", "VARORD",
find.names(ae_pt_tj, "ARM*")), {}) -> ae_soc_pt
aefinal <- proc_sort(ae_soc_pt, by = v( CAT, VARORD, CAT2))
# All Adverse Events ------------------------------------------------------
put("Get frequencies for all events")
proc_freq(ae_sub, tables = "AESEV", by = v(ARM)) -> allfreq
put("Combine all events.")
datastep(allfreq, format = fc,
drop = v(N, CNT, PCT),
{
CNTPCT <- fapply2(CNT, PCT)
CAT <- fapply(CAT, fc$CAT2)
}) -> allfreqm
put("Prepare data for reporting")
proc_transpose(allfreqm, id = v(BY, CAT),
var = CNTPCT, copy = VAR, name = CAT) -> allfreqt
# Final Data --------------------------------------------------------------
sep("Create final data frame")
datastep(allfreqt, set = aefinal,
keep = names(aefinal),
{
if (VAR == "AESEV")
CAT <- "All Adverse Events"
}) -> allfinal
# Print Report ----------------------------------------------------------
sep("Create and print report")
put("Create table object")
tbl <- create_table(allfinal, first_row_blank = TRUE, width = 9) |>
column_defaults(from = `ARM A.Mild`, to = `ARM D.Severe`, width = 1, align = "center") |>
spanning_header("ARM A.Mild", "ARM A.Severe", label = "ARM A", n = arm_pop["ARM A"]) |>
spanning_header("ARM B.Mild", "ARM B.Severe", label = "ARM B", n = arm_pop["ARM B"]) |>
spanning_header("ARM C.Mild", "ARM C.Severe", label = "ARM C", n = arm_pop["ARM C"]) |>
spanning_header("ARM D.Mild", "ARM D.Severe", label = "ARM D", n = arm_pop["ARM D"]) |>
stub(vars = c("CAT", "CAT2"), label = "System Organ Class\n Preferred Term", width = 5) |>
define(CAT, blank_after = TRUE) |>
define(CAT2, indent = .25) |>
define(`ARM A.Mild`, label = "Mild") |>
define(`ARM A.Moderate`, label = "Moderate") |>
define(`ARM A.Severe`, label = "Severe") |>
define(`ARM B.Mild`, label = "Mild", page_wrap = TRUE) |>
define(`ARM B.Moderate`, label = "Moderate") |>
define(`ARM B.Severe`, label = "Severe") |>
define(`ARM C.Mild`, label = "Mild", page_wrap = TRUE) |>
define(`ARM C.Moderate`, label = "Moderate") |>
define(`ARM C.Severe`, label = "Severe") |>
define(`ARM D.Mild`, label = "Mild", page_wrap = TRUE) |>
define(`ARM D.Moderate`,label = "Moderate") |>
define(`ARM D.Severe`, label = "Severe") |>
define(VAR, visible = FALSE) |>
define(VARORD, visible = FALSE)
put("Create report object")
rpt <- create_report(file.path(tmp, "example4.rtf"), output_type = "RTF", font = "Arial") |>
options_fixed(font_size = 10) |>
page_header("Sponsor: Company", "Study: ABC") |>
titles("Table 5.0", "Adverse Events by Maximum Severity", bold = TRUE) |>
add_content(tbl) |>
footnotes("Program: AE_Table.R",
"Note: Adverse events were coded using MedDRA Version 9.1") |>
page_footer(Sys.time(), "Confidential", "Page [pg] of [tpg]")
put("Print report")
res <- write_report(rpt)
# Clean Up ----------------------------------------------------------------
sep("Clean Up")
put("Close log")
log_close()
# Uncomment to view report
# file.show(res$modified_path)
# Uncomment to view log
# file.show(lf)Output
Here are the first three pages of the output report:
Log
Here is part of the log from the above example:
=========================================================================
Log Path: C:/Users/dbosa/AppData/Local/Temp/RtmpEBPgPu/log/example4.log
Program Path: C:/packages/Testing/procs/ProcsAE.R
Working Directory: C:/packages/Testing/procs
User Name: dbosa
R Version: 4.3.1 (2023-06-16 ucrt)
Machine: SOCRATES x86-64
Operating System: Windows 10 x64 build 22621
Base Packages: stats graphics grDevices utils datasets methods base Other
Packages: tidylog_1.0.2 stringr_1.5.0 procs_1.0.3 reporter_1.4.1 libr_1.2.8
fmtr_1.5.9 logr_1.3.4 common_1.0.8 sassy_1.1.0
Log Start Time: 2023-09-05 22:26:16.754728
=========================================================================
=========================================================================
Prepare Data
=========================================================================
# library 'sdtm': 7 items
- attributes: csv not loaded
- path: C:/Users/dbosa/AppData/Local/R/win-library/4.3/sassy/extdata
- items:
Name Extension Rows Cols Size
1 AE csv 150 27 88.5 Kb
2 DM csv 87 24 45.5 Kb
3 DS csv 174 9 34.1 Kb
4 EX csv 84 11 26.4 Kb
5 IE csv 2 14 13.4 Kb
6 SV csv 685 10 70.3 Kb
7 VS csv 3358 17 467.4 Kb
LastModified
1 2023-08-07 17:51:40
2 2023-08-07 17:51:40
3 2023-08-07 17:51:40
4 2023-08-07 17:51:40
5 2023-08-07 17:51:40
6 2023-08-07 17:51:40
7 2023-08-07 17:51:40
Filter DM data
datastep: columns decreased from 24 to 3
# A tibble: 85 × 3
USUBJID ARM ARMCD
<chr> <chr> <chr>
1 ABC-01-049 ARM D 4
2 ABC-01-050 ARM B 2
3 ABC-01-051 ARM A 1
4 ABC-01-052 ARM C 3
5 ABC-01-053 ARM B 2
6 ABC-01-054 ARM D 4
7 ABC-01-055 ARM C 3
8 ABC-01-056 ARM A 1
9 ABC-01-113 ARM D 4
10 ABC-01-114 ARM B 2
# ℹ 75 more rows
# ℹ Use `print(n = ...)` to see more rows
Get population counts
proc_freq: input data set 85 rows and 3 columns
tables: ARM
output: long
view: TRUE
output: 1 datasets
# A tibble: 1 × 6
VAR STAT `ARM A` `ARM B` `ARM C` `ARM D`
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 ARM CNT 20 21 21 23
Create lookup for AE severity
MILD MODERATE SEVERE
1 2 3
Prepare table data
datastep: columns decreased from 27 to 6
# A tibble: 145 × 6
USUBJID ARM AESEV AESEVN AESOC AEDECOD
<chr> <chr> <chr> <dbl> <chr> <chr>
1 ABC-01-049 ARM D MODERATE 2 Invest… BLOOD …
2 ABC-01-049 ARM D MODERATE 2 Invest… BLOOD …
3 ABC-01-049 ARM D MILD 1 Muscul… MUSCUL…
4 ABC-01-049 ARM D MILD 1 Nervou… HEADAC…
5 ABC-01-049 ARM D MODERATE 2 Invest… LABORA…
6 ABC-01-050 ARM B MILD 1 Respir… UPPER …
7 ABC-01-050 ARM B MILD 1 Skin a… RASH
8 ABC-01-051 ARM A MILD 1 Nervou… HEADAC…
9 ABC-01-051 ARM A MILD 1 Nervou… HEADAC…
10 ABC-01-051 ARM A MILD 1 Genera… INFLUE…
# ℹ 135 more rows
# ℹ Use `print(n = ...)` to see more rows
=========================================================================
Prepare Formats
=========================================================================
# A format catalog: 3 formats
- $CNT: type S, "%3d"
- $PCT: type S, "(%5.1f)"
- $CAT2: type V, 3 elements
=========================================================================
Perform Calculations
=========================================================================
Get SOC Frequencies
proc_freq: input data set 145 rows and 6 columns
tables: AESOC * AESEV
by: ARM
view: TRUE
output: 1 datasets
# A tibble: 240 × 8
BY VAR1 VAR2 CAT1 CAT2 N CNT PCT
<chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
1 ARM A AESOC AESEV Blood… MILD 37 0 0
2 ARM A AESOC AESEV Blood… MODE… 37 0 0
3 ARM A AESOC AESEV Blood… SEVE… 37 0 0
4 ARM A AESOC AESEV Cardi… MILD 37 0 0
5 ARM A AESOC AESEV Cardi… MODE… 37 0 0
6 ARM A AESOC AESEV Cardi… SEVE… 37 0 0
7 ARM A AESOC AESEV Conge… MILD 37 0 0
8 ARM A AESOC AESEV Conge… MODE… 37 0 0
9 ARM A AESOC AESEV Conge… SEVE… 37 0 0
10 ARM A AESOC AESEV Ear a… MILD 37 0 0
# ℹ 230 more rows
# ℹ Use `print(n = ...)` to see more rows
Combine columns for SOC
datastep: columns decreased from 8 to 7
# A tibble: 240 × 7
BY VAR CAT CAT2 N VARORD CNTPCT
<chr> <chr> <chr> <chr> <dbl> <dbl> <chr>
1 ARM A AESOC Blood and… Mild 37 1 " 0 …
2 ARM A AESOC Blood and… Mode… 37 1 " 0 …
3 ARM A AESOC Blood and… Seve… 37 1 " 0 …
4 ARM A AESOC Cardiac d… Mild 37 1 " 0 …
5 ARM A AESOC Cardiac d… Mode… 37 1 " 0 …
6 ARM A AESOC Cardiac d… Seve… 37 1 " 0 …
7 ARM A AESOC Congenita… Mild 37 1 " 0 …
8 ARM A AESOC Congenita… Mode… 37 1 " 0 …
9 ARM A AESOC Congenita… Seve… 37 1 " 0 …
10 ARM A AESOC Ear and l… Mild 37 1 " 0 …
# ℹ 230 more rows
# ℹ Use `print(n = ...)` to see more rows
Pivot SOC frequencies
proc_transpose: input data set 240 rows and 7 columns
by: CAT
var: CNTPCT
id: BY CAT2
copy: VAR VARORD
name: NAME
output dataset 20 rows and 16 columns
# A tibble: 20 × 16
VAR CAT VARORD NAME `ARM A.Mild`
<chr> <chr> <dbl> <chr> <chr>
1 AESOC Blood and lymph… 1 CNTP… " 0 ( 0.0…
2 AESOC Cardiac disorde… 1 CNTP… " 0 ( 0.0…
3 AESOC Congenital, fam… 1 CNTP… " 0 ( 0.0…
4 AESOC Ear and labyrin… 1 CNTP… " 0 ( 0.0…
5 AESOC Endocrine disor… 1 CNTP… " 0 ( 0.0…
6 AESOC Gastrointestina… 1 CNTP… " 0 ( 0.0…
7 AESOC General disorde… 1 CNTP… " 2 ( 5.4…
8 AESOC Infections and … 1 CNTP… " 7 ( 18.9…
9 AESOC Injury, poisoni… 1 CNTP… " 0 ( 0.0…
10 AESOC Investigations 1 CNTP… " 4 ( 10.8…
11 AESOC Metabolism and … 1 CNTP… " 0 ( 0.0…
12 AESOC Musculoskeletal… 1 CNTP… " 3 ( 8.1…
13 AESOC Neoplasms benig… 1 CNTP… " 0 ( 0.0…
14 AESOC Nervous system … 1 CNTP… " 7 ( 18.9…
15 AESOC Psychiatric dis… 1 CNTP… " 0 ( 0.0…
16 AESOC Renal and urina… 1 CNTP… " 1 ( 2.7…
17 AESOC Respiratory, th… 1 CNTP… " 2 ( 5.4…
18 AESOC Skin and subcut… 1 CNTP… " 1 ( 2.7…
19 AESOC Surgical and me… 1 CNTP… " 0 ( 0.0…
20 AESOC Vascular disord… 1 CNTP… " 0 ( 0.0…
# ℹ 11 more variables: `ARM A.Moderate` <chr>,
# `ARM A.Severe` <chr>, `ARM B.Mild` <chr>,
# `ARM B.Moderate` <chr>, `ARM B.Severe` <chr>,
# `ARM C.Mild` <chr>, `ARM C.Moderate` <chr>,
# `ARM C.Severe` <chr>, `ARM D.Mild` <chr>,
# `ARM D.Moderate` <chr>, `ARM D.Severe` <chr>
Get PT Frequencies
proc_freq: input data set 145 rows and 6 columns
tables: AEDECOD * AESEV
by: ARM
view: TRUE
output: 1 datasets
# A tibble: 876 × 7
BY VAR1 VAR2 CAT1 CAT2 CNT PCT
<chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
1 ARM A AEDECOD AESEV ANXIETY MILD 0 0
2 ARM A AEDECOD AESEV ANXIETY MODE… 0 0
3 ARM A AEDECOD AESEV ANXIETY SEVE… 0 0
4 ARM A AEDECOD AESEV APPLICATI… MILD 0 0
5 ARM A AEDECOD AESEV APPLICATI… MODE… 0 0
6 ARM A AEDECOD AESEV APPLICATI… SEVE… 0 0
7 ARM A AEDECOD AESEV APPLICATI… MILD 0 0
8 ARM A AEDECOD AESEV APPLICATI… MODE… 0 0
9 ARM A AEDECOD AESEV APPLICATI… SEVE… 0 0
10 ARM A AEDECOD AESEV BACK PAIN MILD 2 5.41
# ℹ 866 more rows
# ℹ Use `print(n = ...)` to see more rows
Get unique SOC and PT combinations
proc_sort: input data set 73 rows and 6 columns
by: AESOC AEDECOD
keep: AESOC AEDECOD
order: a a
options: nodupkey
output data set 73 rows and 2 columns
# A tibble: 73 × 2
AESOC AEDECOD
<chr> <chr>
1 Blood and lymphatic system disorders NEUTRO…
2 Cardiac disorders PALPIT…
3 Cardiac disorders SINUS …
4 Congenital, familial and genetic disord… DERMOI…
5 Ear and labyrinth disorders VERTIGO
6 Endocrine disorders PARATH…
7 Gastrointestinal disorders DIARRH…
8 Gastrointestinal disorders FOOD P…
9 Gastrointestinal disorders TOOTHA…
10 Gastrointestinal disorders VOMITI…
# ℹ 63 more rows
# ℹ Use `print(n = ...)` to see more rows
Combine columns for PT
datastep: columns decreased from 7 to 6
# A tibble: 876 × 6
BY VAR CAT CAT2 VARORD CNTPCT
<chr> <chr> <chr> <chr> <dbl> <chr>
1 ARM A AEDECOD ANXIETY Mild 2 " 0 …
2 ARM A AEDECOD ANXIETY Mode… 2 " 0 …
3 ARM A AEDECOD ANXIETY Seve… 2 " 0 …
4 ARM A AEDECOD APPLICATION S… Mild 2 " 0 …
5 ARM A AEDECOD APPLICATION S… Mode… 2 " 0 …
6 ARM A AEDECOD APPLICATION S… Seve… 2 " 0 …
7 ARM A AEDECOD APPLICATION S… Mild 2 " 0 …
8 ARM A AEDECOD APPLICATION S… Mode… 2 " 0 …
9 ARM A AEDECOD APPLICATION S… Seve… 2 " 0 …
10 ARM A AEDECOD BACK PAIN Mild 2 " 2 …
# ℹ 866 more rows
# ℹ Use `print(n = ...)` to see more rows
Pivot PT frequencies
proc_transpose: input data set 876 rows and 6 columns
by: CAT
var: CNTPCT
id: BY CAT2
copy: VAR VARORD
name: NAME
output dataset 73 rows and 16 columns
# A tibble: 73 × 16
VAR CAT VARORD NAME `ARM A.Mild`
<chr> <chr> <dbl> <chr> <chr>
1 AEDECOD ANXIETY 2 CNTP… " 0 ( 0.0…
2 AEDECOD APPLICATION S… 2 CNTP… " 0 ( 0.0…
3 AEDECOD APPLICATION S… 2 CNTP… " 0 ( 0.0…
4 AEDECOD BACK PAIN 2 CNTP… " 2 ( 5.4…
5 AEDECOD BASAL CELL CA… 2 CNTP… " 0 ( 0.0…
6 AEDECOD BLOOD GLUCOSE… 2 CNTP… " 0 ( 0.0…
7 AEDECOD BLOOD PARATHY… 2 CNTP… " 0 ( 0.0…
8 AEDECOD BLOOD PARATHY… 2 CNTP… " 0 ( 0.0…
9 AEDECOD BLOOD PRESSUR… 2 CNTP… " 1 ( 2.7…
10 AEDECOD BLOOD TRIGLYC… 2 CNTP… " 0 ( 0.0…
# ℹ 63 more rows
# ℹ 11 more variables: `ARM A.Moderate` <chr>,
# `ARM A.Severe` <chr>, `ARM B.Mild` <chr>,
# `ARM B.Moderate` <chr>, `ARM B.Severe` <chr>,
# `ARM C.Mild` <chr>, `ARM C.Moderate` <chr>,
# `ARM C.Severe` <chr>, `ARM D.Mild` <chr>,
# `ARM D.Moderate` <chr>, `ARM D.Severe` <chr>
# ℹ Use `print(n = ...)` to see more rows
Join in SOC
datastep: columns increased from 16 to 17
# A tibble: 73 × 17
VAR CAT2 VARORD NAME `ARM A.Mild`
<chr> <chr> <dbl> <chr> <chr>
1 AEDECOD Anxiety 2 CNTP… " 0 ( 0.0…
2 AEDECOD Application S… 2 CNTP… " 0 ( 0.0…
3 AEDECOD Application S… 2 CNTP… " 0 ( 0.0…
4 AEDECOD Back Pain 2 CNTP… " 2 ( 5.4…
5 AEDECOD Basal Cell Ca… 2 CNTP… " 0 ( 0.0…
6 AEDECOD Blood Glucose… 2 CNTP… " 0 ( 0.0…
7 AEDECOD Blood Parathy… 2 CNTP… " 0 ( 0.0…
8 AEDECOD Blood Parathy… 2 CNTP… " 0 ( 0.0…
9 AEDECOD Blood Pressur… 2 CNTP… " 1 ( 2.7…
10 AEDECOD Blood Triglyc… 2 CNTP… " 0 ( 0.0…
# ℹ 63 more rows
# ℹ 12 more variables: `ARM A.Moderate` <chr>,
# `ARM A.Severe` <chr>, `ARM B.Mild` <chr>,
# `ARM B.Moderate` <chr>, `ARM B.Severe` <chr>,
# `ARM C.Mild` <chr>, `ARM C.Moderate` <chr>,
# `ARM C.Severe` <chr>, `ARM D.Mild` <chr>,
# `ARM D.Moderate` <chr>, …
# ℹ Use `print(n = ...)` to see more rows
Stack SOC and PT counts
datastep: columns started with 16 and ended with 16
# A tibble: 93 × 16
VAR CAT CAT2 VARORD `ARM A.Mild`
<chr> <chr> <chr> <dbl> <chr>
1 AESOC Blood and lymph… <NA> 1 " 0 ( 0.0…
2 AESOC Cardiac disorde… <NA> 1 " 0 ( 0.0…
3 AESOC Congenital, fam… <NA> 1 " 0 ( 0.0…
4 AESOC Ear and labyrin… <NA> 1 " 0 ( 0.0…
5 AESOC Endocrine disor… <NA> 1 " 0 ( 0.0…
6 AESOC Gastrointestina… <NA> 1 " 0 ( 0.0…
7 AESOC General disorde… <NA> 1 " 2 ( 5.4…
8 AESOC Infections and … <NA> 1 " 7 ( 18.9…
9 AESOC Injury, poisoni… <NA> 1 " 0 ( 0.0…
10 AESOC Investigations <NA> 1 " 4 ( 10.8…
# ℹ 83 more rows
# ℹ 11 more variables: `ARM A.Moderate` <chr>,
# `ARM A.Severe` <chr>, `ARM B.Mild` <chr>,
# `ARM B.Moderate` <chr>, `ARM B.Severe` <chr>,
# `ARM C.Mild` <chr>, `ARM C.Moderate` <chr>,
# `ARM C.Severe` <chr>, `ARM D.Mild` <chr>,
# `ARM D.Moderate` <chr>, `ARM D.Severe` <chr>
# ℹ Use `print(n = ...)` to see more rows
proc_sort: input data set 93 rows and 16 columns
by: CAT VARORD CAT2
keep: VAR CAT CAT2 VARORD ARM A.Mild ARM A.Moderate ARM A.Severe ARM B.Mild ARM B.Moderate ARM B.Severe ARM C.Mild ARM C.Moderate ARM C.Severe ARM D.Mild ARM D.Moderate ARM D.Severe
order: a a a
output data set 93 rows and 16 columns
# A tibble: 93 × 16
VAR CAT CAT2 VARORD `ARM A.Mild`
<chr> <chr> <chr> <dbl> <chr>
1 AESOC Blood and lym… <NA> 1 " 0 ( 0.0…
2 AEDECOD Blood and lym… Neut… 2 " 0 ( 0.0…
3 AESOC Cardiac disor… <NA> 1 " 0 ( 0.0…
4 AEDECOD Cardiac disor… Palp… 2 " 0 ( 0.0…
5 AEDECOD Cardiac disor… Sinu… 2 " 0 ( 0.0…
6 AESOC Congenital, f… <NA> 1 " 0 ( 0.0…
7 AEDECOD Congenital, f… Derm… 2 " 0 ( 0.0…
8 AESOC Ear and labyr… <NA> 1 " 0 ( 0.0…
9 AEDECOD Ear and labyr… Vert… 2 " 0 ( 0.0…
10 AESOC Endocrine dis… <NA> 1 " 0 ( 0.0…
# ℹ 83 more rows
# ℹ 11 more variables: `ARM A.Moderate` <chr>,
# `ARM A.Severe` <chr>, `ARM B.Mild` <chr>,
# `ARM B.Moderate` <chr>, `ARM B.Severe` <chr>,
# `ARM C.Mild` <chr>, `ARM C.Moderate` <chr>,
# `ARM C.Severe` <chr>, `ARM D.Mild` <chr>,
# `ARM D.Moderate` <chr>, `ARM D.Severe` <chr>
# ℹ Use `print(n = ...)` to see more rows
Get frequencies for all events
proc_freq: input data set 145 rows and 6 columns
tables: AESEV
by: ARM
view: TRUE
output: 1 datasets
# A tibble: 12 × 6
BY VAR CAT N CNT PCT
<chr> <chr> <chr> <dbl> <dbl> <dbl>
1 ARM A AESEV MILD 37 27 73.0
2 ARM A AESEV MODERATE 37 10 27.0
3 ARM A AESEV SEVERE 37 0 0
4 ARM B AESEV MILD 32 24 75
5 ARM B AESEV MODERATE 32 6 18.8
6 ARM B AESEV SEVERE 32 2 6.25
7 ARM C AESEV MILD 36 29 80.6
8 ARM C AESEV MODERATE 36 7 19.4
9 ARM C AESEV SEVERE 36 0 0
10 ARM D AESEV MILD 40 31 77.5
11 ARM D AESEV MODERATE 40 9 22.5
12 ARM D AESEV SEVERE 40 0 0
Combine all events.
datastep: columns decreased from 6 to 4
# A tibble: 12 × 4
BY VAR CAT CNTPCT
<chr> <chr> <chr> <chr>
1 ARM A AESEV Mild " 27 ( 73.0)"
2 ARM A AESEV Moderate " 10 ( 27.0)"
3 ARM A AESEV Severe " 0 ( 0.0)"
4 ARM B AESEV Mild " 24 ( 75.0)"
5 ARM B AESEV Moderate " 6 ( 18.8)"
6 ARM B AESEV Severe " 2 ( 6.2)"
7 ARM C AESEV Mild " 29 ( 80.6)"
8 ARM C AESEV Moderate " 7 ( 19.4)"
9 ARM C AESEV Severe " 0 ( 0.0)"
10 ARM D AESEV Mild " 31 ( 77.5)"
11 ARM D AESEV Moderate " 9 ( 22.5)"
12 ARM D AESEV Severe " 0 ( 0.0)"
Prepare data for reporting
proc_transpose: input data set 12 rows and 4 columns
var: CNTPCT
id: BY CAT
copy: VAR
name: CAT
output dataset 1 rows and 14 columns
# A tibble: 1 × 14
VAR CAT `ARM A.Mild` `ARM A.Moderate`
<chr> <chr> <chr> <chr>
1 AESEV CNTPCT " 27 ( 73.0)" " 10 ( 27.0)"
# ℹ 10 more variables: `ARM A.Severe` <chr>,
# `ARM B.Mild` <chr>, `ARM B.Moderate` <chr>,
# `ARM B.Severe` <chr>, `ARM C.Mild` <chr>,
# `ARM C.Moderate` <chr>, `ARM C.Severe` <chr>,
# `ARM D.Mild` <chr>, `ARM D.Moderate` <chr>,
# `ARM D.Severe` <chr>
=========================================================================
Create final data frame
=========================================================================
datastep: columns increased from 14 to 16
# A tibble: 94 × 16
VAR CAT CAT2 VARORD `ARM A.Mild`
<chr> <chr> <chr> <dbl> <chr>
1 AESEV All Adverse E… <NA> NA " 27 ( 73.0…
2 AESOC Blood and lym… <NA> 1 " 0 ( 0.0…
3 AEDECOD Blood and lym… Neut… 2 " 0 ( 0.0…
4 AESOC Cardiac disor… <NA> 1 " 0 ( 0.0…
5 AEDECOD Cardiac disor… Palp… 2 " 0 ( 0.0…
6 AEDECOD Cardiac disor… Sinu… 2 " 0 ( 0.0…
7 AESOC Congenital, f… <NA> 1 " 0 ( 0.0…
8 AEDECOD Congenital, f… Derm… 2 " 0 ( 0.0…
9 AESOC Ear and labyr… <NA> 1 " 0 ( 0.0…
10 AEDECOD Ear and labyr… Vert… 2 " 0 ( 0.0…
# ℹ 84 more rows
# ℹ 11 more variables: `ARM A.Moderate` <chr>,
# `ARM A.Severe` <chr>, `ARM B.Mild` <chr>,
# `ARM B.Moderate` <chr>, `ARM B.Severe` <chr>,
# `ARM C.Mild` <chr>, `ARM C.Moderate` <chr>,
# `ARM C.Severe` <chr>, `ARM D.Mild` <chr>,
# `ARM D.Moderate` <chr>, `ARM D.Severe` <chr>
# ℹ Use `print(n = ...)` to see more rows
=========================================================================
Create and print report
=========================================================================
Create table object
Create report object
Print report
# A report specification: 16 pages
- file_path: 'C:\Users\dbosa\AppData\Local\Temp\RtmpEBPgPu/example4.rtf'
- output_type: RTF
- units: inches
- orientation: landscape
- margins: top 0.5 bottom 0.5 left 1 right 1
- line size/count: 9/42
- page_header: left=Sponsor: Company right=Study: ABC
- title 1: 'Table 5.0'
- title 2: 'Adverse Events by Maximum Severity'
- footnote 1: 'Program: AE_Table.R'
- footnote 2: 'Note: Adverse events were coded using MedDRA Version 9.1'
- page_footer: left=2023-09-05 22:26:19.941609 center=Confidential right=Page [pg] of [tpg]
- content:
# A table specification:
- data: tibble 'allfinal' 94 rows 16 cols
- show_cols: all
- use_attributes: all
- width: 9
- spanning_header: from='ARM A.Mild' to='ARM A.Severe' 'ARM A' level=1
- spanning_header: from='ARM B.Mild' to='ARM B.Severe' 'ARM B' level=1
- spanning_header: from='ARM C.Mild' to='ARM C.Severe' 'ARM C' level=1
- spanning_header: from='ARM D.Mild' to='ARM D.Severe' 'ARM D' level=1
- stub: CAT CAT2 'System Organ Class
Preferred Term' width=5 align='left'
- define: CAT
- define: CAT2
- define: ARM A.Mild 'Mild'
- define: ARM A.Moderate 'Moderate'
- define: ARM A.Severe 'Severe'
- define: ARM B.Mild 'Mild' page_wrap='TRUE'
- define: ARM B.Moderate 'Moderate'
- define: ARM B.Severe 'Severe'
- define: ARM C.Mild 'Mild' page_wrap='TRUE'
- define: ARM C.Moderate 'Moderate'
- define: ARM C.Severe 'Severe'
- define: ARM D.Mild 'Mild' page_wrap='TRUE'
- define: ARM D.Moderate 'Moderate'
- define: ARM D.Severe 'Severe'
- define: VAR visible='FALSE'
- define: VARORD visible='FALSE'
=========================================================================
Clean Up
=========================================================================
Remove library from workspace
lib_sync: synchronized data in library 'sdtm'
Close log
=========================================================================
Log End Time: 2023-09-05 22:26:20.855023
Log Elapsed Time: 0 00:00:04
=========================================================================