Module 7 — Final Project: Solution

library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(readr)
library(tidyr)
library(lubridate)

Attaching package: 'lubridate'

The following objects are masked from 'package:base':

    date, intersect, setdiff, union

library(ggplot2)
library(gtsummary)

#StandWithUkraine

library(flextable)

Attaching package: 'flextable'

The following objects are masked from 'package:gtsummary':

    as_flextable, continuous_summary

library(tibble)

✅ Final Project: From SDTM to TLFs (Solution)

This walkthrough shows a full mock clinical reporting workflow using R.

---

🧪 Step 1: Load Raw Data

# Generate mock DM data
dm <- tibble::tibble(
  USUBJID = paste0("01-701-", 1001:1005),
  DOB = as.character(seq(as.Date("1970-01-01"), by = "10 years", length.out = 5)),
  RFSTDTC = rep("2023-01-01", 5),
  ARM = c("Placebo", "Drug A", "Drug B", "Placebo", "Drug A"),
  SEX = c("F", "M", "F", "M", "F")
)

# Generate mock AE data
ae <- tibble::tibble(
  USUBJID = rep(dm$USUBJID, each = 2),
  AETERM = rep(c("Headache", "Nausea"), times = 5),
  AESEV = rep(c("MILD", "SEVERE"), times = 5),
  AESTDTC = rep("2023-01-10", 10),
  AEENDTC = rep("2023-01-12", 10),
  RFSTDTC = rep("2023-01-01", 10)
)

# Generate mock EX data
ex <- tibble::tibble(
  USUBJID = dm$USUBJID,
  EXTRT = dm$ARM,
  EXSTDTC = rep("2023-01-01", 5),
  EXENDTC = rep("2023-01-14", 5),
  EXDOSE = c("0", "50", "75", "0", "50")
)

---

library(dplyr)
library(readr)
library(tidyr)
library(lubridate)
library(ggplot2)
library(gtsummary)
library(flextable)
library(tibble)

🧱 Step 2: SDTM Domains

DM

dm_sdtm <- dm %>%
  mutate(
    BRTHDTC = ymd(DOB),
    AGE = floor((ymd(RFSTDTC) - BRTHDTC) / 365.25),
    ARMCD = substr(ARM, 1, 3),
    DOMAIN = "DM"
  )

AE

ae_sdtm <- ae %>%
  mutate(
    AESTDTC = ymd(AESTDTC),
    AEENDTC = ymd(AEENDTC),
    AESTDY = as.integer(AESTDTC - ymd(RFSTDTC)),
    AEENDY = as.integer(AEENDTC - ymd(RFSTDTC)),
    DOMAIN = "AE",
    AESEQ = row_number()
  )

EX

ex_sdtm <- ex %>%
  mutate(
    EXSTDTC = ymd(EXSTDTC),
    EXENDTC = ymd(EXENDTC),
    EXDOSE = as.numeric(EXDOSE),
    DOMAIN = "EX"
  )

---

📊 Step 3: ADaM Datasets

ADSL

adsl <- dm_sdtm %>%
  select(USUBJID, AGE, SEX, ARM, ARMCD) %>%
  mutate(TRT01P = ARM)

ADAE

adae <- ae_sdtm %>%
  left_join(adsl, by = "USUBJID") %>%
  mutate(
    ASEVFL = ifelse(AESEV == "SEVERE", "Y", "N"),
    AVALC = AESEV
  )

---

library(dplyr)
library(readr)
library(tidyr)
library(lubridate)
library(ggplot2)
library(gtsummary)
library(flextable)
library(tibble)

📋 Step 4: TLFs

1. AE Listing

ae_listing <- adae %>%
  select(USUBJID, AETERM, AESEV, AESTDTC, AEENDTC) %>%
  flextable() %>%
  autofit()
ae_listing

USUBJID	AETERM	AESEV	AESTDTC	AEENDTC
01-701-1001	Headache	MILD	2023-01-10	2023-01-12
01-701-1001	Nausea	SEVERE	2023-01-10	2023-01-12
01-701-1002	Headache	MILD	2023-01-10	2023-01-12
01-701-1002	Nausea	SEVERE	2023-01-10	2023-01-12
01-701-1003	Headache	MILD	2023-01-10	2023-01-12
01-701-1003	Nausea	SEVERE	2023-01-10	2023-01-12
01-701-1004	Headache	MILD	2023-01-10	2023-01-12
01-701-1004	Nausea	SEVERE	2023-01-10	2023-01-12
01-701-1005	Headache	MILD	2023-01-10	2023-01-12
01-701-1005	Nausea	SEVERE	2023-01-10	2023-01-12

2. Demographic Summary Table

adsl %>%
  select(AGE, SEX, ARM) %>%
  tbl_summary(by = ARM, missing = "no")

Characteristic	Drug A, N = 21	Drug B, N = 11	Placebo, N = 21
AGE
12	1 (50%)	0 (0%)	0 (0%)
23	0 (0%)	0 (0%)	1 (50%)
32	0 (0%)	1 (100%)	0 (0%)
43	1 (50%)	0 (0%)	0 (0%)
52	0 (0%)	0 (0%)	1 (50%)
SEX
F	1 (50%)	1 (100%)	1 (50%)
M	1 (50%)	0 (0%)	1 (50%)
1 n (%)

3. AE Severity Bar Plot

ae_fig <- adae %>%
  count(ARM, AESEV) %>%
  ggplot(aes(x = AESEV, y = n, fill = ARM)) +
  geom_col(position = "dodge") +
  labs(title = "Adverse Events by Severity", y = "Count") +
  theme_minimal()
ae_fig

---

🤖 BONUS: Custom Export Function (Copilot Suggestion)

export_xpt <- function(df, name) {
  path <- paste0("output/", name, ".xpt")
  haven::write_xpt(df, path)
}

You can use:

export_xpt(adsl, "adsl")
export_xpt(adae, "adae")