Current challenges

Clinical trials face several challenges in their current state, which can be broadly categorized into three key areas:

Pharmaceutical Standards

The Clinical Data Interchange Standards Consortium (CDISC) provides globally accepted data standards, which are a set of guidelines and models used to standardize the collection, exchange, and analysis of clinical trial data. These standards aim to improve efficiency, data quality, and interoperability across various clinical research areas.

In this article specifically, the key CDISC Standards we are going to highlight are Clinical Data Acquisition Standards Harmonization (CDASH), Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM).

The Role of R in Clinical Trials

Nowadays, R is widely used in clinical trial analysis due to its flexibility, powerful statistical capabilities, and strong support for regulatory compliance. R provides:

• Robust Statistical Analysis used for survival analysis, mixed models, and Bayesian statistics.
• Data Cleaning & Transformation facilitated by specialized packages and modeling tools.
• Reproducibility & Open Source Solutions to ensure that clinical reports and submissions can be automated, reducing errors.
• Integration with CDISC Standards with the help of various R packages that can structure data according to SDTM and ADaM requirements.

Pharmaverse

Pharmaverse is a collaborative open-source ecosystem of tools for clinical trial data processing and regulatory compliance, currently consisting mainly of R packages but now also including emerging Python software. It provides resources that streamline the implementation of CDISC standards such as SDTM, ADaM, and Define-XML.

• Key Pharmaverse Packages:
  • {sdtm.oak} – Helps in structuring SDTM datasets.
  • {admiral} – Facilitates ADaM dataset creation.
  • {teal} – Builds interactive R/Shiny dashboards for exploratory clinical data analysis.
• Related R Packages:
  • {rtables} – Creates regulatory-compliant tables for clinical reports.
  • {tern} – Creates regulatory-compliant tables/graphs for clinical reports.

These are not the only packages that are used, but these are the ones that will be referenced within this blogpost.

Pharmaverse doesn’t have a dedicated CDASH package, but its metadata-driven approach means you can still work with CDASH concepts to connect source data collection to SDTM mapping.

A more comprehensive list of packages can be found here: https://pharmaverse.org/e2eclinical/

Data Pipeline

In Clinical Trial Reporting, the data pipeline could follow this standard:

The raw data from patients is collected according to CDASH guidelines, then processed with SDTM and ADaM standards to produce related Tables, Listings, and Graphs/Figures outputs that correspond to all aspects of a specific clinical trial.

Starting with the CDASH data from a study, the preparation process starts with {sdtm.oak} and {sdtmchecks} packages from the Pharmaverse in order to get them ready for SDTM formatted datasets.

Subsequently, ADaM standards are applied on this data using the {admiral} package, which can then be analysed and transformed into Tables, Listings, and Graphs/Figures using non-pharmaverse (but still related) packages such as {rtables} and {tern}.

The Pharmaverse contains some repositories with test data that can help visualize and make sense of this, namely pharmaversesdtm and pharmaverseadam.

There is also the {random.cdisc.data} package that can be used to generate randomized CDISC data for exploratory purposes.

More information about these datasets can be found in their respective documentation sites.

Tables, Listings, Graphs/Figures

The content of clinical trial reports contains 3 different types of outputs:

1. Tables
2. Listings
3. Graphs/Figures

These outputs play a vital part to summarize the results of a clinical trial during reporting. They can summarize safety, efficacy, and exploratory outcomes (such as biomarker analyses).

In the next sections, we will be using the datasets in the {random.cdisc.data} package to help create example outputs used in clinical trials reports. These will be split into two sections each, namely “Data Setup” and “Standard Output Generation”.

library(random.cdisc.data)
library(dplyr)
library(tern)

# Ensure character variables are converted to factors so that
# empty strings and NAs are explicit missing levels.

adsl <- df_explicit_na(cadsl)
adae <- df_explicit_na(cadae) %>%
  var_relabel(
    AEBODSYS = "MedDRA System Organ Class",
    AEDECOD = "MedDRA Preferred Term"
  ) %>%
  filter(ANL01FL == "Y")

# Define the split function
split_fun <- drop_split_levels

# Standard Table for AET02 --------------------------------------------------------------
lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  add_overall_col(label = "All Patients") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one adverse event",
      nonunique = "Overall total number of events"
    )
  ) %>%
  split_rows_by(
    "AEBODSYS",
    child_labels = "visible",
    nested = FALSE,
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adae$AEBODSYS)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one adverse event",
      nonunique = "Total number of events"
    )
  ) %>%
  count_occurrences(
    vars = "AEDECOD",
    .indent_mods = -1L
  ) %>%
  append_varlabels(adae, "AEDECOD", indent = 1L)

result <- build_table(lyt, df = adae, alt_counts_df = adsl)

result

library(random.cdisc.data)
library(dplyr)
library(rlistings)

out <- cadae %>%
  select(AESOC, AEDECOD, AELLT, AETERM) %>%
  unique()

var_labels(out) <- c(
  AESOC = "MedDRA System Organ Class",
  AEDECOD = "MedDRA Preferred Term",
  AELLT = "MedDRA Lowest Level Term",
  AETERM = "Investigator-Specified\nAdverse Event Term"
)

# Standard Listing for AEL01 ------------------------------------------------------------
lsting <- as_listing(
  out,
  key_cols = c("AESOC", "AEDECOD", "AELLT"),
  disp_cols = names(out),
  main_title = "Listing of Preferred Terms, Lowest Level Terms, and Investigator-Specified Adverse Event Terms"
)

head(lsting, 20)

library(random.cdisc.data)
library(tern)
library(dplyr)
library(forcats)
library(nestcolor)

preprocess_adtte <- function(adtte) {
  # Save variable labels before data processing steps.
  adtte_labels <- var_labels(adtte)

  adtte <- adtte %>%
    df_explicit_na() %>%
    dplyr::filter(
      PARAMCD == "OS",
      ARM %in% c("B: Placebo", "A: Drug X"),
      SEX %in% c("M", "F")
    ) %>%
    dplyr::mutate(
      # Reorder levels of ARM to display reference arm before treatment arm.
      ARM = droplevels(forcats::fct_relevel(ARM, "B: Placebo")),
      SEX = droplevels(SEX),
      is_event = CNSR == 0,
      # Convert time to MONTH
      AVAL = day2month(AVAL),
      AVALU = "Months"
    ) %>%
    var_relabel(
      ARM = adtte_labels["ARM"],
      SEX = adtte_labels["SEX"],
      is_event = "Event Flag",
      AVAL = adtte_labels["AVAL"],
      AVALU = adtte_labels["AVALU"]
    )

  adtte
}

anl <- cadtte %>%
  preprocess_adtte()

# Standard Plot for FSTG02 -------------------------------------------------------
anl1 <- anl

df <- extract_survival_subgroups(
  variables = list(tte = "AVAL", is_event = "is_event", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
  data = anl1
)

result <- basic_table() %>%
  tabulate_survival_subgroups(
    df = df,
    vars = c("n_tot", "n", "median", "hr", "ci"),
    time_unit = anl1$AVALU[1]
  )

result

plot <- g_forest(tbl = result)

plot

Conclusion

Conclusion

Clinical trial reporting remains fundamental to public trust and to the integrity of drug development. Nevertheless, the field continues to face persistent transparency deficits, escalating costs, and reproducibility challenges.

Adopting open standards and collaborative platforms—such as the Pharmaverse ecosystem—together with broader open-source initiatives in R and related technologies, offers a practical path forward. These approaches enable greater transparency, improved efficiency, and rigorous, standards-based data practices that strengthen the scientific foundation of clinical research. Consequently, the level of trust can be further enhanced, and public welfare can be assured.

Join us in part 2 of this series where we take a look at interactive clinical trials data analysis using packages that create custom shiny applications specifically for this space.

Resources

• Pharmaverse
• CDISC
• TLG Catalog