In this QCast episode, co-hosts Jullia and Tom explore how the R programming language is applied to clinical trial data analysis. They walk through its role in study planning, data cleaning, safety and efficacy analysis, and the production of tables, listings, and figures. The discussion highlights how to validate processes in line with regulatory expectations, manage version control for reproducibility, and integrate the language alongside established tools. They also share common pitfalls to avoid, practical workflows for reporting, and quick wins that help teams adopt open-source methods with confidence.
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Key Takeaways
Where the Language Fits in the Trial Lifecycle
The statistical programming language supports simulation and power calculations at design stage, scripted data cleaning during conduct, flexible modelling for efficacy and safety analysis, and reproducible production of tables, listings, and figures at reporting.
Building Inspection-Ready Outputs
Create a reporting dataset that mirrors the table shell with row order, labels, indentation, and page markers. From there, formatted tables and figures can be produced with consistent headers, pagination, and styling using reproducible code.
Validation and Compliance Expectations
Regulators emphasise data integrity, reproducibility, and transparency rather than specific software. Compliance comes from controlling versions, documenting scripts, applying peer review or dual programming, and validating templates that generate final documents.
Introducing the Language Alongside SAS
Start with a focused pilot set of outputs such as demographics, adverse events, and time-to-event figures. Verify results against current processes, resolve differences in defaults, and establish style guides and templates before expanding adoption.
Quick Tips and Common Pitfalls
Pin package versions and capture session info to ensure reproducibility. Automate title blocks, footnotes, and formatting so analysts focus on analysis. Avoid leaving layout to the last step, mixing package versions mid-study, or skipping independent review of “simple” scripts.
Full Transcript
Jullia
Welcome to QCast, the show where biometric expertise meets data-driven dialogue. I’m Jullia.
Tom
I’m Tom, and in each episode, we dive into the methodologies, case studies, regulatory shifts, and industry trends shaping modern drug development.
Jullia
Whether you’re in biotech, pharma or life sciences, we’re here to bring you practical insights straight from a leading biometrics CRO. Let’s get started.
Tom
Today we are focusing on using R programming for clinical trial data analysis. Many teams are evaluating it alongside their established tools. Jullia, can you start by giving us a clear overview of what R is bringing to clinical work that is worth attention right now?
Jullia
Thanks, Tom. So, R is a statistical programming language with a mature ecosystem for data handling, modelling, and graphics. It is open source, widely taught, and well documented. In clinical trials it supports design activities, data cleaning, efficacy and safety analysis, and production of tables, listings and figures, often shortened to TLFs. Adoption depends on process, not brand. Regulators look for data integrity, reproducibility, and validation. R can satisfy those expectations when teams control versions, document methods, and test outputs. The draw is practical: one environment can wrangle data, run models such as Kaplan–Meier and Cox, and produce publication-ready visuals and formatted documents. That reduces hand-offs, speeds iteration, and keeps the workflow transparent for review.
Tom
Let’s map the lifecycle. Where does it fit from planning to reporting?
Jullia
In planning, R is effective for simulation studies and power calculations. Teams can stress-test sample size assumptions and event accrual scenarios before first patient in. During conduct, R helps standardise data cleaning by scripting checks, merges, and derivations so that identical logic runs each time new data arrives. For analysis, it covers descriptive summaries, time-to-event methods, generalised linear models, mixed-effects models for longitudinal data, and estimation workflows with clear contrasts. For safety, it supports adverse event summaries, exposure-adjusted incidence calculations, and clear stratification by treatment, severity, and causality. For reporting, it can construct TLFs and export to Word or PDF using reproducible code. The same scripts can be versioned and re-run at interim and final analysis without manual rework, which supports consistency.
Tom
Teams often ask about TLFs in Word. What is the practical route in R to create inspection-ready tables with the correct layout?
Jullia
The reliable approach is to shape a “reporting dataset” that mirrors the final table. That dataset includes row order, labels, indentation levels, grouping identifiers, and any planned blank lines. From there, you convert that dataset into a formatted table object using packages like flex table, then write to a Word document with officer. You set widths, alignments, and header structures in code. If a table spans multiple pages, add a page variable to the dataset and print per page. This gives you pagination control and repeatable headers. The core idea is simple: do structural work in data, do style work in a small number of functions, and keep both under version control.
Tom
When people weigh this open-source language against established clinical software, what are the main trade-offs they should consider?
Jullia
Consider eight points. First, capability: both can produce compliant TLFs; R excels in flexible modelling and graphics. Second, setup: R requires a defined stack and pinned versions; legacy tools often come with fixed procedures. Third, learning curve: R rewards programming fluency; teams need training in tidy data and idiomatic workflows. Fourth, automation: R is strong for reusable functions and templates. Fifth, integration: R links well to version control, markdown reporting, and reproducible environments; legacy tools may integrate smoothly with existing clinical platforms. Sixth, validation: both require documented testing; with R you validate your process and packages at project level. Seventh, support: community and internal standards in R versus vendor support in commercial tools. Eighth, cost: R has no licence fees; cost sits in training, process design, and maintenance.
Tom
Validation is central. How do you implement a validation approach for R that stands up to inspection without creating unnecessary overhead?
Jullia
Start with an SOP that defines how R is used. Pin R and package versions per study and store them in a controlled environment. Record session info for each deliverable so you can reproduce the exact runtime. For each script, document purpose, inputs, outputs, and acceptance criteria. Apply independent code review or dual programming where risk is higher. Test critical functions with known inputs and expected outputs and keep evidence. For reporting, validate templates that generate Word or PDF so headers, footers, fonts, and pagination behave as specified. Control access through role-based permissions and keep an audit trail in your repository. None of this is unique to R; it is a standard computerised system validation approach applied to an open-source stack.
Tom
Skills and staffing are practical constraints. For a team fluent in SAS, how would you plan a measured introduction of R without disrupting delivery?
Jullia
Begin with a small, representative pilot. Select a limited set of TLFs that reflect your common patterns: a demographics table, an adverse event summary, and a time-to-event figure. Replicate those in R, verify outputs against your current process, and document any differences in defaults or rounding. Establish a shared R style guide, directory structure, and naming conventions. Train the team on tidy data manipulation, model fitting, and the reporting tool chain. Build one reusable function that converts a prepared dataset into a house-styled flextable. Once the pilot meets quality and time targets, extend to similar shells. Keep the rest of the portfolio on the current stack until confidence is stable.
Tom
Can you outline a clear workflow for creating one analysis table in R from the analysis dataset to the final Word file?
Jullia
Prepare the analysis dataset according to your specification, for example an ADaM-like structure with the necessary variables and flags. Derive the reporting dataset by calculating statistics or estimates per row, adding label columns, sort keys, indent levels, and page markers if needed. Convert this dataset to a flextable, set column widths and alignment, and add title, subtitle, and footnotes. Use officer to open a reference Word document that holds your approved styles and write the table. If the table is multi-page, print per page key and repeat headers. Save the Word file with a controlled name, write out the underlying reporting dataset as a CSV for traceability, and capture session information. Commit code and outputs to version control with a clear message.
Tom
Graphics can also be a sticking point. What is the recommended path in R to produce clear clinical figures with consistent styling?
Jullia
Use grammar-of-graphics tools to define defaults once and reuse them. Create a small theme function that sets base font sizes, grid lines, and legend placement to match your house style. For Kaplan–Meier curves, generate survival estimates with the survival package, convert them into a tidy format, and plot with a consistent scale and censoring marks. For forest plots, assemble estimates and confidence intervals into a tidy dataset, then layer points and segments with ordered facets. Export figures at fixed dimensions and resolution appropriate for the destination document. Keep scales, labels, and caption rules standard across studies so reviewers can read figures quickly without relearning the style each time.
Tom
Let’s address data standards and structures. How does it interact with CDISC standards in everyday analysis work?
Jullia
It works well with standardised structures. Many teams create analysis datasets that align with CDISC ADaM. Once variables are standardised and derivations are documented, R scripts can be largely study-agnostic. Functions operate on known variable names and produce consistent outputs. For SDTM, R can support conformance checks and mappings where needed, although creation of SDTM is often done earlier in the data flow. The key is to keep derivations transparent, name variables consistently, and store specifications with the code so the lineage from raw to analysis is traceable.
Tom
People worry about change management. Package updates can shift results if defaults change. How do you maintain stability over a long study?
Jullia
Lock versions at study level and avoid mid-study upgrades unless you have a clear reason and a re-validation plan. Use a dependency file that records exact versions of the open-source language and packages. Build your analysis environment from that file on analyst machines and in your build server so they match. If a security or defect fix requires an update, run a controlled comparison on a defined set of outputs, document any differences, and obtain approval before deploying. Consistency is the objective; version control plus pinned dependencies delivers that.
Tom
What about common pitfalls that slow teams down?
Jullia
Three appear often. First, leaving layout to the printing step. Fix structure in the dataset and printing becomes simple. Second, mixing package versions within a project, which undermines reproducibility. Lock versions from the start. Third, inconsistent naming and file organisation. Adopt a standard project template with clear folders for data, scripts, outputs, and logs. A fourth worth mentioning is skipping independent review of “simple” scripts; minor changes can still affect numbers. A light but consistent review process prevents rework later.
Tom
Finally, could you give a focused set of takeaways for teams starting with R in clinical reporting?
Jullia
Start with a small pilot and choose outputs that recur across studies. Treat the reporting dataset as the source of truth for layout and content. Pin versions of R and key packages and store session information with outputs. Build one reusable table function and one figure theme to enforce style. Document defaults and rounding rules to avoid drift across tools. Compare pilot outputs to your current process and resolve differences early. Automate title blocks, footnotes, and pagination so analysts focus on analysis, not formatting. Keep code under review and maintain a clean repository structure.
With that, we’ve come to the end of today’s episode on Using R Programming for Clinical Trial Data Analysis. If you found this discussion useful, don’t forget to subscribe to QCast so you never miss an episode and share it with a colleague. And if you’d like to learn more about how Quanticate supports data-driven solutions in clinical trials, head to our website or get in touch.
Tom
Thanks for tuning in, and we’ll see you in the next episode.
About QCast
QCast by Quanticate is the podcast for biotech, pharma, and life science leaders looking to deepen their understanding of biometrics and modern drug development. Join co-hosts Tom and Jullia as they explore methodologies, case studies, regulatory shifts, and industry trends shaping the future of clinical research. Where biometric expertise meets data-driven dialogue, QCast delivers practical insights and thought leadership to inform your next breakthrough.
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