Clinical Trial Data-Cut Change Detection and Impact Assessment

A transparent, rule-based framework for detecting changes between successive data cuts of clinical trial datasets, classifying those changes into clinically meaningful categories, tracing user-defined data lineage, and assessing which downstream analyses and outputs may be affected. The package is designed to complement existing low-level data frame comparison tools by adding clinical-trial-specific classification, lineage and impact-assessment layers on top of deterministic comparison. The rule-based classification is similar in spirit to the data validation infrastructure of van der Loo and de Jonge (2021) .

trialdiff

trialdiff is a clinical-trial data-cut change detection and downstream impact assessment framework for R, designed for the pharmaverse ecosystem.

Existing tools tell you that two datasets differ. trialdiff answers the clinical-programming question that follows:

What changed, what does the change represent, and which downstream analyses or outputs might be affected?

It does this in five transparent, composable layers:

Everything is deterministic. There is no machine learning, and no statistical impact is ever claimed - analyses are flagged for review and rerun.

Installation

# From r-universe (includes Windows/macOS binaries)
install.packages(
  "trialdiff",
  repos = c(
    hirujan = "https://hirujan-r.r-universe.dev",
    CRAN = "https://cloud.r-project.org"
  )
)

# Or from GitHub
# install.packages("remotes")
remotes::install_github("Hirujan-R/trialdiff")

Quick start

library(trialdiff)

diff <- compare_cut(
  old = adsl_cut1,
  new = adsl_cut2,
  by = "USUBJID",
  dataset = "ADSL"
)

classified <- classify_changes(diff)

impact <- assess_impact(classified, adsl_adlb_lineage)

report_diff(diff, impact = impact, output = "report.html")

A treatment assignment change (TRT01P = "Placebo" -> "Drug A") is detected, classified as a treatment-assignment change, traced through ADSL.TRT01P -> ADLB.TRT01P -> lab summary -> MMRM -> efficacy table, and every downstream object is flagged for review with a rationale.

Why not just diffdf?

diffdf (and waldo) are excellent low-level comparison tools, and trialdiff deliberately does not reinvent them. trialdiff adds the layers that are specific to clinical programming:

• a clinical change taxonomy (new subject, new visit, corrected value, missingness transitions, derived-variable change, treatment-assignment change, metadata change, ...);
• explicit lineage between datasets, variables, analyses and outputs;
• auditable impact assessment with a documented policy;
• review reports that name the objects a programmer or statistician must check.

It can even delegate the low-level comparison itself: compare_cut(..., backend = "diffdf") uses diffdf to detect differences and translates the result into the same tdiff object.

Design principles

• Deterministic comparison, not opaque inference.
• Transparent, user-extensible rules.
• Explicit, reproducible lineage metadata.
• Auditable impact assessment with a written rationale for every flag.
• Clinically meaningful reporting.

Documentation

• vignette("trialdiff") - getting started.
• vignette("change-classification") - the rule system.
• vignette("lineage-and-impact") - lineage and impact.
• vignette("ecosystem") - relationship to diffdf, admiral, metacore, cards, tern, SAS PROC COMPARE and others.
• vignette("case-study") - end-to-end walkthrough on public pharmaverseadam data.

Project proposal

A full proposal covering the problem statement, ecosystem/gap analysis, novelty assessment, architecture, testing and roadmap is available in the project proposal.

Code of conduct

Please note that the trialdiff project is released with a Contributor Code of Conduct. By contributing you agree to abide by its terms.

License

MIT (c) Hirujan Rangaraj. No proprietary or real patient data is included; all example datasets are synthetic.