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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:

Layer	Function	Purpose
Compare	`compare_cut()`	Added/removed/modified observations and schema changes
Classify	`classify_changes()`	Rule-based clinical change taxonomy
Trace	`define_lineage()`, `trace_dependencies()`, `lineage_from_metadata()`, `output_registry()`	Explicit data lineage graph
Assess	`assess_impact()`	Definitely / potentially / unlikely impact
Report	`report_diff()`	HTML, Quarto and machine-readable JSON

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.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.