Services

R-first clinical analysis infrastructure, built around real study delivery.

Snowbird Analytics helps lean clinical teams build the computational backbone for ADaM datasets, TLGs, QC, documentation, and AI-assisted automation.

Service modules

Engage for a focused buildout, full study delivery, or a hybrid path where Snowbird designs the environment while supporting active clinical outputs.

CLAVIS

CLAVIS TLG Building

Point-and-click clinical tables, listings, and figures with every statistic computed in open-source R, producing submission-style RTF, a QC XPT dataset, and the full R program.

Visual builderTablesListingsGraphs
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CSP

CSP Agent Harness

Compliance-aware harness for clinical programming agents: role policy, work orders, source evidence, execution controls, validation artifacts, mismatch ledgers, and audit dashboards.

Agent harnessADaM QCTLG workflowAudit trail
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SCE

Lightweight SCE Setup

Low-cost small-team environment using JupyterHub or Posit Workbench, Git version control, R-first programming, optional SAS, and AI-assisted programming access.

JupyterHubPositGitAI access
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SCE

Statistical Computational Environment Design

Architecture for a complete analysis environment: project structure, R package strategy, metadata standards, Git workflow, validation checkpoints, execution logs, and reusable templates.

R-firstGitQuartoSOP-ready
R

R Clinical Programming Workflows

R-based derivation and reporting pipelines for analysis datasets and outputs, using modern open-source practices suitable for startups and small CROs managing cost.

tidyversepharmaverseADaMTLGs
AI

AI Agent Workflow Automation

Agent workflows for code drafting, spec interpretation, output consistency review, issue summarization, and documentation support, with human review gates and traceable evidence.

AgentsPrompt opsReview gatesTraceability
TLG

Clinical Study Data Analysis Delivery

Hands-on support for analysis datasets, tables, listings, graphs, medical review outputs, ad-hoc requests, and integrated analysis packages where needed.

ADaMTLGCSRAd-hoc
QC

Independent Validation and Evidence Packs

QC design for R and mixed R/SAS environments: independent programs, reconciliation outputs, logs, reviewer checklists, and decision records that reduce review ambiguity.

ValidationReconciliationAudit trailReview
SAS

SAS-Compatible Alternative Track

SAS support remains available for sponsors and legacy environments that require it, or as a targeted independent validation path for selected high-risk outputs.

LegacySponsor needsMacro reviewMigration
Implementation path

Build the environment while delivering the study.

The goal is not a theoretical platform. Each engagement produces usable code, reusable patterns, and study deliverables that your team can inspect.

1

Assess

Review current tools, study pipeline, standards, data sensitivity, staffing, and cost pain points.

2

Blueprint

Define the R/SAS split, agent candidates, validation model, and first workflows to automate.

3

Build

Create templates, functions, prompts, review checklists, and output generation pipelines.

4

Operationalize

Deliver documentation, training notes, QC evidence, and a practical roadmap for the next study.

Cost logic

Why R-first matters for smaller teams.

  • Open-source R reduces dependence on expensive proprietary statistical software seats.
  • Reusable R functions and Quarto reports make repeat studies faster to start.
  • AI tools integrate naturally with text-based R code, Git diffs, markdown specs, and automated checks.
  • SAS is still available when the business or sponsor requirement justifies it.

Want to know which parts of your current study workflow should move to R and AI-assisted automation?

A focused review can identify the highest ROI workflows without disrupting current delivery commitments.

Request SCE Review