When evidence compounds, uncertainty shrinks.
Variacle is the clinical intelligence meta-layer that turns fragmented data into regulatory-grade evidence.
Clinical development is moving beyond the siloed trial. The future belongs to those who can compound evidence across studies. If you are not aggregating statistical power through Variacle, you are losing the race to Market Access.
What is Variacle?
The Problem
As clinical development moves beyond traditional trials, companies increasingly rely on real-world data (RWD). But transforming that data into evidence that regulators can trust remains one of the hardest challenges in drug development.
The Solution
Variacle bridges that gap. We provide a rigorous, transparent, and auditable framework to analyze real-world data using state-of-the-art causal inference. Our approach is designed to meet the expectations of regulators while remaining flexible enough for modern, data-rich settings.
Sample Size Drastic Reduction
Allocate fewer people to placebo while augmenting power through network effects. Strengthen clinical trial dossiers with real-world evidence.
Compliance as a Service
RCT/RWD ingestion pipelines that meet regulatory-grade governance. Continuous updates aligned to rapidly evolving regulations.
Regulatory Certainty
Variacle focuses on methodological soundness, reproducibility, and clarity—key requirements for scientific advice, clinical dossiers, and health authority interactions.
We don't move your data.
We allow it to speak.
Historical data aggregators failed because they tried to centralize sensitive records. Variacle takes a different approach aligned with FDA's Sentinel initiative.
Statistical Invariance, Not Rigid CDMs
We replace expensive ETL processes and rigid Common Data Models (like OMOP) with causal inference. We compute evidence portions locally.
Zero Data Egress
Patient records never leave the hospital firewall. Variacle only aggregates the statistical answers, ensuring privacy compliance by design.
This is the practical value of Causal Inference - it's not about showing off technical sophistication, but about putting our principles into practice. It allows us to estimate counterfactual outcomes without requiring us to subject actual people to different conditions. This brings us closer to a reality where we can reduce how frequently we tell someone they've been assigned to receive no intervention.
The Science: Beyond the Impossibility Theorem
Why standard data integration fails to convince regulators.
The Limit of Adaptation
It is mathematically impossible to shorten confidence intervals using observational data if the magnitude of the bias is unknown. As proven by Chen, Zhang, and Ye (2021), any valid confidence interval must cover the "worst-case" bias, rendering the external data useless for precision gains. Without knowledge of the confounding bias magnitude, hybrid estimators leveraging observational and experimental data cannot produce shorter confidence intervals than estimators just using purely experimental data.
The "Mean Exchangeability" Trap
Regulators routinely reject external controls because current methodology relies on a fragile assumption: Mean Exchangeability over Studies (S).
This assumes that enrolling in the RCT ($S=1$) does not affect a patient's outcome compared to the real world ($S=1$). Existing methods rely on this conditional independence, but in reality, it is almost always violated.
Standard Method Assumption
Notation:
• a ∈ {0,1}: treatment indicator
• Ya: potential outcome under treatment a
• S ∈ {0,1}: study indicator (0=RCT, 1=RWD)
• W: observed covariates / confounders
Protocol-Driven Adherence
Subjects often adhere more strictly to treatment regimens in an RCT ($S=0$) than in the real world. The "Trial Effect" modifies the outcome, violating exchangeability.
Measurement Inconsistency
Digital health endpoints measured via different devices introduce batch effects. Manufacturer discrepancies cause potential outcomes in RWD to differ from RCT, even for the same patient.
The Variacle Difference
Variacle is the first regulatory engine designed to function withoutassuming exchangeability. We explicitly model the divergence between Trial and Real-World populations.
We allow for reality
Core Literature
[1] Chen, S., Zhang, B., & Ye, T. (2021). Minimax rates and adaptivity in combining experimental and observational data. arXiv:2109.10522
[2] Valancius M, Pang H, Zhu J, Cole SR, Funk MJ, Kosorok MR. (2024). A causal inference framework for leveraging external controls. Biometrics, 80(4), ujae095.
[3] Colnet B, Mayer I, Chen G, et al. (2020). Causal inference methods for combining randomized and observational data: a review. Statistical Science.
The Tides Have Turned
Regulators historically said "No" to external controls due to hypothesis violations. Today, global guidance is demanding hybrid designs.
Key Regulatory Shifts
The "Methodological Constraints" Era
The EMA's latest Reflection Paper on External Controls demands rigor. It explicitly scrutinizes the "methodological constraints" required to turn Real-World Data into pivotal evidence. Variacle provides the exact answer to this scrutiny: an auditable causal framework that validates exchangeability assumptions instead of ignoring them, aligning perfectly with the EU's demand for defensible causal conclusions.
A Pragmatic Step Toward RWE
The MHRA has released draft guidance on External Control Arms (ECAs). While RCTs remain the gold standard, they acknowledge ECAs can provide credible evidence when RCTs are unethical or infeasible. Key requirement: Hybrid designs over single-arm studies.
The 21st Century Cures Act Era
Variacle is the operational engine for the FDA's vision. We align with the 'Framework for FDA's RWE Program' and 'Assessing EHR Data'. We enable the same distributed architecture as FDA's Sentinel but replace rigid harmonization with causal inference estimands without moving data.
Same Principles, Different Railways
Europe (DARWIN EU®), Japan (MID-NET®), and China (Hainan Pilot) are all aligning. The winners won't be the biggest datasets—they'll be the clearest protocols and auditable code.
The Evidence: RWE Success Stories
Vimpat® (UCB)
RWE supported new loading dose in children.
Vijoice® (Novartis)
Chart review provided effectiveness evidence where RCTs weren't feasible.
Orencia® (BMS)
Registry data served as pivotal evidence for transplant patients.
Actemra® (Genentech)
National death records used to assess mortality.
Why Variacle Fits This Landscape
Variacle acts as a "Compliance-as-a-Service" layer. We provide the immutable audit trails, the blind-maintenance, and the "Mean-Exchangeability" assessment that regulators demand to prove that external controls match protocol criteria.
Who is Variacle For?
Building the modular trial blocks of the future.
Biotech and Pharma
The "Evidence Wallet." Extract value from fragmented data you already own. Fill data gaps in Rare Disease or Oncology without running expensive new trials.
- De-risk Phase II submissions
- Avoid "sample imbalance" rejection
- Get regulatory air cover
Hospitals and Registries
Post-Approval Power. You are constantly running Phase IV, head-to-head, and system studies. Variacle connects this work to regulatory value without data egress.
- Monetize insights, not data
- Participate in global research
- Zero compliance risk (data stays local)
Data Science Teams
White-Box Evidence. Move beyond black-box predictions. Get transparent, causal inference that quantifies uncertainty and tests assumptions.
- Automated sensitivity analysis
- Reproducible pipelines
- Audit-ready outputs