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Advanced Analytics bundle · for Head of Biostatistics

From governed OMOP data to submission-grade evidence

One analytics spine for biostatistics: define cohorts once, run repeatable analyses across real-world and study-collected data, and carry results straight through QC into Briefings and submission outputs — with quality monitoring and randomization reading from the same governed record.

Adopted together by the Head of Biostatistics CuRE Calculate CuRE Caliber CuRE Cascade

The problem today

Biostats teams stitch together SAS, ATLAS, home-grown R, and separate RBQM and IRT tools that each see only part of the data, leaving analyses hard to reproduce and outputs reassembled by hand.

Faster

Cross-modal answers in one workspace

Cohort, characterization, and prediction run natively across trial, registry, EHR, biomarker, manufacturing, and shipping signals resolved to one OMOP record, so questions that span data sources stop being integration projects.

Better

Reproducible, submission-grade evidence

Spec-first, content-hashed cohorts and double-programming QC keep narratives, tables, figures, and citations tied to the governed data, producing CDISC-conformant outputs defensible to a regulator.

Cheaper

One spine replaces the point stack

Analytics, risk-based quality monitoring, and randomization share one governed OMOP model and one validation story instead of per-vendor licenses, integration work, and re-validation cycles.

How this bundle composes

Turn governed OMOP data into repeatable analyses, Briefings, and submission-grade outputs.

CuRE Calculate Advanced Analytics · Analysts / Researchers

Calculate is the analytics hub where biostatisticians define cohorts once on the governed OMOP record and carry them through characterization, QC, AI-authored Briefings, and submission-grade outputs without rebuilding the analysis at each step.

CuRE Caliber RBQM · Quality & risk managers

Caliber turns risk-based quality management into an always-on early-warning system, detecting risk where it appears first — including EHR-observed events a site has not yet reported — and routing it to the person who can act.

CuRE Cascade RTSM / IRT · Clinical Ops + Statistician + CRC

Cascade gives clinical ops randomization and trial supply orchestration on the same OMOP substrate as the rest of the study — IRT capability without standing up a separate IRT vendor, silo, or reconciliation process.

Cross-app journey
Live demo — synthetic data, runs in your browser

Design once, generate everywhere (MDR)

Change a study-design element in Control's metadata repository and watch it propagate downstream — the Conduct FHIR→OMOP mapping, the Capture eCRF and its edit checks, and the Calculate define.xml / characterization all regenerate from the same design. The metadata-driven "design once, generate everywhere" story.

design elementstudy protocolphasePhase III (randomized)conditionType 2 diabetessnomed44054006omop concept201826
Stage 1 · CuRE ControlStudy-design authority (MDR)

Author the study-design element once

In Control's metadata repository the design is set once: the study phase and its condition of interest. Control's real stage-matrix logic derives the funnel from the phase — change either and everything downstream re-derives.

Funnel stage matrix — derived by Control from the chosen phase (ADR-CTL-010)
ScreenedEnrolledRandomizedOn TreatmentCompleted
study VELT-042screened 115enrolled 83vs. plan behind

Both dropdowns are the study design authored once in Control's metadata repository. The stage matrix above is Control's stagesForType() run live on the chosen phase; the counts come from the real assessStudy(). Everything below regenerates from this same design — no re-authoring.

FeedsConduct
condition of interest →Type 2 diabetes · SNOMED 44054006

The design's condition (a SNOMED code) is the metadata Conduct maps to an OMOP concept next — the single fact this journey threads forward.

Stage 1 / 4

This is the full cross-app journey — open it alongside every CuRE journey.

Why it holds up

  • Calculate defines OMOP-native cohorts once and reuses them across studies, registries, and data refreshes, then AI-authors Briefings whose narrative, tables, and citations stay tied to the underlying analysis.
  • Calculate's submission pipeline is designed for native ADaM, TLF, and define.xml generation with CDISC CORE conformance, plus double-programming QC and empirical-calibration diagnostics as roadmap parity points.
  • Caliber computes centralized statistical monitoring and live KRIs off the same OMOP data, surfacing risk signals and drafting investigation narratives without a separate quality warehouse.
  • Cascade writes randomization and dispensation events back into the platform OMOP store, so allocation and supply data are analyzable in Calculate rather than locked in a separate IRT silo.

The apps in this bundle

CuRE Calculate

Calculate is the analytics hub where biostatisticians define cohorts once on the governed OMOP record and carry them through characterization, QC, AI-authored Briefings, and submission-grade outputs without rebuilding the analysis at each step.

  • Cross-modal questions become native: ask whether cryopreservation, manufacturing variance, shipping time, biomarkers, ePRO, and EHR history affect CAR-T outcomes in one workspace.
  • A governed in-app data-science IDE is the primary surface: a reactive SQL/Python/R notebook (dependency-inferred cell-DAG, live co-editing, publish-as-data-app) on the Conduct-governed enclave, with every result egress-checked and snapshot-pinned for reproducibility — JupyterLab stays as a per-session escape hatch. Governed AI cells ride the same enclave: generate code from natural language or narrate a cell's output in prose, each egress-checked before any data reaches the model.
Explore CuRE Calculate
CuRE Caliber

Caliber turns risk-based quality management into an always-on early-warning system, detecting risk where it appears first — including EHR-observed events a site has not yet reported — and routing it to the person who can act.

  • RWD-native RBQM can flag EHR-observed events a site has not reported yet, then draft the investigation narrative and CAPA trail.
  • The signal engine drives both execution surfaces CuRE owns — Control monitoring-visit planning and Capture targeted SDV — the closed RBQM loop CluePoints can't own end-to-end.
Explore CuRE Caliber
CuRE Cascade

Cascade gives clinical ops randomization and trial supply orchestration on the same OMOP substrate as the rest of the study — IRT capability without standing up a separate IRT vendor, silo, or reconciliation process.

  • Algorithm core is built in-house and validated to randomizeR / carat as statistical-equivalence oracles.
  • Predictive supply simulation forecasts demand and optimizes overage from the enrollment RWD Cascade owns — RTSM's flagship differentiator.
Explore CuRE Cascade

See the Advanced Analytics bundle in action

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