Our REAL-WORLD DATA OFFERINGS

Based on a unique real-world genomic dataset, built for precision oncology decisions.

Explore Omico’s real-world data offering

 

This page is designed to show what’s possible with Omico’s real-world precision oncology data—a unique, prospectively consented and integrated dataset combining genomic, clinical and outcomes insights—and how it can help you generate insights, reduce uncertainty, and make more confident decisions.

As you scroll, you can:

  • interact and visualise the breadth and depth of the data firsthand
  • view our suite of RWD offerings at a glance
  • dive into each offering in more detail, including its practical applications and value

Explore our real-world data below


Our RWD offerings at a glance

Explore each offering in detail

1. Interactive RWD Dashboard

Real-world data subscription

An interactive, aggregated view of Omico’s precision oncology database, enabling high-level analysis of patient populations by cancer type or genomic biomarker.

The interactive dashboard allows users to explore key variables including mutations, co-mutations, demographics, treatments and outcomes, supporting early landscape assessment and hypothesis generation. 20,000+ patients and growing.

  • Understand distribution of primary sites, mutations and co-mutations
  • Profile patient demographics within biomarker-defined cohorts
  • Explore real-world treatment patterns and common interventions
  • Review outcomes across tumour types and genomic subgroups
  • Inform early clinical trial thinking and protocol development
  • Identify areas for deeper investigation

2. Custom Data Analytics

Targeted, decision-ready insights

A consultative analytics partnership delivering tailored, decision-ready insights from Omico’s real-world data.

Each engagement is co-defined through a structured process, ensuring analyses are aligned to specific strategic questions. 

Outputs are grounded in robust methodology, scientific independence and governed use of de-identified patient-level data.

  • Align pipeline, study design and indication prioritisation with real-world treatment patterns and populations with the greatest unmet need
  • Rapidly answer high-priority strategic questions, assess feasibility for future studies, and focus formal evidence generation on areas of greatest impact
  • Inform value strategy, economic assumptions and access planning based on observed patient pathways rather than theoretical models
  • Understand lines of treatment and interventions for a defined patient cohort

3. Patient Journey Mapping

End-to-end patient pathway insights

A clinically contextualised, data-driven view of how patients move through diagnosis, testing, treatment and access within the Australian healthcare system.

By integrating clinico-genomic, geographic, treatment, access and patient-reported data, this offering maps real-world patient pathways across public and private settings over time.

  • Assess data fitness, define meaningful endpoints and prioritise future local evidence generation based on real patient flow
  • Navigate Australia’s reimbursement environment and identify where delays, restrictions or system complexity impact time to treatment
  • Understand how care pathways, funding mechanisms and treatment sequencing operate in practice, and how they influence resource use, outcomes and value in the Australian system
  • Align global strategy with local clinical reality

4. RWE Generation Projects

Protocol-driven real-world evidence studies

Collaborate with Omico’s CSO, RWD experts and bioinformaticians to develop protocol-led real-world evidence studies, such as natural history or concurrent cohort designs for Phase II/III programs.

We work with you from research question through to final outputs—co-developing study concepts, designing observational protocols, applying robust statistical methodology, and generating publication- and HTA-ready evidence from de-identified patient data under transparent governance and scientific independence.

  • Design and execute fit-for-purpose studies that align with global evidence plans and meet regulatory and scientific standards
  • Understand outcomes in defined cohorts across different treatments and genomic profiles
  • Answer clinically meaningful questions and generate high-quality publications that inform practice and guideline discussions
  • Address payer and HTA evidence requirements with methodologically robust real-world studies

5. External Control Arm Solutions

Advanced comparative real-world analytics

A methodology-led solution developed by Omico for constructing external control arms using clinically and temporally relevant real-world data.

It enables rapid 1:1 case-to-control matching within a governed, curated, de-identified dataset, incorporating covariate weighting and iterative cohort sampling.

Users can refine matching variables and similarity thresholds to explore how different clinical scenarios influence comparative results—generating transparent, hypothesis-shaping insights for study planning, evidence strategy and reimbursement decisions.

  • Evaluate treatment effect in single-arm trials
  • Analyse time-to-event outcomes, including overall survival
  • Generate supportive evidence to complement randomised controlled trials
  • Provide comparative data to inform pipeline development, and asset evaluation or acquisition
  • Support regulatory, reimbursement and market access decision-making

6. Direct Data Access

Secure row-level data access

Secure access to de-identified, row-level patient data for advanced and bespoke analysis.

Access requests are reviewed by Omico’s Access Committee, with Human Research Ethics Committee (HREC) approval required prior to access via a secure environment or controlled data transfer.

  • Execute bespoke statistical methodologies or integrate the dataset into a global evidence framework
  • Perform detailed cohort construction, endpoint derivation or exploratory analyses for highly specific development questions
  • Run internally developed models or advanced analyses requiring patient-level data that cannot be executed in aggregated outputs

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