Productize your real world data to quickly demonstrate the safety and efficacy of your treatments to regulators, payers, providers, and patients.
Rapidly analyze new and existing data types
Advanced self-service analysis to extract the most value from your real world data (RWD). Gain a complete picture of your clinical and commercial results to inform decision-making across your drug development lifecycle.
Instant access to analysis
Promptly analyze a wide range of RWD sources using a streamlined approach to statistical analysis. Tag.bio’s data products offer self-guided, no-code analysis apps that enable you to instantly ask and answer your clinical, regulatory, and commercial questions. Below are some examples of how our apps can be used to identify trends to support your pre- and post- real world evidence studies:
Perform unsupervised clustering analysis to clinically demonstrate the value of your treatment
Create cohorts — using the point-and-click function — to compare the clinical outcomes of your drug candidate against the standard of care
Generate a cox survival analysis report, such as from R or Python scripts, on your defined subgroups to identify clinical trial endpoints
Access the outputs of your ML/AI models to identify and diagnose patients with rare diseases so that treatments can be initiated
See analysis app demo
Single Cell Gene Expression
In this demo, we’re going to show you how you can use a no-code analysis app to look for genes that are differentially expressed in cancer cells.
How Top Organizations Are Using Tag.bio
A Digital Health Company is generating evidence to show that their health app produces better outcomes and lower claim costs for their corporate customers.
A Top 50 Pharma is analyzing Phase IV & RWD to investigate both drug & medication-compliance device clinical trials.
Productizing your data
Your data analysis process can be dramatically streamlined when you productize your data. By turning your RWD into analyzable data products, you gain fast access into crucial information that will help you demonstrate the safety, efficacy, and value of your treatments to regulators, payers and providers. Furthermore, the data products will be readily available throughout your drug development lifecycle for quick references.
Curious what productizing your RWD looks like? Below are two examples.
Pfizer’s Trial Phase III - JAVELIN 101
This data product contains data on avelumab with axitinib versus sunitinib in advanced renal cell cancer. Examples analysis apps available within this data product:
This data product contains data on patient encounters and costs for 80,000 patients at 8 hospitals compared to standard Medicare costing. Examples analysis apps available within this data product:
Types of data that can be turned into data products
Tag.bio’s data product framework will integrate any combination of datasets into a simplified, flexible model – perfect for use within apps or as a dataframe to export to other tools.
Some examples of potential data sources, types, and formats:
Data type examples
- Clinical trials
- Longitudinal studies
- Annotation, ontology, pathways
- Machine behavior and maintenance
- Drug response & pKa studies
- Patient-reported outcomes
- Patient apps
- Patient registries
- Medical tests
- Hospital data
- Pharmacy data
- Medical claims
- Insurance claims
Data source examples
- Siloed data
- Data warehouses
- Data lakes
- Data products
- GEO & ArrayExpress
- Clinical trials
- UK Biobank
- Epic/Clarity EMR
- OMOP (Observational Medical Outcomes Partnership)
- FHIR (Fast Healthcare Interoperability Resources)
- Patient registries
Data format examples
- Any form of tabular data (CSV/TSV)
Talk To Our Experts
If you don’t see the data types or sources you’re looking for in the lists, chances are we can accommodate them.
Talk to our experts to discover how to turn your real world data into data products!
Harmonize your RWD using data mesh
Build a data mesh of reusable real world data products to promote data harmonization and bring fragmented data together across markets – from clinical trials to digital health to claims data.
Seamlessly integrate both historical and emerging data, such as biomarkers and IOT, to optimize current and future clinical trials
Instantly compare your proprietary data against public datasets to demonstrate the value, effectiveness, and safeness of your treatments
Quickly perform exploratory and confirmatory analysis to reduce costs and gain leverage in pricing negotiations
Data mesh in a box
Tag.bio offers an out-of-the-box solution to help you accelerate your data mesh implementation. Talk to our experts to learn more about our offerings!
Promote cross-organizational collaboration
Access your data mesh of real world data products and collaborate efficiently using Tag.bio’s two-sided analysis environment.
Tag.bio offers a customizable Analysis Platform to support your unique business and research needs. Leverage the enterprise features to drive collaboration and innovation.
- Ask and answer your clinical, regulatory, and commercial questions with confidence – by using self-guided, no-code analysis apps within each data products to perform advanced analytics, such as clustering analysis and generating R reports
- Promptly identify, store, and share your useful data artifacts (UDATs), such as biomarkers and clinical outcomes, with your fellow team members
For data scientists and bioinformaticians
- Demonstrate the value of your data science in RWE – by making your work accessible to researchers and other domain experts across the organization
- Save hours from performing one-off analyses – by repurposing your work as reusable analysis apps across multiple data products
- Promote a consistent view of reliable, harmonized RWD – by providing a platform that presents a single source of truth
- Boost efficiency and drive innovation – by streamlining the data analysis process and making data science accessible across your organization
For strategic IT
- Simplify your data governance process as RWD grows – by streamlining your data management and access
- Stay compliant with regulatory and legal requirements without compromising data use
To help you build high quality data products, Tag.bio offers a Developer Studio that uses a familiar, Jupyter notebook-based setting. Leverage the pre-built templates to streamline your data product creation.
For data scientists
- Access reliable and harmonized RWD from data products for your R & Python algorithms and ML/AI models
- Collaborate more efficiently when your fellow data scientists and data engineers use the same tool to access data and build data products
For data engineers
- Deliver quality and harmonized RWD to data scientists and promote data reusability – by standardizing how nomenclatures get mapped into the data products
- Advance data literacy when you use terminologies that researchers understand – by partnering with domain experts to standardize the terminologies being mapped into the data products
For quality controlled analysts
- Rapidly reproduce any reports and analyses to support RWE – with instant access to all the versioned data, software packages, and analysis methods
- Streamline the auditing procedures when you gain transparency into the data mapping process and source code
- Set a lasting foundation that promotes consistent quality controlled outputs as the demand for RWD and RWE grows and new data types emerge
- Significantly free up your resources time so that they can focus on what they do best – by streamlining and automating manual repetitive work
More ways to use Tag.bio
Tag.bio helps you overcome data challenges across your drug development process.
To identify novel targets, pathways, biomarkers, and signatures.
To evaluate potential success of a treatment, using data from cell lines and model organisms.
To stratify heterogeneous patient populations and develop diagnostics for stratification, toxicity, and response to treatment.
To produce quality controlled reports for reviews.
Data Security in Clinical Research Environment
Tag.bio is hosted entirely within your secure network and/or your secure cloud. Source data storage and access is tightly controlled within your network, and the platform is compliant with standards such as GxP, GDPR and HIPAA.