A Meaningful Synthetic
Data Generation Solution
A Meaningful Synthetic
Data Generation Solution
Datalution™ - Meaningful Synthetic Data Generation Solution
Introducing Datalution – Your Solution for Synthetic Data Generation
Generate Meaningful Synthetic Data for Testing and Analysis
Introducing Datalution, the all-in-one solution for generating synthetic data for testing electronic data capture screens, edit checks, Data management activities (as part of UAT Process), programming, and statistical setup activities.
Our product generates “meaningful” synthetic data that simulates real-world scenarios, enabling you to test such things as your edit checks, CDISC (SDTM) dataset generation programs, data visualizations, and TFL (Tables, Figures, and Listings) generation programs under a wide range of conditions. It makes clinical trial simulation and synthetic data generation for healthcare easier.
With our product, you can create large and diverse datasets quickly and cost-effectively, without the need to first collect real data.
Clean data is crucial for the accuracy, safety, regulatory compliance, efficiency, cost-effectiveness, and reproducibility of clinical trial analysis. To ensure the quality of the data, it is important to have appropriate data cleaning and quality control procedures in place.
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Datalution™ offers a range of features that make it the ideal solution for testing database, programming, and statistical setup activities:
Our product:
- Provides customizable data generation, allowing you to tailor the data to meet your specific testing requirements.
- Supports a variety of data types, including numeric, text (utilizing expected categorical values or realistically expected values as appropriate), and date/time data, making it easy to create complex datasets.
- Generates data that is statistically representative of the real-world, allowing you to test your delivery programs and processes in a way that accurately reflects the “Patient Journey”, i.e., the typical behavior of clinical trial patients.
Datalution™ - Synthetic Data Generation for Healthcare
Feature Summary
Synthetic Data for Medical Research and Drug Development
Leverages clinical study documents, including:
- Clinical Trial Protocol
- eDC/CDMS specifications such as Medidata Rave’s ALS
- eCRF Completion Guidelines
Generates “Meaningful” test data:
- Categorical variables have values that comply with their associated code lists
- Continuous variables have values that comply with their normal ranges
- Outliers and bad values can be optionally introduced to test these lists/ranges
- Dates and time values are set in chronological order in compliance with the protocol’s visit schedule
- Ability to ensure specific number of patients in treatment and demographic groups
Ensures data are representative of the “Patient Journey”:
Configurable test data groups to represent:
- Screen failures
- Completed patients
- Ongoing patients
- Early withdrawal/discontinued patients
Completion of data forms in compliance with the above groups
