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Datalution™
Datalution™

A Meaningful Synthetic
Data Generation Solution

A Meaningful Synthetic
Data Generation Solution

Datalution™ Product Videos

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.  

One of the challenges to ensuring that quality checks are sufficiently in place is the generation of test data for testing data capture screens, edit checks, and even downstream programming for dataset generation and analysis.  This data frequently is manually entered for testing and perhaps not as much of that data is provided given demanding timelines.  

Are you tired of spending countless hours collecting and manipulating data for testing purposes? Do you want to ensure that your data management and biometrics delivery is robust and reliable before having to deploy it in production to work on real data? Look no further than Datalution. 

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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.
  • Allows you to identify and fix potential issues before your delivery programs are expected to process real data funneling in from your clinical trials, minimizing the time to begin analysis/review of the data.

     

  • Tests edge cases and unusual scenarios, helping you to identify potential issues that might be difficult or impossible to replicate with real world data.

     

  • Allows users to introduce statistical outliers into fields that have been mostly compliant with controlled terminologies or normal ranges, to test your edit check and QC processes. 

 

Don’t waste any more time manually generating and entering data for testing purposes. Try Datalution today and experience the benefits of meaningful, synthetic data & synthetic patient data. This allows you to test your clinical data management, database, statistical programming, data science, clinical data analytics, biostatistics and statistical setup activities.

Show more Show less
Datalution™
A Meaningful Synthetic
Data Generation Solution  

Datalution™, a “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.  

One of the challenges to ensuring that quality checks are sufficiently in place is the generation of test data for testing data capture screens, edit checks, and even downstream programming for dataset generation and analysis.  This data frequently is manually entered for testing and perhaps not as much of that data is provided given demanding timelines.  

Are you tired of spending countless hours collecting and manipulating data for testing purposes? Do you want to ensure that your data management and biometrics delivery is robust and reliable before having to deploy it in production to work on real data? Look no further than Datalution. 

Show more Show less

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.
  • Allows you to identify and fix potential issues before your delivery programs are expected to process real data funneling in from your clinical trials, minimizing the time to begin analysis/review of the data.

     

  • Tests edge cases and unusual scenarios, helping you to identify potential issues that might be difficult or impossible to replicate with real world data.

     

  • Allows users to introduce statistical outliers into fields that have been mostly compliant with controlled terminologies or normal ranges, to test your edit check and QC processes. 

 

Don’t waste any more time manually generating and entering data for testing purposes. Try Datalution today and experience the benefits of meaningful, synthetic data & synthetic patient data. This allows you to test your clinical data management, database, statistical programming, data science, clinical data analytics, biostatistics and statistical setup activities.

Show more Show less

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

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

Navigational Panel:
  • Study, Doc, Repository, Strategy Application, and tracking of strategies applied
  • Parameter-driven Risk Analysis and Data Utility per EMA guidance. Determine by data/variable or determine for entire study/project.
Strategy Application:
  • Repository and “Pre-De-ID Analysis” tools provide methods/strategies to the user to leverage with confidence speeding setup and delivery.
View Panels:
  • See datasets before/after and strategies applied immediately to confirm which provide the best results for a specific project. “Sync scroll” summary statistics and histograms are available to make it easier for reviewers/analysts to evaluate the effectiveness of applied de-identification strategies.

  • View docs before/after with “Redaction Proposal”-like display. De-identified values are highlighted and easily found for review by either the navi panel or via a drop-down menu directly over the doc. Annotations show how the value will appear in accordance with EMA Policy 0070 in the final de-identified doc.

  • Get results before/after with interactive visualizations to identify data risks over/under threshold(no comma) and alternate displays to view risk separately by quasi-identifiers/variable.

Strategy Types:
  • From basic “search-and-replace/redact” to ML and regular expressions to leveraging of de-identified datasets to queries and patient-specific/narrative strategies.