Synthetic Dataset Generation

Synthetic data is data which has been generated using purpose-built computer simulations, mathematical/statistical models or algorithms. Synthetic data is generated to meet specific needs or certain conditions that may not be found in the original, real data. It has many applications across multiple industries including:

• Market research and business intelligence
• Testing and validating software products and systems
• Building and testing algorithms
• Predictive modelling, machine learning and AI

Synthetic data is useful in clinical research, where it can be used:

• In clinical trial design optimization to maximize chance of success.
• To create external control arms for clinical trials to save time and resources.
• In anonymization to enable the sharing of regulated or sensitive data.
• To create large, auto labelled data for predictive modelling, machine learning and AI to address issues of imbalanced data.

Within KerusCloud® is a synthetic data generator. It can handle diverse and complex data collected from disparate data sources and produce synthetic datasets from them. KerusCloud’s exceptional modelling capability allows it to incorporate realistic characteristics into the synthetic datasets it produces such as missing data, truncation and censoring. It can model the inter-correlation between subject-level data such as subgroups and strata, risk factors/covariates and multiple outcomes and data types. This delivers a highly realistic synthetic version of the original data.