A Framework for Generating Realistic Synthetic Tabular Data in a Randomized Controlled Trial Setting

TL;DR

This paper proposes a new framework for generating realistic synthetic tabular data in randomized controlled trial settings, combining statistical and machine learning methods to better preserve data distribution and features.

Contribution

It introduces a sequential generation approach using R-vine copula models and regression techniques, improving the realism of synthetic RCT data compared to existing methods.

Findings

Sequential generation with R-vine copula models is most effective.

The approach captures key features of real RCT data.

Synthetic data closely mimics actual trial outcomes.

Abstract

Generation of realistic synthetic data has garnered considerable attention in recent years, particularly in the health research domain due to its utility in, for instance, sharing data while protecting patient privacy or determining optimal clinical trial design. While much work has been concentrated on synthetic image generation, generation of realistic and complex synthetic tabular data of the type most commonly encountered in classic epidemiological or clinical studies is still lacking, especially with regards to generating data for randomized controlled trials (RTCs). There is no consensus regarding the best way to generate synthetic tabular RCT data such that the underlying multivariate data distribution is preserved. Motivated by an RCT in the treatment of Human Immunodeficiency Virus, we empirically compared the ability of several strategies and two generation techniques (one machine learning, the other a more classical statistical method) to faithfully reproduce realistic data. Our results suggest that using a sequential generation approach with a R-vine copula model to generate baseline variables, followed by a simple random treatment allocation to mimic the RCT setting, and subsequent regression models for variables post-treatment allocation (such as the trial outcome) is the most effective way to generate synthetic tabular RCT data that capture important and realistic features of the real data.