Generating high-fidelity synthetic time-to-event datasets to improve data transparency and accessibility.

ABSTRACT:

Background

A lack of available data and statistical code being published alongside journal articles provides a significant barrier to open scientific discourse, and reproducibility of research. Information governance restrictions inhibit the active dissemination of individual level data to accompany published manuscripts. Realistic, high-fidelity time-to-event synthetic data can aid in the acceleration of methodological developments in survival analysis and beyond by enabling researchers to access and test published methods using data similar to that which they were developed on.

Methods

We present methods to accurately emulate the covariate patterns and survival times found in real-world datasets using synthetic data techniques, without compromising patient privacy. We mode

SUBMITTER: Smith A 

PROVIDER: S-EPMC9229142 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature