Optimizing the synthesis of clinical trial data using sequential trees.

ABSTRACT:

Objective

With the growing demand for sharing clinical trial data, scalable methods to enable privacy protective access to high-utility data are needed. Data synthesis is one such method. Sequential trees are commonly used to synthesize health data. It is hypothesized that the utility of the generated data is dependent on the variable order. No assessments of the impact of variable order on synthesized clinical trial data have been performed thus far. Through simulation, we aim to evaluate the variability in the utility of synthetic clinical trial data as variable order is randomly shuffled and implement an optimization algorithm to find a good order if variability is too high.

Materials and methods

Six oncology clinical trial datasets were evaluated in a simulation. Three u

SUBMITTER: Emam KE 

PROVIDER: S-EPMC7810457 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature