Article

Replicate Engineered Virtual Patient Populations as Surrogates for Real Patient-Level Data

2018-04-25

Abstract excerpt

<h4>Objectives</h4> To demonstrate a new method for generating virtual, individual-level data by testing it on a known clinical trial population. <h4>Design</h4> Virtualization of aggregate data from a clinical trial. <h4>Setting</h4> Virtual <h4>Participants</h4> 936,100 virtual patients <h4>Interventions</h4> None <h4>Main Outcomes Measures</h4> Odds ratios for adverse outcomes in virtual patient populatio...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: d12f3ecf-5c52-53cd-bfae-ceedaa3d0997
DOI: 10.1101/308403

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Replicate Engineered Virtual Patient Populations as Surrogates for Real Patient-Level DataDOI 10.1101/308403Read

The <i>All of Us</i> Research Program: data quality, utility, and diversitySemantic proximity Read

Assessing the Secondary Use and Scientific Impact of Shared Clinical Trial Data: A Cross-Sectional Study of Clinical Trials Shared on the YODA Project PlatformSemantic proximity Read

Stratified Covariate-Reconstructed Survival (SCRS): Extending Guyot IPD Reconstruction to Preserve Covariate-Outcome Associations for Population- Adjusted Indirect ComparisonsSemantic proximity Read

Einstein Cardiovascular Data Registry: A Longitudinal, Anonymized Platform for Advancing Cardiovascular Outcomes ResearchSemantic proximity Read

Computational Phenomapping of Randomized Clinical Trials to Enable Assessment of their Real-world Representativeness and Personalized InferenceSemantic proximity Read

Actionability of Synthetic Data in a Heterogeneous and Rare Healthcare Demographic; Adolescents and Young Adults (AYAs) with CancerSemantic proximity Read

Efficient Generation and Selection of Virtual Populations in Quantitative Systems Pharmacology ModelsSemantic proximity Read

A plasmode simulation-based bias analysis for residual confounding by unmeasured variables leveraging information-rich subsetsSemantic proximity Read

Evaluation of AI-Generated Synthetic Data for Clinical Research in Secondary Cardiovascular Prevention among Dyslipidemia PatientsSemantic proximity Read

Clinical trial and real-world data: A comparative study in patients with diabetic kidney diseaseSemantic proximity Read

Patient-centric synthetic data generation, no reason to risk re-identification in the analysis of biomedical pseudonymised dataSemantic proximity Read

Privacy-by-design generation of two virtual clinical trials in multiple sclerosis and their release as open datasetsSemantic proximity Read