Article

Characterizing Subpopulations with Better Response to Treatment Using Observational Data – an Epilepsy Case Study

2018-03-29

Abstract excerpt

Electronic health records and health insurance claims, providing observational data on millions of patients, offer great opportunities, and challenges, for population health studies. The objective of this study is identifying subpopulations that are likely to benefit from a given treatment using observational data. We refer to these subpopulations as “better responders” and focus on characterizing these using line...

Topics

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

Identifiers and source

Literature Corpus work: 2c58c6b7-f3d0-56d3-9518-79730f433130
DOI: 10.1101/290585

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Characterizing Subpopulations with Better Response to Treatment Using Observational Data – an Epilepsy Case StudyDOI 10.1101/290585Read

Virtual Trials: Causally-validated treatment effects efficiently learned from an observational cancer registrySemantic proximity Read

Developing and validating a clinical prediction model to predict epilepsy-related hospital admission or death within the next year using administrative healthcare data: a population-based cohort study protocolSemantic proximity Read

Leveraging population-based clinical quantitative phenotyping for drug repositioningSemantic proximity Read

High-Throughput Observational Evidence Generation Using Linked Electronic Health Record and Claims DataSemantic proximity Read

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

Medication-Wide Association Study of Alzheimer’s Disease and Related Dementias: Identifying Drug Candidates from Electronic Health Records through Explainable AISemantic proximity Read

Pharmacoepidemiology research: Drug therapy and drug safety in mental healthSemantic proximity Read

The R.O.A.D. to clinical trial emulationSemantic proximity Read

Computational Phenomapping of Randomized Clinical Trial Participants to Enable Assessment of Their Real-World Representativeness and Personalized Inference.Semantic proximity Read

Machine learning enabled subgroup analysis with real-world data to inform better clinical trial designSemantic proximity Read

Estimating the Impact of Pre-Exposure Prophylaxis (PrEP) on Mortality in COVID-19 Patients: A Causal Inference ApproachSemantic proximity Read

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