Article

Virtual Trials: Causally-validated treatment effects efficiently learned from an observational cancer registry

2021-06-18

Abstract excerpt

Randomized controlled trials (RCTs) offer a clear causal interpretation of treatment effects, but are inefficient in terms of information gain per patient. Moreover, because they are intended to test cohort-level effects, RCTs rarely provide information to support precision medicine, which strives to choose the best treatment for an individual patient. If causal information could be efficiently extracted from wide...

Topics

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

Identifiers and source

Literature Corpus work: a53b33a7-d66b-5148-ba39-bf5a44b2a35d
DOI: 10.1101/2021.06.12.21258409

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Virtual Trials: Causally-validated treatment effects efficiently learned from an observational cancer registryDOI 10.1101/2021.06.12.21258409Read

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

The Virtual Biotech: A Multi-Agent AI Framework for Therapeutic Discovery and DevelopmentSemantic proximity Read

Data reuse enables cost-efficient randomized trials of medical AI modelsSemantic proximity Read

A Deep Learning Framework for Causal Inference in Clinical Trial Design: The CURE AI Large Clinicogenomic Foundation ModelSemantic proximity Read

A Meta-Learner Framework to Estimate Individualized Treatment Effects for Survival OutcomesSemantic proximity Read

Temporal dynamics of radiotherapy and chemotherapy response in lower-grade gliomas using causal machine learningSemantic proximity Read

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

RCT-Twin-GAN Generates Digital Twins of Randomized Control Trials Adapted to Real-world Patients to Enhance their Inference and ApplicationSemantic proximity Read

Trustworthy personalized treatment selection: causal effect-trees and calibration in perioperative medicineSemantic proximity Read

In Silico Trial Simulation with Artificial Intelligence-Generated Synthetic Control Cohorts Reproduces Results of a Randomized Controlled Trial in Acute Myeloid LeukemiaSemantic proximity Read

Replicate Engineered Virtual Patient Populations as Surrogates for Real Patient-Level DataSemantic proximity Read

An Evidenced-Based Prior for Estimating the Treatment Effect of Phase III Randomized Trials in OncologySemantic proximity Read