activity
20122023
most citedLogistic regression models for patient-level prediction based on massive observational data: Do we need all data?

20 citations · 26 across the 8 of their papers we have counts for

collaborators

8 papers

cs.AI2023★ 1 cited

An Open-Source Knowledge Graph Ecosystem for the Life Sciences

Tiffany J. Callahan, Ignacio J. Tripodi, Adrianne L. Stefanski +29

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these…

stat.AP2023

Quantitative bias analysis for outcome phenotype error correction in comparative effect estimation: an empirical and synthetic evaluation

James Weaver, Patrick B. Ryan, Victoria Y. Strauss +3

Outcome phenotype measurement error is rarely corrected in comparative effect estimation studies in observational pharmacoepidemiology. Quantitative bias analysis (QBA) is a miscla…

stat.ME2023

Bayesian Safety Surveillance with Adaptive Bias Correction

Fan Bu, Martijn J. Schuemie, Akihiko Nishimura +7

Post-market safety surveillance is an integral part of mass vaccination programs. Typically relying on sequential analysis of real-world health data as they accrue, safety surveill…

cs.DB2022★ 1 cited

Ontologizing Health Systems Data at Scale: Making Translational Discovery a Reality

Tiffany J. Callahan, Adrianne L. Stefanski, Jordan M. Wyrwa +29

Background: Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate all the resources needed for dee…

stat.ME2020

A standardized framework for risk-based assessment of treatment effect heterogeneity in observational healthcare databases

Alexandros Rekkas, David van Klaveren, Patrick B. Ryan +3

The Predictive Approaches to Treatment Effect Heterogeneity statement focused on baseline risk as a robust predictor of treatment effect and provided guidance on risk-based assessm…

stat.AP2020★ 20 cited

Logistic regression models for patient-level prediction based on massive observational data: Do we need all data?

Luis H. John, Jan A. Kors, Jenna M. Reps +2

Objective: Provide guidance on sample size considerations for developing predictive models by empirically establishing the adequate sample size, which balances the competing object…