Machine learning‐guided covariate selection for time‐to‐event models developed from a small sample of real‐world patients receiving bevacizumab treatment-Reference-Cited by-同舟云学术

Machine learning‐guided covariate selection for time‐to‐event models developed from a small sample of real‐world patients receiving bevacizumab treatment

Published:2022-08-04 Issue:10 Volume:11 Page:1328-1340
ISSN:2163-8306
Container-title:CPT: Pharmacometrics & Systems Pharmacology
language:en
Short-container-title:CPT Pharmacom & Syst Pharma

Author:

Karatza Eleni¹^ORCID,Papachristos Apostolos²^ORCID,Sivolapenko Gregory B.²^ORCID,Gonzalez Daniel¹^ORCID

Affiliation:

1. Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy The University of North Carolina at Chapel Hill Chapel Hill North Carolina USA

2. Laboratory of Pharmacokinetics, Department of Pharmacy, School of Health Sciences University of Patras Rion, Patras Greece

Publisher

Wiley

Subject

Pharmacology (medical),Modeling and Simulation

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/psp4.12848

Reference50 articles.

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2. Pharmacogenetics in Model-Based Optimization of Bevacizumab Therapy for Metastatic Colorectal Cancer

3. Prospective Validation of Candidate SNPs of VEGF/VEGFR Pathway in Metastatic Colorectal Cancer Patients Treated with First-Line FOLFIRI Plus Bevacizumab

4. Phase II Study of Bevacizumab in Combination with Sorafenib in Recurrent Glioblastoma (N0776): A North Central Cancer Treatment Group Trial

5. Prediction of overall survival or progression free survival by disease control rate at week 8 is independent of ethnicity: Western versus Chinese patients with first-line non-small cell lung cancer treated with chemotherapy with or without bevacizumab

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