ArticleClinical and translational science2018
Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time-to-Event Analysis.
Article in Clinical and translational science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Early predictions of response and survival from a tumor dynamics model in patients with recurrent, metastatic head and neck squamous cell carcinoma treated with immunotherapy.CPT: pharmacometrics & systems pharmacology · 2021Trial
- Improving arrhythmic risk prediction using cardiac magnetic resonance within deep learning in ischemic heart disease.NPJ cardiovascular health · 2026Article
- Current State-of-the-Art and Future Advancements of Model-Based Meta-Analysis.CPT: pharmacometrics & systems pharmacology · 2026Article
- Comparison of oblique random survival forest, random survival forest, and statistical models for time-to-event data using simulation study.Scientific reports · 2025Article
- Opportunities for AI-based Model-informed Drug Development: A Comparative Analysis of NONMEM and AI-based Models for Population Pharmacokinetic Prediction.The AAPS journal · 2025Article
- Dynamic survival analysis via a landmarking-gradient boosting approach and its application to kidney transplant data.BMC medical informatics and decision making · 2025Article
- Application of Machine Learning for Predicting Progression-Free and Overall Survival in Patients With Renal Cell Carcinoma.Clinical and translational science · 2025Article
- Clinical Pharmacology of Cytokine Release Syndrome with T-Cell-Engaging Bispecific Antibodies: Current Insights and Drug Development Strategies.Clinical cancer research : an official journal of the American Association for Cancer Research · 2025Review
- Article
- Comparison of sequential and joint nonlinear mixed effects modeling of tumor kinetics and survival following Durvalumab treatment in patients with metastatic urothelial carcinoma.Journal of pharmacokinetics and pharmacodynamics · 2023Article
- Performance of Cox proportional hazard models on recovering the ground truth of confounded exposure-response relationships for large-molecule oncology drugs.CPT: pharmacometrics & systems pharmacology · 2022Article
- A novel analytical framework for risk stratification of real-world data using machine learning: A small cell lung cancer study.Clinical and translational science · 2022Article
- Adoption of Machine Learning in Pharmacometrics: An Overview of Recent Implementations and Their Considerations.Pharmaceutics · 2022Article
- Decoding kinase-adverse event associations for small molecule kinase inhibitors.Nature communications · 2022Article
- A scoping methodological review of simulation studies comparing statistical and machine learning approaches to risk prediction for time-to-event data.Diagnostic and prognostic research · 2022Article
- Neural Networks for Survival Prediction in Medicine Using Prognostic Factors: A Review and Critical Appraisal.Computational and mathematical methods in medicine · 2022Review
- Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker.BMC medical research methodology · 2021Article
- Application of Deep Neural Networks as a Prescreening Tool to Assign Individualized Absorption Models in Pharmacokinetic Analysis.Pharmaceutics · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time-to-event data synthesized under various preset scenarios, i.e., with linear vs. nonlinear and dependent vs. independent predictors in the proportional hazard function, or with high-dimensional data featured by a large number of predictor variables. Our results showed that ML-based methods outperformed the Cox model in prediction performance as assessed by concordance index and in identifying the preset influential variables for high-dimensional data. The prediction performances of ML-based methods are also less sensitive to data size and censoring rates than the Cox regression model. In conclusion, ML-based methods provide a powerful tool for time-to-event analysis, with a built-in capacity for high-dimensional data and better performance when the predictor variables assume nonlinear relationships in the hazard function.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.