ArticleThe AAPS journal2020
Bayesian Individual Dynamic Predictions with Uncertainty of Longitudinal Biomarkers and Risks of Survival Events in a Joint Modelling Framework: a Comparison Between Stan, Monolix, and NONMEM.
Article in The AAPS journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Tutorial on Conditional Simulations With a Tumor Size-Overall Survival Model to Support Oncology Drug Development.CPT: pharmacometrics & systems pharmacology · 2025Article
- A sample size analysis of a mathematical model of longitudinal tumor volume and progression-free survival for Bayesian individual dynamic predictions in recurrent high-grade glioma.CPT: pharmacometrics & systems pharmacology · 2025Article
- Monitoring peripheral blood data supports the prediction of immunotherapy response in advanced non-small cell lung cancer based on real-world data.Cancer immunology, immunotherapy : CII · 2025Article
- Joint modeling of monocyte HLA-DR expression trajectories predicts 28-day mortality in severe SARS-CoV-2 patients.CPT: pharmacometrics & systems pharmacology · 2024Article
- Bayesian estimation in NONMEM.CPT: pharmacometrics & systems pharmacology · 2024Article
- Joint modeling under competing risks: Application to survival prediction in patients admitted in Intensive Care Unit for sepsis with daily Sequential Organ Failure Assessment score assessments.CPT: pharmacometrics & systems pharmacology · 2022Article
- A workflow for the joint modeling of longitudinal and event data in the development of therapeutics: Tools, statistical methods, and diagnostics.CPT: pharmacometrics & systems pharmacology · 2022Article
- Multistate model for pharmacometric analyses of overall survival in HER2-negative breast cancer patients treated with docetaxel.CPT: pharmacometrics & systems pharmacology · 2021Article
- Longitudinal Tumor Size and Neutrophil-to-Lymphocyte Ratio Are Prognostic Biomarkers for Overall Survival in Patients With Advanced Non-Small Cell Lung Cancer Treated With Durvalumab.CPT: pharmacometrics & systems pharmacology · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Given a joint model and its parameters, Bayesian individual dynamic prediction (IDP) of biomarkers and risk of event can be performed for new patients at different landmark times using observed biomarker values. The aim of the present study was to compare IDP, with uncertainty, using Stan 2.18, Monolix 2018R2 and NONMEM 7.4. Simulations of biomarker and survival were performed using a nonlinear joint model of prostate-specific antigen (PSA) kinetics and survival in metastatic prostate cancer. Several scenarios were evaluated, according to the strength of the association between PSA and survival. For various landmark times, a posteriori distribution of PSA kinetic individual parameters was estimated, given individual observations, with each software. Samples of individual parameters were drawn from the posterior distribution. Bias and imprecision of individual parameters as well as coverage of 95% credibility interval for PSA and risk of death were evaluated. All software performed equally well with small biases on individual parameters. Imprecision on individual parameters was comparable across software and showed marked improvements with increasing landmark time. In terms of coverage, results were also comparable and all software were able to well predict PSA kinetics and survival. As for computing time, Stan was faster than Monolix and NONMEM to obtain individual parameters. Stan 2.18, Monolix 2018R2 and NONMEM 7.4 are able to characterize IDP of biomarkers and risk of event in a nonlinear joint modelling framework with correct uncertainty and hence could be used in the context of individualized medicine.
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