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
| Autor: | François Riglet, France Mentre, Christine Veyrat-Follet, Julie Bertrand |
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| Rok vydání: | 2020 |
| Předmět: |
Male
Time Factors Computer science Bayesian probability Posterior probability Pharmaceutical Science Context (language use) Risk Assessment 030226 pharmacology & pharmacy 03 medical and health sciences 0302 clinical medicine Predictive Value of Tests Risk Factors Statistics Credible interval Humans Computer Simulation Neoplasm Metastasis Event (probability theory) Models Statistical business.industry Uncertainty Prostatic Neoplasms Bayes Theorem Prostate-Specific Antigen Prognosis NONMEM Nonlinear Dynamics 030220 oncology & carcinogenesis Biomarker (medicine) Kallikreins Personalized medicine business Software |
| Zdroj: | The AAPS Journal. 22 |
| ISSN: | 1550-7416 |
| DOI: | 10.1208/s12248-019-0388-9 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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