ArticleThe AAPS journal2015
Nonlinear Mixed-effect Models for Prostate-specific Antigen Kinetics and Link with Survival in the Context of Metastatic Prostate Cancer: A Comparison by Simulation of Two-stage and Joint Approaches.
Article in The AAPS journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
34 citing papers in PubMed.
- Joint modeling of tumor dynamics and progression-free survival in advanced breast cancer: Leveraging data from amcenestrant early phase I-II trials.CPT: pharmacometrics & systems pharmacology · 2024Trial
- Modeling the Relationship Between Exposure to Abiraterone and Prostate-Specific Antigen Dynamics in Patients with Metastatic Castration-Resistant Prostate Cancer.Clinical pharmacokinetics · 2017Trial
- Models for change in tumour size, appearance of new lesions and survival probability in patients with advanced epithelial ovarian cancer.British journal of clinical pharmacology · 2016Trial
- PK-PD modeling of individual lesion FDG-PET response to predict overall survival in patients with sunitinib-treated gastrointestinal stromal tumor.CPT: pharmacometrics & systems pharmacology · 2016Trial
- Modelling longitudinal and time-to-event data: a phase IV simulation study comparing R package implementations of joint models with time-varying Cox proportional-hazards regression, and the two-stage approach.BMC medical research methodology · 2026Article
- Novel Drug-Disease Modeling Framework for Oncology Benefit-Risk Evaluation: Application to Tusamitamab Ravtansine.CPT: pharmacometrics & systems pharmacology · 2026Article
- Measurable imaging-based changes in enhancement of intrahepatic cholangiocarcinoma after radiotherapy reflect physical mechanisms of response.NPJ systems biology and applications · 2025Article
- KELIM PSA as a Prognostic Biomarker in Castration-Resistant Prostate Cancer Treated with ARPI.Journal of clinical medicine · 2025Article
- Development of a Joint Tumor Size-Overall Survival Modeling and Simulation Framework Supporting Oncology Development Decision-Making.CPT: pharmacometrics & systems pharmacology · 2025Article
- Tutorial on Conditional Simulations With a Tumor Size-Overall Survival Model to Support Oncology Drug Development.CPT: pharmacometrics & systems pharmacology · 2025Article
- Modeling serum M-protein response for early detection of biochemical relapse in myeloma patients treated with bortezomib, lenalidomide and dexamethasone.CPT: pharmacometrics & systems pharmacology · 2024Article
- Isatuximab-dexamethasone-pomalidomide combination effects on serum M protein and PFS in myeloma: Development of a joint model using phase I/II data.CPT: pharmacometrics & systems pharmacology · 2024Article
- Joint modeling of longitudinal health-related quality of life during concurrent chemoradiotherapy period and long-term survival among patients with advanced nasopharyngeal carcinoma.Radiation oncology (London, England) · 2024Article
- Tumor growth and overall survival modeling to support decision making in phase Ib/II trials: A comparison of the joint and two-stage approaches.CPT: pharmacometrics & systems pharmacology · 2024Article
- Comparison of two-stage and joint TGI-OS modeling using data from six atezolizumab clinical studies in patients with metastatic non-small cell lung cancer.CPT: pharmacometrics & systems pharmacology · 2024Article
- 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
- Bayesian forecasting of tumor size metrics and overall survival.CPT: pharmacometrics & systems pharmacology · 2022Article
- In Vitro/In Vivo Translation of Synergistic Combination of MDM2 and MEK Inhibitors in Melanoma Using PBPK/PD Modelling: Part II.International journal of molecular sciences · 2022Article
- Joint models for dynamic prediction in localised prostate cancer: a literature review.BMC medical research methodology · 2022Review
- Joint modelling and simulation of M-protein dynamics and progression-free survival for alternative isatuximab dosing with pomalidomide/dexamethasone.British journal of clinical pharmacology · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In metastatic castration-resistant prostate cancer (mCRPC) clinical trials, the assessment of treatment efficacy essentially relies on the time to death and the kinetics of prostate-specific antigen (PSA). Joint modeling has been increasingly used to characterize the relationship between a time to event and a biomarker kinetics, but numerical difficulties often limit this approach to linear models. Here, we evaluated by simulation the capability of a new feature of the Stochastic Approximation Expectation-Maximization algorithm in Monolix to estimate the parameters of a joint model where PSA kinetics was defined by a mechanistic nonlinear mixed-effect model. The design of the study and the parameter values were inspired from one arm of a clinical trial. Increasingly high levels of association between PSA and survival were considered, and results were compared with those found using two simplified alternatives to joint model, a two-stage and a joint sequential model. We found that joint model allowed for a precise estimation of all longitudinal and survival parameters. In particular, the effect of PSA kinetics on survival could be precisely estimated, regardless of the strength of the association. In contrast, both simplified approaches led to bias on longitudinal parameters, and two-stage model systematically underestimated the effect of PSA kinetics on survival. In summary, we showed that joint model can be used to characterize the relationship between a nonlinear kinetics and survival. This opens the way for the use of more complex and physiological models to improve treatment evaluation and prediction in oncology.
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