ArticleCPT: pharmacometrics & systems pharmacology2022
Finding the right hazard function for time-to-event modeling: A tutorial and Shiny application.
Article in CPT: pharmacometrics & systems pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Pharmacokinetic Modeling and Model-Based Hypothesis Generation for Dose Optimization of Clonidine in Neonates With Neonatal Opioid Withdrawal Syndrome.Clinical pharmacology and therapeutics · 2025Trial
- Reproducibility in pharmacometrics applied in a phase III trial of BCG-vaccination for COVID-19.Scientific reports · 2023Trial
- Long-Term Survival Heterogeneity Across PD-L1-Defined Subgroups in Pembrolizumab-Treated Non-Small-Cell Lung Cancer: A Model-Based Analysis.Pharmaceutics · 2026Article
- AI-Powered Lesion-Level Tumor Growth Inhibition Modeling Improves Model Stability and Prognostic Association With PFS.CPT: pharmacometrics & systems pharmacology · 2026Article
- Infection risk of peripheral intravenous catheters: meta-synthesis of 18 prospective studies with 14,606 catheters.Antimicrobial resistance and infection control · 2025Article
- Relationship between Dose, Factor IX Activity Levels and Bleeding Probability for rIX-FP Prophylaxis in Hemophilia B: A Repeated Time-to-Event Analysis.Clinical pharmacology and therapeutics · 2025Article
- Use of PopPK and E-R Analyses toward Explaining Causal Link Between ADAMTS13 in Recombinant vs. Plasma-Based Therapies and Clinical Effects in cTTP.Clinical pharmacology and therapeutics · 2025Article
- Evaluating Semi-Markov Processes and Other Epidemiological Time-to-Event Models by Computing Disease Sojourn Density as Partial Differential Equations.Medical decision making : an international journal of the Society for Medical Decision Making · 2025Article
- Time-to-Event Modeling for Survival Prediction of Osimertinib as the First- and Second-Line Therapy.Journal of clinical medicine · 2025Review
- Model-Informed Dose Optimization of Pazopanib in Real-World Patients with Cancer.Clinical pharmacokinetics · 2025Article
- Tutorial on Conditional Simulations With a Tumor Size-Overall Survival Model to Support Oncology Drug Development.CPT: pharmacometrics & systems pharmacology · 2025Article
- Assessment of NSCLC disease burden: A survival model-based meta-analysis study.Computational and structural biotechnology journal · 2024Article
- A transformer model for cause-specific hazard prediction.BMC bioinformatics · 2024Article
- Risk Factors for COVID-19 and Respiratory Tract Infections during the Coronavirus Pandemic.Vaccines · 2024Article
- Finding the right hazard function for time-to-event modeling: A tutorial and Shiny application.CPT: pharmacometrics & systems pharmacology · 2022Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parametric time-to-event analysis is an important pharmacometric method to predict the probability of an event up until a certain time as a function of covariates and/or drug exposure. Modeling is performed at the level of the hazard function describing the instantaneous rate of an event occurring at that timepoint. We give an overview of the parametric time-to-event analysis starting with graphical exploration by Kaplan-Meier plotting for the event data including censoring and nonparametric hazard estimators such as the kernel-based visual hazard comparison for the underlying hazard. The most common hazard functions including the exponential, Gompertz, Weibull, log-normal, log-logistic, and circadian functions are described in detail. A Shiny application was developed to graphically guide the modeler which of the most common hazard functions presents a similar shape compared to the data in order to guide which hazard functions to test in the parametric time-to-event analysis. For the chosen hazard function(s), the Shiny application can additionally be used to explore corresponding parameter values to inform on suitable initial estimates for parametric modeling as well as on possible covariate or treatment relationships to certain parameters. Moreover, it can be used for the dissemination of results as well as communication, training, and workshops on time-to-event analysis. By guiding the modeler on which functions and what parameter values to test and compare as well as to assist in dissemination, the Shiny application developed here greatly supports the modeler in complicated parametric time-to-event modeling.
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