Title of Course 1

Bayesian Joint Models for Longitudinal and Time-to-Event Data using jmpost in R

Title of Course 3

Full-Day/Half-Day

Course Instructor: Insert Name

General Outline and Main Topics

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Learning Objectives

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Tentative Structure

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Appeal to Attendees

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Attendees Expertise and Technical Requirements

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Full-Day

Course Instructor: D. Sabanés Bové, F. Mercier

General Outline and Main Topics

This short course facilitates the implementation of Bayesian joint NLME-OS (Overall Survival) models, enabling researchers to gain deeper insights into disease progression and treatment effects. By equipping participants with the skills to implement these advanced models, this training will empower them to conduct more sophisticated analyses, leading to more informed decision-making in clinical development.

Learning Objectives

In particular, participants will understand the basic concepts of joint models, apply them to biomedical applications using the R package jmpost in practice, and interpret the results

Tentative Structure

• Bayesian Nonlinear Mixed-Effect (NLME) Models: This section will introduce the principles of Bayesian NLME modeling, using illustrative examples such as tumor growth inhibition (TGI) models. Participants will learn how to specify, fit, and interpret these models within a Bayesian framework.
• Bayesian Survival Models: We will delve into Bayesian approaches to survival analysis, focusing on practical examples like Weibull Proportional Hazards (PH) models. This will cover essential concepts of survival data analysis and their Bayesian implementation.
• Sequential (Two-Stage) Modeling of TGI and Overall Survival: This module will explore a common approach to linking longitudinal and survival outcomes, demonstrating its strengths and limitations. Joint Modeling of TGI and Overall Survival Processes: The core of the course, this section will provide a deep dive into the theory and application of joint models. Participants will learn how to simultaneously model tumor growth inhibition and overall survival, accounting for their interdependence.
• Practical Implementation with jmpost: The course will introduce the jmpost R package, highlighting its intuitive R syntax and its capacity to fit a wide range of joint TGI-OS models. Hands-on exercises will focus on data preparation, model specification, execution, and interpretation of results.
• Leveraging Publicly Available Data for Decision-Making: Publicly available data from Phase 3 oncology clinical trials will be extensively used throughout the course to support the implementation of these models and demonstrate their utility in real-world decision-making. Detailed guidance on handling input data and interpreting model outputs for actionable insights will be provided

Appeal to Attendees

We will assume familiarity with the basic concepts of Bayesian  inference, as well as survival analysis and mixed effects models.  Participants should be comfortable working with R.

Attendees Expertise and Technical Requirements

We will assume familiarity with the basic concepts of Bayesian inference, as well as survival analysis and mixed effects models. Participants should be comfortable working with R.

Full-Day/Half-Day

Course Instructor: Insert Name

General Outline and Main Topics

Text

Learning Objectives

Text

Tentative Structure

Text

Appeal to Attendees

text

Attendees Expertise and Technical Requirements

Text