|
Activity Number:
|
355
- Contributed Poster Presentations: Biopharmaceutical Section
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Biopharmaceutical Section
|
|
Abstract #306520
|
|
|
Title:
|
Tumor-Growth Modeling for Informed Go/No-Go Decisions
|
|
Author(s):
|
Wei Wei* and Daniel Zelterman and Elizabeth Garrett-Mayer
|
|
Companies:
|
Yale University School of Public Health and Yale University School of Public Health and American Society of Clinical Oncology
|
|
Keywords:
|
objective response;
RECIST;
Bayesian;
mixture;
mixed effects model
|
|
Abstract:
|
Tumor burden is regularly assessed in cancer clinical trials. However, the dynamics of tumor growth are often ignored and a binary indicator of tumor shrinkage is commonly used as the primary efficacy endpoint in early phase cancer trials. To provide more accurate measures of efficacy, we develop a Bayesian mixed-effects mixture model to estimate tumor growth trajectory in response to treatment. The tumor trajectory of patients with progressive disease is described with the use of a log linear function with a positive slope (Model 1), whereas a function with quadratic terms is used to estimate the tumor trajectory of patients who progressed after initial response (Model 2). The tumor trajectory of patients with durable response is described by a log linear function with a negative slope (Model 3). The resulting tumor growth curve is the weighted average of these three functions. The probability of assigning a patient to Model 1 or 2 provides a patient specific estimate for the risk of progression. We demonstrate that the model estimated progression risk predicts overall survival and leads to more efficient and informative designs for early phase cancer trials.
|
Authors who are presenting talks have a * after their name.
