Tumor growth and overall survival modeling to support decision making in phase Ib/II trials: A comparison of the joint and two-stage approaches
Contact us if these videos are not relevant.
Contact us if these videos are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.Tumor growth inhibition (TGI) metrics like K<sub>G</sub> can predict overall survival (OS) in immunotherapy trials. Joint modeling did not outperform two-stage approaches, suggesting K<sub>G</sub> supports early decisions with sufficient patient numbers and follow-up.
Area Of Science
- Clinical trial methodology
- Biostatistics
- Pharmacometrics
Background
- Model-based tumor growth inhibition (TGI) metrics are emerging as predictors of overall survival (OS) in immunotherapy trials.
- Understanding the comparative performance of two-stage versus joint modeling approaches for leveraging early-phase data is crucial for timely clinical decisions.
Purpose Of The Study
- To evaluate the operating characteristics of TGI metrics using joint modeling compared to a two-stage approach.
- To assess the utility of TGI metrics for early decision-making in Phase Ib/II immunotherapy trials.
Main Methods
- Utilized TGI and OS data from the IMpower150 trial (atezolizumab for non-small cell lung cancer).
- Simulated Phase Ib/II trials with varying patient numbers (15-40 per arm) and follow-up durations (6-24 weeks).
- Compared joint modeling against a two-stage approach for predicting OS using TGI metrics.
Main Results
- Joint modeling did not demonstrate superior operating characteristics compared to the two-stage approach.
- Both methods yielded similar performance across all simulated trial scenarios.
- The K<sub>G</sub> geometric mean ratio is suggested for early decision-making if at least 30 patients per arm are included and followed for 12 weeks.
Conclusions
- The choice between two-stage and joint modeling may not significantly impact the operating characteristics of TGI metrics for early decision-making in this context.
- The K<sub>G</sub> metric shows promise as an early endpoint, with specific recommendations for sample size and follow-up duration to ensure reliability.
Contact us if these videos are not relevant.
Contact us if these videos are not relevant.
Related Concept Videos
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...