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Oncology Clinical Trial Design Planning Based on a Multistate Model That Jointly Models Progression-Free and Overall
Alexandra Erdmann1, Jan Beyersmann1, Kaspar Rufibach2
1Institute of Statistics, Ulm University, Ulm, Germany.
Biometrical Journal. Biometrische Zeitschrift
|December 17, 2024
Summary
Standard oncology clinical trial planning often uses simplifying assumptions for overall survival (OS) and progression-free survival (PFS). This study demonstrates a survival multistate model for jointly analyzing OS and PFS, revealing these assumptions often fail and can lead to underpowered trials.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Oncology Research
Background:
- Traditional oncology clinical trial planning often assumes proportional hazards and exponential distributions for time-to-event endpoints like overall survival (OS) and progression-free survival (PFS).
- These simplifying assumptions may not accurately reflect the complex relationship between multiple dependent endpoints in clinical trials.
Purpose of the Study:
- To introduce and apply a survival multistate model for jointly analyzing OS and PFS in oncology clinical trials.
- To demonstrate how this model can improve the planning and design of clinical trials by accounting for endpoint dependencies and non-proportional hazards.
Main Methods:
- Utilized a survival multistate model, a stochastic process approach, to jointly model the dependency between OS and PFS.
- Employed simulation studies based on the multistate model to address clinical trial design questions, including coprimary endpoints and group-sequential designs.
- Developed an R package available on CRAN for implementing the proposed methodology.
Main Results:
- Found that neither exponential distribution nor proportional hazards typically hold simultaneously for both OS and PFS when modeled jointly.
- Demonstrated that non-proportional hazards for at least one endpoint are a natural consequence of the dependency between OS and PFS.
- Simulation results indicated that standard simplifying approaches can lead to underpowered or overpowered clinical trials.
Conclusions:
- The survival multistate model offers a more robust framework for jointly modeling dependent endpoints like OS and PFS in oncology trials.
- This approach allows for more accurate clinical trial design and planning by naturally incorporating endpoint dependencies and non-proportional hazards.
- The methodology is generalizable to more complex trial designs, additional endpoints, and other therapeutic areas.
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