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Published on: January 22, 2011
A multistate model for early decision-making in oncology
Ulrich Beyer1, David Dejardin1, Matthias Meller1
1Department of Biostatistics, MDBB 663, F. Hoffmann-La Roche Ltd., Basel, Switzerland.
Abstract:
The development of oncology drugs progresses through multiple phases, where after each phase, a decision is made about whether to move a molecule forward. Early phase efficacy decisions are often made on the basis of single-arm studies based on a set of rules to define whether the tumor improves ("responds"), remains stable, or progresses (response evaluation criteria in solid tumors [RECIST]). These decision rules are implicitly assuming some form of surrogacy between tumor response and long-term endpoints like progression-free survival (PFS) or overall survival (OS). With the emergence of new therapies, for which the link between RECIST tumor response and long-term endpoints is either not accessible yet, or the link is weaker than with classical chemotherapies, tumor response-based rules may not be optimal. In this paper, we explore the use of a multistate model for decision-making based on single-arm early phase trials. The multistate model allows to account for more information than the simple RECIST response status, namely, the time to get to response, the duration of response, the PFS time, and time to death. We propose to base the decision on efficacy on the OS hazard ratio (HR) comparing historical control to data from the experimental treatment, with the latter predicted from a multistate model based on early phase data with limited survival follow-up. Using two case studies, we illustrate feasibility of the estimation of such an OS HR. We argue that, in the presence of limited follow-up and small sample size, and making realistic assumptions within the multistate model, the OS prediction is acceptable and may lead to better early decisions within the development of a drug.
Insights
This study introduces a multistate model for oncology drug development, improving early efficacy decisions by integrating more patient data than traditional RECIST criteria. The model predicts overall survival (OS) hazard ratios from early trial data, enhancing decision-making for novel therapies.
Area of Science:
- Clinical Pharmacology
- Biostatistics
- Oncology Drug Development
Background:
- Oncology drug development relies on phase-based decisions, often using Response Evaluation Criteria in Solid Tumors (RECIST) from single-arm trials.
- RECIST implicitly assumes tumor response surrogacy for long-term outcomes like progression-free survival (PFS) and overall survival (OS).
- New therapies may have weaker or inaccessible links between RECIST response and long-term endpoints, challenging traditional decision-making.
Purpose of the Study:
- To explore a multistate model for enhanced decision-making in early-phase single-arm oncology trials.
- To utilize richer patient data beyond RECIST status, including time to response, duration of response, PFS, and time to death.
- To predict overall survival (OS) hazard ratios (HR) for experimental treatments using early phase data with limited survival follow-up.
Main Methods:
- Development and application of a multistate model to analyze early phase oncology trial data.
- Prediction of OS HR by comparing historical controls to experimental treatment data derived from the multistate model.
- Validation using two case studies to demonstrate the feasibility of OS HR estimation.
Main Results:
- The multistate model successfully incorporates detailed temporal patient data, offering more information than standard RECIST criteria.
- Feasibility of estimating OS HR was demonstrated through case studies, showing the model's practical application.
- The model provides acceptable OS predictions even with limited follow-up and small sample sizes.
Conclusions:
- The proposed multistate model offers a more robust approach for early efficacy decisions in oncology drug development.
- This method enhances decision-making by providing reliable OS predictions, particularly for novel therapies with uncertain RECIST-outcome links.
- The approach supports better-informed decisions early in the drug development pipeline, even with limited patient data.
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