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Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
Published on: July 25, 2020
Randomized phase III clinical trial designs for targeted agents
Antje Hoering1, Mike Leblanc, John J Crowley
1Fred Hutchinson Cancer Research Center, Seattle, Washington, USA. antjeh@crab.org
Purpose:
Cancer therapies with mechanisms of action which are very different from the more conventional chemotherapies are now being developed. In this article, we investigate the performance of several phase III clinical trial designs, both for testing the overall efficacy of a targeted agent and for testing its efficacy in a subgroup of patients with a tumor marker present. We study different designs and different underlying scenarios assuming continuous markers, and assess the trade-off between the number of patients on the study and the effectiveness of treatment in the subgroup of marker-positive patients.
Experimental Design:
We investigate binary outcomes and use simulation studies to determine sample size and power for the different designs and the various scenarios. We also simulate marker prevalence and marker misclassification and evaluate their effect on power and sample size.
Results:
In general, a targeted design which randomizes patients with the appropriate marker status performs the best in all scenarios with an underlying true predictive marker. Randomizing all patients regardless of their marker values performs as well as or better in most cases than a clinical trial that randomizes the patient to a treatment strategy based on marker value versus standard of care.
Conclusion:
If there is the possibility that the new treatment helps marker-negative patients, or that the cutpoint determining marker status has not been well established and the marker prevalence is large enough, we recommend randomizing all patients regardless of marker values, but using a design such that both the overall and the targeted subgroup hypothesis can be tested.
Insights
For targeted cancer therapies, a design randomizing patients with the specific tumor marker offers the best performance. However, randomizing all patients may be preferable if the new treatment benefits marker-negative patients or if the marker is not well-established.
Area of Science:
- Clinical trial design
- Biostatistics
- Oncology
Background:
- Development of novel cancer therapies with distinct mechanisms of action from conventional chemotherapy.
- Need for robust clinical trial designs to evaluate targeted agents, especially in specific patient subgroups.
- Importance of tumor markers in identifying patient populations likely to benefit from targeted treatments.
Purpose of the Study:
- To investigate the performance of various phase III clinical trial designs for targeted cancer therapies.
- To compare designs for testing overall efficacy versus efficacy in marker-positive subgroups.
- To assess the trade-off between study size and treatment effectiveness in marker-positive patients under different scenarios.
Main Methods:
- Simulation studies were employed to evaluate sample size and statistical power for different trial designs.
- Binary outcomes were analyzed, considering continuous markers and various underlying scenarios.
- Marker prevalence and misclassification effects on power and sample size were simulated.
Main Results:
- Targeted designs, randomizing patients with the specific marker, demonstrated superior performance when a true predictive marker exists.
- Randomizing all patients, irrespective of marker status, often performed comparably or better than marker-based randomization strategies.
- The performance of different designs was evaluated against the standard of care in simulated clinical trials.
Conclusions:
- Randomizing all patients is recommended if the new treatment may benefit marker-negative patients or if marker status is uncertain.
- Designs allowing testing of both overall and targeted subgroup hypotheses are advisable in such cases.
- Careful consideration of marker characteristics and potential benefits across patient groups is crucial for optimal trial design.
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