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New Model May Improve Patient Selection in Breast Cancer Trials

Jason Harris
Published: Friday, Feb 02, 2018

Lajos Pusztai, MD, DPhil
Lajos Pusztai, MD, DPhil
Investigators at Yale Cancer Center believe that a statistical model that combines multiple variables such as tumor size and lymph-node status into a single score can improve patient selection for breast cancer clinical trials and produce stronger results.

With a sample size of 800, the respective power associated with scenario 1 was 0.87, compared with 0.84 in scenario 2, 0.80 in scenario 3, and 0.76 in scenario 4. Investigators emphasized these changes in power occurred even though all patients met eligibility criteria based on tumor size and nodal status.

However, the power of the trial increases when eligibility is defined as >40% residual risk. An 800-patient randomized clinical trial retains a power of 82% even when 70% of patients have T2/N0 disease.

In scenario 1, a trial would have an 82% power to detect an HR of 0.70 with a sample size of 600 if eligibility is defined as a minimum 10-year residual risk of 50%, whereas a trial with the same sample size using combinations of nodal size and tumor size for eligibility would have a power of 0.77.

“This approach guarantees that the statistical power of the study is adequate to demonstrate if the new treatment is really effective or not. We also can expose fewer patients to the side effects of the new treatment. That's good for the patients,” Pusztai said. “We can select patients who really require clinical trials because their outcome with current treatments is less than optimal.”
Wei W, Kurita T, Hess KR, et al. Comparison of residual risk–based eligibility vs tumor size and nodal status for power estimates in adjuvant trials of breast cancer therapies [published online January 25, 2018]. JAMA Oncol. doi:10.1001/jamaoncol.2017.5092.

 



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