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Abstract
Breast cancer is the most commonly diagnosed cancer within women. A great amount of research has focused on discovering and evaluating predictive biomarkers. In our research, we investigate the interaction between a biomarker and treatment effects(true , which is the decrease in the population event rate under marker-based treatment versus a standard of care)based on the assumption of Cox regression model, and then we conduct a simulation to calculate the estimated under the range of ICC from 0 to 1. We plot the curve of estimated vs. ICC under four different settings. Then we conduct a random effects simulation for the biomarker Ki67, and get the ICC of biomarker Ki67. We conclude that the biomarker is better to detect the treatment effect when the ICC value is greater. We could get the true value of risk rate decrease under marker-based treatment of particular biomarker if we know the estimated value and the ICC of the biomarker in experiments. Our study is informative to evaluate the predictive biomarker detection of treatment effects in cancer.