Journal Article

Combining biomarkers to optimize patient treatment recommendations

Huang, Ying, Kang, Chaeryon, Janes, Holly

2014

Overview

Markers that predict treatment effect have the potential to improve patient outcomes. For example, the OncotypeDX® RecurrenceScore® has some ability to predict the benefit of adjuvant chemotherapy over and above hormone therapy for the treatment of estrogen‐receptor‐positive breast cancer, facilitating the provision of chemotherapy to women most likely to benefit from it. Given that the score was originally developed for predicting outcome given hormone therapy alone, it is of interest to develop alternative combinations of the genes comprising the score that are optimized for treatment selection. However, most methodology for combining markers is useful when predicting outcome under a single treatment. We propose a method for combining markers for treatment selection which requires modeling the treatment effect as a function of markers. Multiple models of treatment effect are fit iteratively by upweighting or “boosting” subjects potentially misclassified according to treatment benefit at the previous stage. The boosting approach is compared to existing methods in a simulation study based on the change in expected outcome under marker‐based treatment. The approach improves upon methods in some settings and has comparable performance in others. Our simulation study also provides insights as to the relative merits of the existing methods. Application of the boosting approach to the breast cancer data, using scaled versions of the original markers, produces marker combinations that may have improved performance for treatment selection.

• Publisher: Blackwell Publishers,Blackwell Publishing Ltd,International Biometric Society
• Subject: Adjuvant chemotherapy / Biomarker / Biomarkers / Biomarkers, Tumor - blood / Biometrics / biometry / Biometry - methods / Boosting / Breast cancer / breast neoplasms / Breast Neoplasms - blood / Breast Neoplasms - mortality / Breast Neoplasms - therapy / Chemotherapy / Data Interpretation, Statistical / DISCUSSION PAPER / Disease models / Endocrine therapy / Estimation methods / Estimators / Female / genes / hormone replacement therapy / Humans / Logistic regression / Logistics / Male / Maximum likelihood estimation / Model mis-specification / Outcome Assessment (Health Care) - methods / patients / prediction / Prevalence / Prognosis / Reproducibility of Results / Risk Assessment - methods / Sensitivity and Specificity / Simulations / Survival Rate / Treatment selection / women