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. 2014 Sep;70(3):695-707.
doi: 10.1111/biom.12191. Epub 2014 May 30.

Combining biomarkers to optimize patient treatment recommendations

Chaeryon Kang  1 , Holly Janes  1 , Ying Huang  1
Affiliations

Combining biomarkers to optimize patient treatment recommendations

Chaeryon Kang et al. Biometrics. 2014 Sep.

Abstract

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.

Keywords: Biomarker; Boosting; Model mis‐specification; Treatment selection.

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Figures

Figure 1
Figure 1
Distribution of the marker-specific treatment effect, Δ(Y) = P(D = 1|T = 0, Y)−P(D = 1|T = 1, Y), for each of the seven simulation scenarios. The proportion of individuals with negative treatment effects is indicated on the Y-axis, and θ = [P{D = 1|T = 1, ϕ(Y) = 1} − P{D = 1|T = 0, ϕ(Y) = 1}] × P{ϕ(Y) = 1}, measuring the impact of marker-based treatment assignment, is shown.
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