American Association for Cancer Research
10780432ccr132127-sup-ccr-13-2127tab1-2fig1.pdf (47.02 kB)

Supplementary Tables 1 - 2, Figure 1 from Adaptive Prediction Model in Prospective Molecular Signature–Based Clinical Studies

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journal contribution
posted on 2023-03-31, 17:29 authored by Guanghua Xiao, Shuangge Ma, John Minna, Yang Xie

PDF file - 47K, Table S1. Simulation settings, Table S2. Biological verification of genes with high adaptive scores, Figure S1. ROC curves in the simulation study.



Use of molecular profiles and clinical information can help predict which treatment would give the best outcome and survival for each individual patient, and thus guide optimal therapy, which offers great promise for the future of clinical trials and practice. High prediction accuracy is essential for selecting the best treatment plan. The gold standard for evaluating the prediction models is prospective clinical studies, in which patients are enrolled sequentially. However, there is no statistical method using this sequential feature to adapt the prediction model to the current patient cohort. In this article, we propose a reweighted random forest (RWRF) model, which updates the weight of each decision tree whenever additional patient information is available, to account for the potential heterogeneity between training and testing data. A simulation study and a lung cancer example are used to show that the proposed method can adapt the prediction model to current patients' characteristics, and, therefore, can improve prediction accuracy significantly. We also show that the proposed method can identify important and consistent predictive variables. Compared with rebuilding the prediction model, the RWRF updates a well-tested model gradually, and all of the adaptive procedure/parameters used in the RWRF model are prespecified before patient recruitment, which are important practical advantages for prospective clinical studies. Clin Cancer Res; 20(3); 531–9. ©2013 AACR.

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