TY - JOUR
T1 - Adaptive prediction model in prospective molecular signature-based clinical studies
AU - Xiao, Guanghua
AU - Ma, Shuangge
AU - Minna, John
AU - Xie, Yang
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84893511255&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893511255&partnerID=8YFLogxK
U2 - 10.1158/1078-0432.CCR-13-2127
DO - 10.1158/1078-0432.CCR-13-2127
M3 - Article
C2 - 24323903
AN - SCOPUS:84893511255
SN - 1078-0432
VL - 20
SP - 531
EP - 539
JO - Clinical Cancer Research
JF - Clinical Cancer Research
IS - 3
ER -