title: A two-stage approach for dynamic prediction of time-to-event distributions.
abstract: Dynamic prediction uses longitudinal biomarkers for real-time prediction of an individual patient's prognosis. This is critical for patients with an incurable disease such as cancer. Biomarker trajectories are usually not linear, nor even monotone, and vary greatly across individuals. Therefore, it is difficult to fit them with parametric models. With this consideration, we propose an approach for dynamic prediction that does not need to model the biomarker trajectories. Instead, as a trade-off, we assume that the biomarker effects on the risk of disease recurrence are smooth functions over time. This approach turns out to be computationally easier. Simulation studies show that the proposed approach achieves stable estimation of biomarker effects over time, has good predictive performance, and is robust against model misspecification. It is a good compromise between two major approaches, namely, (i) joint modeling of longitudinal and survival data and (ii) landmark analysis. The proposed method is applied to patients with chronic myeloid leukemia. At any time following their treatment with tyrosine kinase inhibitors, longitudinally measured BCR-ABL gene expression levels are used to predict the risk of disease progression. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 77230, TX, U.S.A.
authors.forename: Xuelin
authors.identifier: 0000-0003-1192-9336
authors.initials: X
authors.lastname: Huang
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 77230, TX, U.S.A.|Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, 210009, China.
authors.forename: Fangrong
authors.initials: F
authors.lastname: Yan
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 77230, TX, U.S.A.
authors.forename: Jing
authors.initials: J
authors.lastname: Ning
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 77230, TX, U.S.A.
authors.forename: Ziding
authors.initials: Z
authors.lastname: Feng
authors.affiliation: Department of Internal Medicine, The University of Texas Health Science Center at Houston, Houston, 77230, TX, U.S.A.
authors.forename: Sangbum
authors.initials: S
authors.lastname: Choi
authors.affiliation: Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, 77230, TX, U.S.A.
authors.forename: Jorge
authors.initials: J
authors.lastname: Cortes
mesh_terms: D015415:Biomarkers / Q000032:analysis; D003933:Diagnosis; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000175:diagnosis; D011379:Prognosis; D013223:Statistics as Topic; D016019:Survival Analysis; D013997:Time Factors
chemical_list: D015415:Biomarkers
keywords: biomarker; dynamic prediction; landmark analysis; longitudinal data; survival analysis; time-dependent covariate