Estimating mono- and bi-phasic regression parameters using a mixture piecewise linear Bayesian hierarchical model

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Estimating mono- and bi-phasic regression parameters using a mixture piecewise linear Bayesian hierarchical model

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Title: Estimating mono- and bi-phasic regression parameters using a mixture piecewise linear Bayesian hierarchical model
Author: Zhao, Rui; Catalano, Paul; DeGruttola, Victor G.; Michor, Franziska

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Citation: Zhao, Rui, Paul Catalano, Victor G. DeGruttola, and Franziska Michor. 2017. “Estimating mono- and bi-phasic regression parameters using a mixture piecewise linear Bayesian hierarchical model.” PLoS ONE 12 (7): e0180756. doi:10.1371/journal.pone.0180756. http://dx.doi.org/10.1371/journal.pone.0180756.
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Abstract: The dynamics of tumor burden, secreted proteins or other biomarkers over time, is often used to evaluate the effectiveness of therapy and to predict outcomes for patients. Many methods have been proposed to investigate longitudinal trends to better characterize patients and to understand disease progression. However, most approaches assume a homogeneous patient population and a uniform response trajectory over time and across patients. Here, we present a mixture piecewise linear Bayesian hierarchical model, which takes into account both population heterogeneity and nonlinear relationships between biomarkers and time. Simulation results show that our method was able to classify subjects according to their patterns of treatment response with greater than 80% accuracy in the three scenarios tested. We then applied our model to a large randomized controlled phase III clinical trial of multiple myeloma patients. Analysis results suggest that the longitudinal tumor burden trajectories in multiple myeloma patients are heterogeneous and nonlinear, even among patients assigned to the same treatment cohort. In addition, between cohorts, there are distinct differences in terms of the regression parameters and the distributions among categories in the mixture. Those results imply that longitudinal data from clinical trials may harbor unobserved subgroups and nonlinear relationships; accounting for both may be important for analyzing longitudinal data.
Published Version: doi:10.1371/journal.pone.0180756
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516991/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:34375018
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