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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/18836

Title: Evaluation of treatment efficacy using a Bayesian mixture piecewise linear model of longitudinal biomarkers
Authors: Zhao, Lili
Feng, Dai
Neelon, Brian
Issue Date: 2015
Citation: STATISTICS IN MEDICINE, 34 (10), p. 1733-1746
Abstract: Prostate-specific antigen (PSA) is a widely used marker in clinical trials for patients with prostate cancer. We develop a mixture model to estimate longitudinal PSA trajectory in response to treatment. The model accommodates subjects responding and not responding to therapy through a mixture of two functions. A responder is described by a piecewise linear function, represented by an intercept, a PSA decline rate, a period of PSA decline, and a PSA rising rate; a nonresponder is described by an increasing linear function with an intercept and a PSA rising rate. Each trajectory is classified as a linear or a piecewise linear function with a certain probability, and the weighted average of these two functions sufficiently characterizes a variety of patterns of PSA trajectories. Furthermore, this mixture structure enables us to derive clinically useful endpoints such as a response rate and time-to-progression, as well as biologically meaningful endpoints such as a cancer cell killing fraction and tumor growth delay. We compare our model with the most commonly used dynamic model in the literature and show its advantages. Finally, we illustrate our approach using data from two multicenter prostate cancer trials. The R code used to produce the analyses reported in this paper is available on request. Copyright (c) 2015 John Wiley & Sons, Ltd.
Notes: [Zhao, Lili] Univ Michigan, Dept Biostat, Ann Arbor, MI 48109 USA. [Feng, Dai] Merck Res Lab, Biometr Res, Rahway, NJ 07033 USA. [Neelon, Brian] Med Univ S Carolina, Dept Publ Hlth Sci, Charleston, SC 29425 USA. [Buyse, Marc] Int Inst Drug Dev, Louvain La Neuve, Belgium. [Buyse, Marc] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat I BioSt, Diepenbeek, Belgium.
URI: http://hdl.handle.net/1942/18836
DOI: 10.1002/sim.6445
ISI #: 000352572200009
ISSN: 0277-6715
Category: A1
Type: Journal Contribution
Validation: ecoom, 2016
Appears in Collections: Research publications

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