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Activity Number:
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104
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #308895 |
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Title:
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Semiparametric Joint Modeling of Longitudinal and Time-to-Event Data Using P-Spline: A Penalized Likelihood Approach
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Author(s):
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Wen Ye*+ and Xihong Lin and Jeremy Taylor
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Companies:
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University of Michigan and Harvard School of Public Health and University of Michigan
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Address:
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M4073 SPH II, Ann Arbor, MI, 48109,
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Keywords:
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Joint Modeling ; Survivial ; Longitudinal ; P-spline ; Penalized Likelihood
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Abstract:
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Longitudinal studies in medical research often generate both repeated measurements of biomarkers and possibly censored survival data. Recently several joint models have been developed to deal with the challenges arising in this type of data. Commonly, in joint models, the longitudinal covariate is modeled by a linear mixed model. However, in some cases, the longitudinal covariate time trajectory is not linear. We propose a joint model using penalized cubic B-spline to accommodate the non-linear trajectory of longitudinal covariate measurements. To ease computation the estimation procedure is maximizing a penalized joint likelihood generated by a Laplace approximation of the joint likelihood, which combines the likelihood of the longitudinal data and the partial likelihood of the time-to-event data. Properties of parameter estimators are investigated in simulation studies.
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