Bayesian Hierarchical Changepoint Model for Longitudinal Biomarkers PDF Download
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Author: Wonsuk Yoo
Publisher:
ISBN:
Category :
Languages : en
Pages : 240
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Book Description
Author: Wonsuk Yoo
Publisher:
ISBN:
Category :
Languages : en
Pages : 240
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Book Description
Author: Stephen Walter Gulyas
Publisher:
ISBN:
Category :
Languages : en
Pages : 722
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Author: Laura A. Hatfield
Publisher:
ISBN:
Category :
Languages : en
Pages : 111
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Author: Jun Ying
Publisher:
ISBN:
Category :
Languages : en
Pages : 210
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Author: Lei Qian
Publisher:
ISBN:
Category :
Languages : en
Pages : 226
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Author: Lili Yang
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 268
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Book Description
Epidemiologic and clinical studies routinely collect longitudinal measures of multiple outcomes. These longitudinal outcomes can be used to establish the temporal order of relevant biological processes and their association with the onset of clinical symptoms. In the first part of this thesis, we proposed to use bivariate change point models for two longitudinal outcomes with a focus on estimating the correlation between the two change points. We adopted a Bayesian approach for parameter estimation and inference. In the second part, we considered the situation when time-to-event outcome is also collected along with multiple longitudinal biomarkers measured until the occurrence of the event or censoring. Joint models for longitudinal and time-to-event data can be used to estimate the association between the characteristics of the longitudinal measures over time and survival time. We developed a maximum-likelihood method to joint model multiple longitudinal biomarkers and a time-to-event outcome. In addition, we focused on predicting conditional survival probabilities and evaluating the predictive accuracy of multiple longitudinal biomarkers in the joint modeling framework. We assessed the performance of the proposed methods in simulation studies and applied the new methods to data sets from two cohort studies.
Author: Constantine Gatsonis
Publisher: Springer Science & Business Media
ISBN: 1461222907
Category : Mathematics
Languages : en
Pages : 483
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Book Description
This third volume of case studies presents detailed applications of Bayesian statistical analysis, emphasising the scientific context. The papers were presented and discussed at a workshop held at Carnegie-Mellon University, and this volume - dedicated to the memory of Morrie Groot-reproduces six invited papers, each with accompanying invited discussion, and nine contributed papers with the focus on econometric applications.
Author: Alfonso Ang
Publisher:
ISBN:
Category : Bayesian statistical decision theory
Languages : en
Pages : 404
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Book Description
Author: Dimitris Rizopoulos
Publisher: CRC Press
ISBN: 1439872864
Category : Mathematics
Languages : en
Pages : 279
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Book Description
In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest, e.g., prostate cancer studies where longitudinal PSA level measurements are collected in conjunction with the time-to-recurrence. Joint Models for Longitudinal and Time-to-Event Data: With Applications in R provides a full treatment of random effects joint models for longitudinal and time-to-event outcomes that can be utilized to analyze such data. The content is primarily explanatory, focusing on applications of joint modeling, but sufficient mathematical details are provided to facilitate understanding of the key features of these models. All illustrations put forward can be implemented in the R programming language via the freely available package JM written by the author. All the R code used in the book is available at: http://jmr.r-forge.r-project.org/
Author: Jonathan S. Berek
Publisher: Lippincott Williams & Wilkins
ISBN: 1469890836
Category : Medical
Languages : en
Pages : 1858
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Book Description
Berek and Hacker's Gynecologic Oncology is written for gynecologic oncologists and fellows, general gynecologists and medical and radiation oncologists and presents the general principles and medical and surgical treatment for the range of gyencologic cancers: cervical, breast, ovarian, vulvar and vaginal and uterine. Chapters are templated and evidence-based. The strength of this book is its ability to translate basic science to clinical practice. Gynecologic Oncology is one of the four gynecologic subspecialties (along with FPMRS, REI and MFM).
