As chapters in this book demonstrate, BNP has important uses in clinical sciences and inference for issues like unknown partitions in genomics. Nonparametric Bayesian approaches (BNP) play an ever expanding role in biostatistical inference from use in proteomics to clinical trials. Many research problems involve an abundance of data and require flexible and complex probability models beyond the traditional parametric approaches. As this book's expert contributors show, BNP approaches can be the answer. Survival Analysis, in particular survival regression, has traditionally used BNP, but BNP's potential is now very broad. This applies to important tasks like arrangement of patients into clinically meaningful subpopulations and segmenting the genome into functionally distinct regions. This book is designed to both review and introduce application areas for BNP. While existing books provide theoretical foundations, this book connects theory to practice through engaging examples and research questions. Chapters cover: clinical trials, spatial inference, proteomics, genomics, clustering, survival analysis and ROC curve.
Author(s): Riten Mitra, Peter Müller (eds.)
Series: Frontiers in Probability and the Statistical Sciences
Edition: 1
Publisher: Springer International Publishing
Year: 2015
Language: English
Pages: XVII, 448
Tags: Statistics for Life Sciences, Medicine, Health Sciences; Biostatistics; Statistical Theory and Methods
Front Matter....Pages i-xvii
Front Matter....Pages 1-1
Bayesian Nonparametric Models....Pages 3-13
Bayesian Nonparametric Biostatistics....Pages 15-54
Front Matter....Pages 55-55
Bayesian Shape Clustering....Pages 57-75
Estimating Latent Cell Subpopulations with Bayesian Feature Allocation Models....Pages 77-95
Species Sampling Priors for Modeling Dependence: An Application to the Detection of Chromosomal Aberrations....Pages 97-114
Modeling the Association Between Clusters of SNPs and Disease Responses....Pages 115-134
Bayesian Inference on Population Structure: From Parametric to Nonparametric Modeling....Pages 135-151
Bayesian Approaches for Large Biological Networks....Pages 153-173
Nonparametric Variable Selection, Clustering and Prediction for Large Biological Datasets....Pages 175-192
Front Matter....Pages 193-193
Markov Processes in Survival Analysis....Pages 195-213
Bayesian Spatial Survival Models....Pages 215-246
Fully Nonparametric Regression Modelling of Misclassified Censored Time-to-Event Data....Pages 247-267
Front Matter....Pages 269-269
Neuronal Spike Train Analysis Using Gaussian Process Models....Pages 271-285
Bayesian Analysis of Curves Shape Variation Through Registration and Regression....Pages 287-310
Biomarker-Driven Adaptive Design....Pages 311-326
Bayesian Nonparametric Approaches for ROC Curve Inference....Pages 327-344
Front Matter....Pages 345-345
Spatial Bayesian Nonparametric Methods....Pages 347-357
Spatial Species Sampling and Product Partition Models....Pages 359-375
Spatial Boundary Detection for Areal Counts....Pages 377-399
Front Matter....Pages 401-401
A Bayesian Nonparametric Causal Model for Regression Discontinuity Designs....Pages 403-421
Front Matter....Pages 401-401
Bayesian Nonparametrics for Missing Data in Longitudinal Clinical Trials....Pages 423-446
Back Matter....Pages 447-448
