Note: Intro -- Contents -- Part I Biomedical Big Data -- Extreme Observations in Biomedical Data -- 1 Introduction -- 2 Methods -- 3 Application to Data in Autism Multiplex Families -- 4 Application to Gene Expression Data in Cancer -- References -- An Ordinal Joint Model for Breast Cancer -- 1 Introduction -- 2 A Bayesian Joint Model for Ordinal Longitudinal and Left Truncated Survival Data -- 3 Breast Cancer and Mammographic Breast Density -- References -- Sample Size Impact on the Categorisation of Continuous Variables in Clinical Prediction -- 1 Motivation -- 2 Methods -- 2.1 Categorisation Proposal Based on GAM with P-Spline Smoothers -- 2.2 Optimal Categorisation Based on the Maximisation of the AUC -- 3 Simulation Study -- 3.1 Scenarios and Set Up -- 3.2 Results -- 4 Conclusions -- References -- Integrative Analysis of Transcriptomics and Proteomics Data for the Characterization of Brain Tissue After Ischemic Stroke -- 1 Introduction and Objectives -- 2 Methods -- 3 Results -- 4 Conclusions -- References -- Applying INAR-Hidden Markov Chains in the Analysis of Under-Reported Data -- 1 Introduction -- 2 The Model -- 2.1 Parameter Estimation -- 2.2 Reconstruction of the Underlying Process -- 2.3 Model Selection and Goodness of Fit -- 3 Example of Application -- References -- Joint Modelling for Flexible Multivariate Longitudinal and Survival Data: Application in Orthotopic Liver Transplantation -- 1 Introduction -- 2 Orthotopic Liver Transplantation Data -- 3 Two-Stage Model Based Proposal -- 3.1 Stage 1: Flexible Multivariate Longitudinal Data -- 3.2 Stage 2: Survival Model -- 4 Conclusions -- References -- A Multi-state Model for the Progression to Osteopenia and Osteoporosis Among HIV-Infected Patients -- 1 Motivation -- 2 Multi-state Model -- 2.1 Notation -- 2.2 Estimation Method -- 3 Results -- 3.1 Descriptive Analysis. , 3.2 Estimated Transition Probabilities -- 4 Discussion -- References -- Statistical Challenges for Human Microbiome Analysis -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Conclusions -- References -- Integrative Analysis to Select Genes Regulated by Methylation in a Cancer Colon Study -- 1 Introduction and Objectives -- 2 Methods for L-Pattern Selection -- 2.1 Gene Selection Based on Conditional Mutual Information -- 2.2 Gene Selection Based on Spline Regression -- 3 Results and Application: Selecting L-Shaped Genes from a Genome-Wide Analysis of Colorectal Cancer -- 3.1 Results Using the Conditional Mutual Information Approach -- 3.2 Results Using Splines Regression to Select Genes -- 4 Discussion and Conclusions -- References -- Topological Pathway Enrichment Analysis of Gene Expression in High Grade Serous Ovarian Cancer Reveals Tumor-Stoma Cross-Talk -- 1 Introduction -- 2 Methods -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Part II Statistics for Low Dose Radiation Research -- Biological Dosimetry, Statistical Challenges: Biological Dosimetry After High-Dose Exposures to Ionizing Radiation -- References -- Heterogeneous Correlation of Multi-level Omics Data for the Consideration of Inter-tumoural Heterogeneity -- 1 Introduction -- 1.1 Radiobiological Background -- 1.2 Heterogeneous Gene Expression -- 2 Normal Mixture Models -- 3 Regression Clustering -- 4 Conclusion -- References -- Overview of Topics Related to Model Selection for Regression -- 1 Introduction -- 2 Strategies for Combining Low and High Dimensional Data -- 2.1 Naive Strategy -- 2.2 Clinical Offset Strategy -- 2.3 Favouring Strategy -- 3 Statistical Methods -- 3.1 Lasso -- 3.2 Boosting -- 3.3 Tuning Parameters -- 4 Data -- 4.1 Split in Training and Test Data -- 5 Evaluation -- 6 Results -- References. , Understanding Plaque Overlap Is Essential for Modelling Radiation Induced Atherosclerosis -- 1 Radiation Induced Atherosclerosis -- 1.1 Plaque Initiation and Early Plaque Growth -- 1.2 Plaque Overlap -- References -- On the Use of Random Effect Models for Radiation Biodosimetry -- 1 Introduction -- 2 Protein Biomarkers -- 3 Biomarkers Based on Gene Expressions -- 4 Cytogenetic Biomarkers -- 5 Conclusion -- References -- Modelling of the Radiation Carcinogenesis: The Analytic and Stochastic Approaches -- 1 Introduction -- 2 Stochastic Approach -- 2.1 Adaptive Response Effect -- 2.2 Bystander Effect -- 3 Analytic Approach -- 4 Discussion -- References -- Bayesian Solutions to Biodosimetry Count Data Problems and Supporting Software -- 1 Introduction -- 2 Bayesian Approach -- 3 Whole Body Homogeneous Exposure -- 3.1 Poisson Responses -- 3.2 Software: radir -- 3.3 Compound-Poisson Responses -- 4 Homogeneous Partial Body Exposure -- 5 Conclusions -- References -- Empirical Assessment of Gene Expression Biomarkers for Radiation Exposure -- 1 Introduction -- 2 Method -- 3 Case Study from Drug Discovery -- 4 Discussion -- References -- Poisson-Weighted Estimation by Discrete Kernel with Application to Radiation Biodosimetry -- 1 Introduction and Motivations -- 2 Methodology and Results -- 3 Concluding Remarks -- References -- R Implementation of the Excess Relative Rate Model: Applications to Radiation Epidemiology -- 1 Introduction -- 2 The Package linERR -- 3 Examples -- References -- Uncertainty Considerations Following a Mechanistic Analysis of Lung Cancer Mortality -- 1 Introduction -- 2 The TSCE Model -- 3 Sources of Uncertainty in the Current Study -- 4 Conclusions and Open Questions -- References -- 22 Erratum to: Understanding Plaque Overlap Is Essential for Modelling Radiation Induced Atherosclerosis. , Erratum to: Chapter 14 in: E.A. Ainsbury et al. (eds.), Extended Abstracts Fall 2015, Trends in Mathematics 7, DOI 10.1007/978-3-319-55639-0_14.