Mixture modelling for medical and health sciences

Content: Introduction Why Mixture Modelling is Needed Example: UCLA Example Data Set Fundamental Concepts of Finite Mixture Models Maximum Likelihood Estimation Spurious Clusters Determination of the Number of Components Identifiability of Mixture Distributions EM Algorithm Basic Principles of the EM Algorithm Formulation of Mixture Modelling as Incomplete-Data ProblemsConvergence and Initialization of the EM Algorithm Provision of Standard Errors of Estimates Applications of Mixture Models in Medical and Health Sciences Overview of Book Sample Size Considerations for Mixture Models Computing Packages for Mixture Models R Programs Fortran Programs Mixture of Normal Distributions for Continuous Data Introduction E- and M-steps Diagnostic Procedures Example: Univariate Normal Mixtures Example: Multivariate Normal Mixtures Extensions of the Normal Mixture Model R Programs for Fitting Mixtures of Normal Distributions  Mixture of Gamma Distributions for Continuous Nonnormal Data Introduction E- and M-steps Diagnostic Procedures Example: Mixture of Gamma Regression Model Example: Mixture of Gamma Distributions for Clustering Cost Data Fortran Programs for Fitting Mixtures of Gamma Distributions Mixture of Generalized Linear Models for Count or Categorical Data Introduction Poisson Mixture Regression Model Zero-inflated Poisson Regression Model Zero-inflated Negative Binomial Regression Models Example: Pancreas Disorder Length of Stay Data Score Tests for Zero-inflation in Count Models Example: Revisit of the Pancreas Disorder LOS Data Mixture of Generalized Bernoulli Distributions E- and M-steps Cluster Analysis in Comorbidity Research Example: Australian National Health Survey Data Computing Programs for Fitting Mixture of Generalized Linear Models Mixture Models for Survival Data Introduction Application of Mixture Models in Survival Analysis Mixture Models of Parametric Survival Distributions The EM Algorithm for Mixtures of Parametric Survival Models Example: Survival mixture modelling of mortality data Semi-Parametric Mixture Survival Models The ECM Algorithm Example: Survival analysis of competing-risks data Long-Term Survivor Mixture Models Example: Long-term survivors mixture model Diagnostic Procedures Fortran Programs for Fitting Mixtures of Survival Models Advanced mixture modelling with random-effects components Why is random effects modelling needed? Fundamentals for GLMM formulation and derivation Normally distributed random components and BLUP estimationMaximum likelihood (ML) estimation Residual maximum likelihood (REML) estimation Generalized linear mixed models (GLMM) Application of GLMM to mixture models with random effects Poisson mixture models Zero-inflated Poisson mixture models Frailty models in survival analysis Survival mixture models Long-term survivor models with random effects Advanced Mixture Models for Multilevel or Repeated-measured Data Introduction Poisson Mixture Regression Model with Random Effects Robust Estimation Using Minimum Hellinger Distance Assessment of Model Adequacy and Influence Diagnostics Example: Recurrent Urinary Tract Infection Data Zero-inflated Poisson Mixture Models with Random Effects Score test for zero-inflation in mixed Poisson models Example: Revisit of the Recurrent UTI Data Survival Mixture Models with Random Effects Example: rhDNase Clinical Trial Data Long-Term Survivor Mixture Models with Random Effects Example: Chronic Granulomatous Disease (CGD) Data Computing Programs for Fitting Multilevel Mixture Models Advanced Mixture Models for Correlated Multivariate Continuous Data Introduction Maximum likelihood estimation via the EM algorithm Clustering of gene-expression data (cross-sectional with repeated measurements) Inference on differences between classes using cluster specific contrasts of mixed effects A non-parametric clustering approach for identification of correlated differentially-expressed genes Example: Cluster analysis of a pancreatic cancer gene expression data set Clustering of time-course gene-expression data Inference for gene regulatory interactions Example: Cluster analysis of a time-course gene expression data set Clustering of multilevel longitudinal data EM-based estimation via maximum likelihood Example: Cluster analysis of a multilevel longitudinal data set R and Fortran Programs for Fitting Mixtures of Linear Mixed Models Miscellaneous: Handling of Missing Data Introduction Mixture model-based clustering of data with missing values Multiple imputation approach EM Algorithm Example: Multivariate normal mixture model Missing data in longitudinal studies Example: Clustering longitudinal data with missing values Summary Miscellaneous: Cluster Analysis of "Big Data" Using Mixture Models Introduction Speeding up the EM Algorithm for Multivariate Normal Mixtures Example: Segmentation of Magnetic Resonance (MR)Images of the Human Brain Example: Segmentation of Molecular Pathology Images of Cancer Patients Mixtures of linear mixed models for clustering big data with a hierarchical structure Clustering of Multilevel Data from Multiple Sources Consensus Clustering of Data from Multiple Sources