Dynamical biostatistical models

Content: Introduction General presentation of the book Organization of the book Notation Presentation of examples Classical Biostatistical Models Inference Generalities on inference: the concept of model Likelihood and applications Other types of likelihoods and estimation methods Model choice Optimization algorithms Survival Analysis Introduction Event, origin, and functions of interest Observation patterns: censoring and truncation Estimation of the survival function The proportional hazards model Accelerated failure time model Counting processes approach Additive hazards models Degradation models Models for Longitudinal Data Linear mixed models Generalized mixed linear models Non-linear mixed models Marginal models and generalized estimating equations (GEE) Incomplete longitudinal data Modeling strategies Advanced Biostatistical Models Extensions of Mixed Models Mixed models for curvilinear outcomes Mixed models for multivariate longitudinal data Latent class mixed models Advanced Survival Models Relative survival Competing risks models Frailty models Extension of frailty models Cure models Multistate Models Introduction Multistate processes Multistate models: generalities Observation schemes Statistical inference for multistate models observed in continuous time Inference for multistate models from interval-censored data Complex functions of parameters: individualized hazards, sojourn times Approach by counting processes Other approaches Joint Models for Longitudinal and Time-to-Event Data Introduction Models with shared random effects Latent class joint model Latent classes versus shared random effects The joint model as prognostic model Extension of joint models The Dynamic Approach to Causality Introduction Local independence, direct and indirect influence Causal influences The dynamic approach to causal reasoning in ageing studies Mechanistic models The issue of dynamic treatment regimes Appendix: Software Index