Dynamical Biostatistical Models

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Opis: Dynamical Biostatistical Models - Helene Jacqmin-Gadda, Daniel Commenges

Dynamical Biostatistical Models presents statistical models and methods for the analysis of longitudinal data. The book focuses on models for analyzing repeated measures of quantitative and qualitative variables and events history, including survival and multistate models. Most of the advanced methods, such as multistate and joint models, can be applied using SAS or R software. The book describes advanced regression models that include the time dimension, such as mixed-effect models, survival models, multistate models, and joint models for repeated measures and time-to-event data. It also explores the possibility of unifying these models through a stochastic process point of view and introduces the dynamic approach to causal inference. Drawing on much of their own extensive research, the authors use three main examples throughout the text to illustrate epidemiological questions and methodological issues. Readers will see how each method is applied to real data and how to interpret the results.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


Szczegóły: Dynamical Biostatistical Models - Helene Jacqmin-Gadda, Daniel Commenges

Tytuł: Dynamical Biostatistical Models
Autor: Helene Jacqmin-Gadda, Daniel Commenges
Producent: Productivity Press Inc
ISBN: 9781498729673
Rok produkcji: 2015
Ilość stron: 408
Oprawa: Twarda


Recenzje: Dynamical Biostatistical Models - Helene Jacqmin-Gadda, Daniel Commenges

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Dynamical Biostatistical Models

,

Dynamical Biostatistical Models presents statistical models and methods for the analysis of longitudinal data. The book focuses on models for analyzing repeated measures of quantitative and qualitative variables and events history, including survival and multistate models. Most of the advanced methods, such as multistate and joint models, can be applied using SAS or R software. The book describes advanced regression models that include the time dimension, such as mixed-effect models, survival models, multistate models, and joint models for repeated measures and time-to-event data. It also explores the possibility of unifying these models through a stochastic process point of view and introduces the dynamic approach to causal inference. Drawing on much of their own extensive research, the authors use three main examples throughout the text to illustrate epidemiological questions and methodological issues. Readers will see how each method is applied to real data and how to interpret the results.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

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Cena 352,00 PLN
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