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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.
Probability, Statistics and Modelling in Public Health consists of
refereed contributions by expert biostatisticians that discuss
various probabilistic and statistical models used in public health.
Many of them are based on the work of Marvin Zelen of the Harvard
School of Public Health. Topics discussed include models based on
Markov and semi-Markov processes, multi-state models, models and
methods in lifetime data analysis, accelerated failure models,
design and analysis of clinical trials, Bayesian methods,
pharmaceutical and environmental statistics, degradation models,
epidemiological methods, screening programs, early detection of
diseases, and measurement and analysis of quality of life.
Probability, Statistics and Modelling in Public Health consists of
refereed contributions by expert biostatisticians that discuss
various probabilistic and statistical models used in public health.
Many of them are based on the work of Marvin Zelen of the Harvard
School of Public Health. Topics discussed include models based on
Markov and semi-Markov processes, multi-state models, models and
methods in lifetime data analysis, accelerated failure models,
design and analysis of clinical trials, Bayesian methods,
pharmaceutical and environmental statistics, degradation models,
epidemiological methods, screening programs, early detection of
diseases, and measurement and analysis of quality of life.
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.
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