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Joint Modeling of Longitudinal and Time-to-Event Data

for scientific investigators who need to analyze longitudinal and/or survival data, as well as researchers developing methodology in this;

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Joint Modeling of Longitudinal and Time-to-Event Data

for scientific investigators who need to analyze longitudinal and/or survival data, as well as researchers developing methodology in this;

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Modeling Survival Data

This book is for statistical practitioners, particularly those who design and analyze studies for survival and event history data. Building;

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Handbook for Applied Modeling

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data;

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Handbook for Applied Modeling

Designed for the applied practitioner, this book is a compact, entry-level guide to modeling and analyzing non-Gaussian and correlated data;

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Survival Analysis for Epidemiologic and Medical Research

, confidence intervals, and analytic modeling are presented in the context of survival data but are, in fact, statistical tools that apply to;

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Modeling Microbial Responses in Food

on techniques in mathematical modeling of microbial growth and survival. The comprehensive coverage includes basic approaches such as;

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Modeling Microbial Responses in Food

on techniques in mathematical modeling of microbial growth and survival. The comprehensive coverage includes basic approaches such as;

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Practical Guides to Biostatistics and Epidemiology

This practical guide to survival data and its analysis for readers with a minimal background in statistics shows why the analytic methods;

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Statistical Modeling in Biomedical Research

applications Large scale data analysis and its applications Biomedical research and modeling Survival analysis with complex data structure and its;

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Recent Advances In Stochastic Modeling And Data Analysis

This volume presents the most recent applied and methodological issues in stochastic modeling and data analysis. The contributions cover;

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Bayesian Disease Mapping

Focusing on data commonly found in public health databases and clinical settings, Bayesian Disease Mapping: Hierarchical Modeling;

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Statistical Modeling in Biomedical Research: Contemporary Topics and Voices in the Field

Large scale data analysis and its applications Biomedical research and modeling Survival analysis with complex data structure and its;

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Bayesian Disease Mapping

Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the;

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Bayesian Disease Mapping

Since the publication of the second edition, many new Bayesian tools and methods have been developed for space-time data analysis, the;

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Essential Bayesian Models

of topics including objective and subjective Bayesian inferences, with a variety of applications in modeling categorical, survival, spatial;

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Generalized Latent Variable Modeling

response types, such as ordinal, nominal, count, and survival data. Joint modeling of mixed responses, such as survival and longitudinal data;

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Applied Survival Analysis

Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical;

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Integrated Population Biology and Modeling, Part A

-environment interactions, Survival Probabilities from 5-Year Cumulative Life Table Survival Ratios (Tx+5/Tx): Some Innovative Methodological;

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Interval-Censored Time-to-Event Data

data modeling, including nonparametric estimation, survival functions, regression analysis, multivariate data analysis, competing risks;

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Bayesian Modeling in Bioinformatics

error and survival models for cDNA microarrays, a Bayesian hidden Markov modeling approach for CGH array data, Bayesian approaches for;

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Bayesian Modeling in Bioinformatics

error and survival models for cDNA microarrays, a Bayesian hidden Markov modeling approach for CGH array data, Bayesian approaches for;

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Modeling the Agile Data Warehouse with Data Vault

Data Modeling for Agile Data Warehouse using Data Vault Modeling Approach. Includes Enterprise Data Warehouse Architecture. This is a;

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Regression With Social Data

response models* Poisson, negative binomial, and other regression models for event-count data* Survival analysis using multistate, multiepisode;

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Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

multivariate models, survival analysis, missing data, measurement error, variable selection, individual event modeling, and infectious disease modeling;

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Bayesian Ideas And Data Analysis

regression, analysis of variance, and Poisson regression, before extending these methods to handle correlated data. The authors also examine survival;

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Foundations of Predictive Analytics

Drawing on the authors' two decades of experience in applied modeling and data mining, Foundations of Predictive Analytics presents the;

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