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An Introduction to Survival Analysis Using Stata, Revised Third Edition

models. Survival analysis is a field of its own that requires specialized data management and analysis procedures. To meet this requirement;

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Dynamic Prediction in Clinical Survival Analysis

Survival Analysis focuses on dynamic models for the remaining lifetime at later points in time, for instance using landmark models. Designed to;

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Statistical Models and Methods for Reliability and Survival Analysis

Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as;

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Survival Analysis

lifetime events. Scientists interested in survival analysis should find it a useful guidebook for the incorporation of survival data and methods;

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Survival Models and Data Analysis

, univariate survival function, multiple-failure data, and advanced topics.;

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Survival Models and Data Analysis

, univariate survival function, multiple-failure data, and advanced topics.;

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Survival Analysis Using the Sas System

Easy to read and comprehensive, Survival Analysis Using SAS: A Practical Guide, Second Edition, by Paul D. Allison, is an accessible, data;

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Quantitative Medical Data Analysis Using Mathematical Tools And Statistical Techniques

in data analysis, survival data analysis and survival models for cancer patients, statistical analysis and neural network techniques for genomic;

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Survival Analysis Using SAS

Easy to read and comprehensive, Survival Analysis Using SAS: A Practical Guide, Second Edition, by Paul Allison, is an accessible, data;

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Analysis of Survival Data with Dependent Censoring

basic survival analysis and explains the mathematical foundations of copula-based survival models.;

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Modelling Survival Data in Medical Research

Modelling Survival Data in Medical Research describes the modelling approach to the analysis of survival data using a wide range;

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Frailty Models in Survival Analysis

The concept of frailty offers a convenient way to introduce unobserved heterogeneity and associations into models for survival data. In its;

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Multivariate Survival Analysis and Competing Risks

Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It;

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Multivariate Survival Analysis and Competing Risks

Multivariate Survival Analysis and Competing Risks introduces univariate survival analysis and extends it to the multivariate case. It;

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Event History Analysis with R

With an emphasis on social science applications, Event History Analysis with R, Second Edition, presents an introduction to survival and;

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Advanced Survival Models

Survival data analysis is a very broad field of statistics, encompassing a large variety of methods used in a wide range of applications;

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Advanced Survival Models

Survival data analysis is a very broad field of statistics, encompassing a large variety of methods used in a wide range of applications;

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Flowgraph Models For Multistate Time-To-Event Data

Multistate Time--to--Event Data is an invaluable resource/reference for researchers in biostatisticssurvival analysis, systems engineering, and;

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Accelerated Life Models

The authors of this monograph have developed a large and important class of survival analysis models that generalize most of the existing;

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Accelerated Life Models

The authors of this monograph have developed a large and important class of survival analysis models that generalize most of the existing;

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Empirical Likelihood Method in Survival Analysis

empirical likelihood method for right censored survival data. The author uses R for calculating empirical likelihood and includes many worked out;

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Empirical Likelihood Method in Survival Analysis

empirical likelihood method for right censored survival data. The author uses R for calculating empirical likelihood and includes many worked out;

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Survival Analysis Using S

Survival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival;

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Mixed Effects Models for Complex Data

examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data;

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Mixed Effects Models for Complex Data

examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data;

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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;

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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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