# Analysis For Time To Event Data Under Censoring And Truncation

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## Analysis for Time to event Data Under Censoring and Truncation

Author | : Hongsheng Dai,Huan Wang |

Publsiher | : Academic Press |

Total Pages | : 96 |

Release | : 2016-10-01 |

ISBN 10 | : 9780128054802 |

ISBN 13 | : 0128054808 |

Language | : EN, FR, DE, ES & NL |

**Analysis for Time to event Data Under Censoring and Truncation Book Review:**

"Survival Analysis for Bivariate Truncated Data" provides readers with a comprehensive review on the existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function. The most distinguishing feature of survival data is known as censoring, which occurs when the survival time can only be exactly observed within certain time intervals. A second feature is truncation, which is often deliberate and usually due to selection bias in the study design. Truncation presents itself in different ways. For example, left truncation, which is often due to a so-called late entry bias, occurs when individuals enter a study at a certain age and are followed from this delayed entry time. Right truncation arises when only individuals who experienced the event of interest before a certain time point can be observed. Analyzing truncated survival data without considering the potential selection bias may lead to seriously biased estimates of the time to event of interest and the impact of risk factors. Assists statisticians, epidemiologists, medical researchers, and actuaries who need to understand the mechanism of selection biasReviews existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival functionOffers a guideline for analyzing truncated survival data

## Analysis for Time to Event Data under Censoring and Truncation

Author | : Hongsheng Dai,Huan Wang |

Publsiher | : Academic Press |

Total Pages | : 102 |

Release | : 2016-10-06 |

ISBN 10 | : 0081010087 |

ISBN 13 | : 9780081010082 |

Language | : EN, FR, DE, ES & NL |

**Analysis for Time to Event Data under Censoring and Truncation Book Review:**

Survival Analysis for Bivariate Truncated Data provides readers with a comprehensive review on the existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function. The most distinguishing feature of survival data is known as censoring, which occurs when the survival time can only be exactly observed within certain time intervals. A second feature is truncation, which is often deliberate and usually due to selection bias in the study design. Truncation presents itself in different ways. For example, left truncation, which is often due to a so-called late entry bias, occurs when individuals enter a study at a certain age and are followed from this delayed entry time. Right truncation arises when only individuals who experienced the event of interest before a certain time point can be observed. Analyzing truncated survival data without considering the potential selection bias may lead to seriously biased estimates of the time to event of interest and the impact of risk factors. Assists statisticians, epidemiologists, medical researchers, and actuaries who need to understand the mechanism of selection bias Reviews existing works on survival analysis for truncated data, mainly focusing on the estimation of univariate and bivariate survival function Offers a guideline for analyzing truncated survival data

## Survival Analysis

Author | : John P. Klein,Melvin L. Moeschberger |

Publsiher | : Springer Science & Business Media |

Total Pages | : 538 |

Release | : 2006-05-17 |

ISBN 10 | : 0387216456 |

ISBN 13 | : 9780387216454 |

Language | : EN, FR, DE, ES & NL |

**Survival Analysis Book Review:**

Applied statisticians in many fields must frequently analyze time to event data. While the statistical tools presented in this book are applicable to data from medicine, biology, public health, epidemiology, engineering, economics, and demography, the focus here is on applications of the techniques to biology and medicine. The analysis of survival experiments is complicated by issues of censoring, where an individual's life length is known to occur only in a certain period of time, and by truncation, where individuals enter the study only if they survive a sufficient length of time or individuals are included in the study only if the event has occurred by a given date. The use of counting process methodology has allowed for substantial advances in the statistical theory to account for censoring and truncation in survival experiments. This book makes these complex methods more accessible to applied researchers without an advanced mathematical background. The authors present the essence of these techniques, as well as classical techniques not based on counting processes, and apply them to data. Practical suggestions for implementing the various methods are set off in a series of Practical Notes at the end of each section. Technical details of the derivation of the techniques are sketched in a series of Technical Notes. This book will be useful for investigators who need to analyze censored or truncated life time data, and as a textbook for a graduate course in survival analysis. The prerequisite is a standard course in statistical methodology.

## Survival Analysis Using S

Author | : Mara Tableman,Jong Sung Kim |

Publsiher | : CRC Press |

Total Pages | : 280 |

Release | : 2003-07-28 |

ISBN 10 | : 1482285975 |

ISBN 13 | : 9781482285970 |

Language | : EN, FR, DE, ES & NL |

**Survival Analysis Using S Book Review:**

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 analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology. Prerequisites are a standard pre-calculus first course in probability and statistics, and a course in applied linear regression models. No prior knowledge of S or R is assumed. A wide choice of exercises is included, some intended for more advanced students with a first course in mathematical statistics. The authors emphasize parametric log-linear models, while also detailing nonparametric procedures along with model building and data diagnostics. Medical and public health researchers will find the discussion of cut point analysis with bootstrap validation, competing risks and the cumulative incidence estimator, and the analysis of left-truncated and right-censored data invaluable. The bootstrap procedure checks robustness of cut point analysis and determines cut point(s). In a chapter written by Stephen Portnoy, censored regression quantiles - a new nonparametric regression methodology (2003) - is developed to identify important forms of population heterogeneity and to detect departures from traditional Cox models. By generalizing the Kaplan-Meier estimator to regression models for conditional quantiles, this methods provides a valuable complement to traditional Cox proportional hazards approaches.

## Survival Analysis Using S

Author | : Mara Tableman,Jong Sung Kim |

Publsiher | : CRC Press |

Total Pages | : 280 |

Release | : 2003-07-28 |

ISBN 10 | : 0203501411 |

ISBN 13 | : 9780203501412 |

Language | : EN, FR, DE, ES & NL |

**Survival Analysis Using S Book Review:**

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 analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology. Prerequisites are a standard pre-calculus first course in probability and statistics, and a course in applied linear regression models. No prior knowledge of S or R is assumed. A wide choice of exercises is included, some intended for more advanced students with a first course in mathematical statistics. The authors emphasize parametric log-linear models, while also detailing nonparametric procedures along with model building and data diagnostics. Medical and public health researchers will find the discussion of cut point analysis with bootstrap validation, competing risks and the cumulative incidence estimator, and the analysis of left-truncated and right-censored data invaluable. The bootstrap procedure checks robustness of cut point analysis and determines cut point(s). In a chapter written by Stephen Portnoy, censored regression quantiles - a new nonparametric regression methodology (2003) - is developed to identify important forms of population heterogeneity and to detect departures from traditional Cox models. By generalizing the Kaplan-Meier estimator to regression models for conditional quantiles, this methods provides a valuable complement to traditional Cox proportional hazards approaches.

## Survival Analysis with Interval Censored Data

Author | : Kris Bogaerts,Arnost Komarek,Emmanuel Lesaffre |

Publsiher | : CRC Press |

Total Pages | : 584 |

Release | : 2017-11-20 |

ISBN 10 | : 1351643053 |

ISBN 13 | : 9781351643054 |

Language | : EN, FR, DE, ES & NL |

**Survival Analysis with Interval Censored Data Book Review:**

Survival Analysis with Interval-Censored Data: A Practical Approach with Examples in R, SAS, and BUGS provides the reader with a practical introduction into the analysis of interval-censored survival times. Although many theoretical developments have appeared in the last fifty years, interval censoring is often ignored in practice. Many are unaware of the impact of inappropriately dealing with interval censoring. In addition, the necessary software is at times difficult to trace. This book fills in the gap between theory and practice. Features: -Provides an overview of frequentist as well as Bayesian methods. -Include a focus on practical aspects and applications. -Extensively illustrates the methods with examples using R, SAS, and BUGS. Full programs are available on a supplementary website. The authors: Kris Bogaerts is project manager at I-BioStat, KU Leuven. He received his PhD in science (statistics) at KU Leuven on the analysis of interval-censored data. He has gained expertise in a great variety of statistical topics with a focus on the design and analysis of clinical trials. Arnošt Komárek is associate professor of statistics at Charles University, Prague. His subject area of expertise covers mainly survival analysis with the emphasis on interval-censored data and classification based on longitudinal data. He is past chair of the Statistical Modelling Society?and editor of?Statistical Modelling: An International Journal. Emmanuel Lesaffre is professor of biostatistics at I-BioStat, KU Leuven. His research interests include Bayesian methods, longitudinal data analysis, statistical modelling, analysis of dental data, interval-censored data, misclassification issues, and clinical trials. He is the founding chair of the?Statistical Modelling Society, past-president of the?International Society for Clinical Biostatistics,?and fellow of?ISI?and?ASA.

## Reliability and Survival Analysis

Author | : Md. Rezaul Karim,M. Ataharul Islam |

Publsiher | : Springer |

Total Pages | : 252 |

Release | : 2019-08-09 |

ISBN 10 | : 9811397767 |

ISBN 13 | : 9789811397769 |

Language | : EN, FR, DE, ES & NL |

**Reliability and Survival Analysis Book Review:**

This book presents and standardizes statistical models and methods that can be directly applied to both reliability and survival analysis. These two types of analysis are widely used in many fields, including engineering, management, medicine, actuarial science, the environmental sciences, and the life sciences. Though there are a number of books on reliability analysis and a handful on survival analysis, there are virtually no books on both topics and their overlapping concepts. Offering an essential textbook, this book will benefit students, researchers, and practitioners in reliability and survival analysis, reliability engineering, biostatistics, and the biomedical sciences.

## Analysis of Doubly Truncated Data

Author | : Achim Dörre,Takeshi Emura |

Publsiher | : Springer |

Total Pages | : 109 |

Release | : 2019-05-13 |

ISBN 10 | : 9811362416 |

ISBN 13 | : 9789811362415 |

Language | : EN, FR, DE, ES & NL |

**Analysis of Doubly Truncated Data Book Review:**

This book introduces readers to statistical methodologies used to analyze doubly truncated data. The first book exclusively dedicated to the topic, it provides likelihood-based methods, Bayesian methods, non-parametric methods, and linear regression methods. These procedures can be used to effectively analyze continuous data, especially survival data arising in biostatistics and economics. Because truncation is a phenomenon that is often encountered in non-experimental studies, the methods presented here can be applied to many branches of science. The book provides R codes for most of the statistical methods, to help readers analyze their data. Given its scope, the book is ideally suited as a textbook for students of statistics, mathematics, econometrics, and other fields.

## Advanced Survival Models

Author | : Catherine Legrand |

Publsiher | : CRC Press |

Total Pages | : 360 |

Release | : 2021-03-23 |

ISBN 10 | : 0429622554 |

ISBN 13 | : 9780429622557 |

Language | : EN, FR, DE, ES & NL |

**Advanced Survival Models Book Review:**

Survival data analysis is a very broad field of statistics, encompassing a large variety of methods used in a wide range of applications, and in particular in medical research. During the last twenty years, several extensions of "classical" survival models have been developed to address particular situations often encountered in practice. This book aims to gather in a single reference the most commonly used extensions, such as frailty models (in case of unobserved heterogeneity or clustered data), cure models (when a fraction of the population will not experience the event of interest), competing risk models (in case of different types of event), and joint survival models for a time-to-event endpoint and a longitudinal outcome. Features Presents state-of-the art approaches for different advanced survival models including frailty models, cure models, competing risk models and joint models for a longitudinal and a survival outcome Uses consistent notation throughout the book for the different techniques presented Explains in which situation each of these models should be used, and how they are linked to specific research questions Focuses on the understanding of the models, their implementation, and their interpretation, with an appropriate level of methodological development for masters students and applied statisticians Provides references to existing R packages and SAS procedure or macros, and illustrates the use of the main ones on real datasets This book is primarily aimed at applied statisticians and graduate students of statistics and biostatistics. It can also serve as an introductory reference for methodological researchers interested in the main extensions of classical survival analysis.

## Applied Categorical and Count Data Analysis

Author | : Wan Tang,Hua He,Xin M. Tu |

Publsiher | : CRC Press |

Total Pages | : 384 |

Release | : 2012-06-04 |

ISBN 10 | : 143989793X |

ISBN 13 | : 9781439897935 |

Language | : EN, FR, DE, ES & NL |

**Applied Categorical and Count Data Analysis Book Review:**

Developed from the authors’ graduate-level biostatistics course, Applied Categorical and Count Data Analysis explains how to perform the statistical analysis of discrete data, including categorical and count outcomes. The authors describe the basic ideas underlying each concept, model, and approach to give readers a good grasp of the fundamentals of the methodology without using rigorous mathematical arguments. The text covers classic concepts and popular topics, such as contingency tables, logistic models, and Poisson regression models, along with modern areas that include models for zero-modified count outcomes, parametric and semiparametric longitudinal data analysis, reliability analysis, and methods for dealing with missing values. R, SAS, SPSS, and Stata programming codes are provided for all the examples, enabling readers to immediately experiment with the data in the examples and even adapt or extend the codes to fit data from their own studies. Designed for a one-semester course for graduate and senior undergraduate students in biostatistics, this self-contained text is also suitable as a self-learning guide for biomedical and psychosocial researchers. It will help readers analyze data with discrete variables in a wide range of biomedical and psychosocial research fields.

## Encyclopedia of Quantitative Risk Analysis and Assessment

Author | : Anonim |

Publsiher | : John Wiley & Sons |

Total Pages | : 2176 |

Release | : 2008-09-02 |

ISBN 10 | : 0470035498 |

ISBN 13 | : 9780470035498 |

Language | : EN, FR, DE, ES & NL |

**Encyclopedia of Quantitative Risk Analysis and Assessment Book Review:**

Leading the way in this field, the Encyclopedia of Quantitative Risk Analysis and Assessment is the first publication to offer a modern, comprehensive and in-depth resource to the huge variety of disciplines involved. A truly international work, its coverage ranges across risk issues pertinent to life scientists, engineers, policy makers, healthcare professionals, the finance industry, the military and practising statisticians. Drawing on the expertise of world-renowned authors and editors in this field this title provides up-to-date material on drug safety, investment theory, public policy applications, transportation safety, public perception of risk, epidemiological risk, national defence and security, critical infrastructure, and program management. This major publication is easily accessible for all those involved in the field of risk assessment and analysis. For ease-of-use it is available in print and online.

## Applied Survival Analysis

Author | : David W. Hosmer, Jr.,Stanley Lemeshow,Susanne May |

Publsiher | : John Wiley & Sons |

Total Pages | : 416 |

Release | : 2011-09-23 |

ISBN 10 | : 1118211588 |

ISBN 13 | : 9781118211588 |

Language | : EN, FR, DE, ES & NL |

**Applied Survival Analysis Book Review:**

THE MOST PRACTICAL, UP-TO-DATE GUIDE TO MODELLING AND ANALYZING TIME-TO-EVENT DATA—NOW IN A VALUABLE NEW EDITION Since publication of the first edition nearly a decade ago, analyses using time-to-event methods have increase considerably in all areas of scientific inquiry mainly as a result of model-building methods available in modern statistical software packages. However, there has been minimal coverage in the available literature to9 guide researchers, practitioners, and students who wish to apply these methods to health-related areas of study. Applied Survival Analysis, Second Edition provides a comprehensive and up-to-date introduction to regression modeling for time-to-event data in medical, epidemiological, biostatistical, and other health-related research. This book places a unique emphasis on the practical and contemporary applications of regression modeling rather than the mathematical theory. It offers a clear and accessible presentation of modern modeling techniques supplemented with real-world examples and case studies. Key topics covered include: variable selection, identification of the scale of continuous covariates, the role of interactions in the model, assessment of fit and model assumptions, regression diagnostics, recurrent event models, frailty models, additive models, competing risk models, and missing data. Features of the Second Edition include: Expanded coverage of interactions and the covariate-adjusted survival functions The use of the Worchester Heart Attack Study as the main modeling data set for illustrating discussed concepts and techniques New discussion of variable selection with multivariable fractional polynomials Further exploration of time-varying covariates, complex with examples Additional treatment of the exponential, Weibull, and log-logistic parametric regression models Increased emphasis on interpreting and using results as well as utilizing multiple imputation methods to analyze data with missing values New examples and exercises at the end of each chapter Analyses throughout the text are performed using Stata® Version 9, and an accompanying FTP site contains the data sets used in the book. Applied Survival Analysis, Second Edition is an ideal book for graduate-level courses in biostatistics, statistics, and epidemiologic methods. It also serves as a valuable reference for practitioners and researchers in any health-related field or for professionals in insurance and government.

## Survival Analysis

Author | : John P. Klein,Melvin L. Moeschberger |

Publsiher | : Springer Science & Business Media |

Total Pages | : 502 |

Release | : 2013-06-29 |

ISBN 10 | : 1475727283 |

ISBN 13 | : 9781475727289 |

Language | : EN, FR, DE, ES & NL |

**Survival Analysis Book Review:**

Making complex methods more accessible to applied researchers without an advanced mathematical background, the authors present the essence of new techniques available, as well as classical techniques, and apply them to data. Practical suggestions for implementing the various methods are set off in a series of practical notes at the end of each section, while technical details of the derivation of the techniques are sketched in the technical notes. This book will thus be useful for investigators who need to analyse censored or truncated life time data, and as a textbook for a graduate course in survival analysis, the only prerequisite being a standard course in statistical methodology.

## Machine Learning and Knowledge Discovery in Databases

Author | : Frank Hutter,Kristian Kersting,Jefrey Lijffijt,Isabel Valera |

Publsiher | : Springer Nature |

Total Pages | : 755 |

Release | : 2021-02-24 |

ISBN 10 | : 3030676641 |

ISBN 13 | : 9783030676643 |

Language | : EN, FR, DE, ES & NL |

**Machine Learning and Knowledge Discovery in Databases Book Review:**

The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

## Modeling Discrete Time to Event Data

Author | : Gerhard Tutz,Matthias Schmid |

Publsiher | : Springer |

Total Pages | : 247 |

Release | : 2016-06-14 |

ISBN 10 | : 3319281585 |

ISBN 13 | : 9783319281582 |

Language | : EN, FR, DE, ES & NL |

**Modeling Discrete Time to Event Data Book Review:**

This book focuses on statistical methods for the analysis of discrete failure times. Failure time analysis is one of the most important fields in statistical research, with applications affecting a wide range of disciplines, in particular, demography, econometrics, epidemiology and clinical research. Although there are a large variety of statistical methods for failure time analysis, many techniques are designed for failure times that are measured on a continuous scale. In empirical studies, however, failure times are often discrete, either because they have been measured in intervals (e.g., quarterly or yearly) or because they have been rounded or grouped. The book covers well-established methods like life-table analysis and discrete hazard regression models, but also introduces state-of-the art techniques for model evaluation, nonparametric estimation and variable selection. Throughout, the methods are illustrated by real life applications, and relationships to survival analysis in continuous time are explained. Each section includes a set of exercises on the respective topics. Various functions and tools for the analysis of discrete survival data are collected in the R package discSurv that accompanies the book.

## Flexible Imputation of Missing Data Second Edition

Author | : Stef van Buuren |

Publsiher | : CRC Press |

Total Pages | : 416 |

Release | : 2018-07-17 |

ISBN 10 | : 0429960344 |

ISBN 13 | : 9780429960345 |

Language | : EN, FR, DE, ES & NL |

**Flexible Imputation of Missing Data Second Edition Book Review:**

Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or mean imputation, only work under highly restrictive conditions, which are often not met in practice. Multiple imputation replaces each missing value by multiple plausible values. The variability between these replacements reflects our ignorance of the true (but missing) value. Each of the completed data set is then analyzed by standard methods, and the results are pooled to obtain unbiased estimates with correct confidence intervals. Multiple imputation is a general approach that also inspires novel solutions to old problems by reformulating the task at hand as a missing-data problem. This is the second edition of a popular book on multiple imputation, focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. This new edition incorporates the recent developments in this fast-moving field. This class-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by verbal statements that explain the formula in accessible terms. The book sharpens the reader’s intuition on how to think about missing data, and provides all the tools needed to execute a well-grounded quantitative analysis in the presence of missing data.

## The Statistical Analysis of Doubly Truncated Data

Author | : Jacobo J de Uña-Álvarez,Rosa R. Crujeiras,Carla C. Moreira |

Publsiher | : John Wiley & Sons |

Total Pages | : 208 |

Release | : 2022-01-18 |

ISBN 10 | : 1119951372 |

ISBN 13 | : 9781119951377 |

Language | : EN, FR, DE, ES & NL |

**The Statistical Analysis of Doubly Truncated Data Book Review:**

This book provides an up-to-date review of the existing methods to deal with randomly truncated data, with focus on the difficult problem of random double truncation. Doubly truncated data is introduced in a comprehensive way and explores the latest developments in this field. Illustrative examples with R code are provided along with real data examples from astronomy, economy, and biomedical sciences to supplement the methods featured throughout the book.

## Epidemiology and Medical Statistics

Author | : Anonim |

Publsiher | : Elsevier |

Total Pages | : 870 |

Release | : 2007-11-21 |

ISBN 10 | : 9780080554211 |

ISBN 13 | : 0080554210 |

Language | : EN, FR, DE, ES & NL |

**Epidemiology and Medical Statistics Book Review:**

This volume, representing a compilation of authoritative reviews on a multitude of uses of statistics in epidemiology and medical statistics written by internationally renowned experts, is addressed to statisticians working in biomedical and epidemiological fields who use statistical and quantitative methods in their work. While the use of statistics in these fields has a long and rich history, explosive growth of science in general and clinical and epidemiological sciences in particular have gone through a see of change, spawning the development of new methods and innovative adaptations of standard methods. Since the literature is highly scattered, the Editors have undertaken this humble exercise to document a representative collection of topics of broad interest to diverse users. The volume spans a cross section of standard topics oriented toward users in the current evolving field, as well as special topics in much need which have more recent origins. This volume was prepared especially keeping the applied statisticians in mind, emphasizing applications-oriented methods and techniques, including references to appropriate software when relevant. · Contributors are internationally renowned experts in their respective areas · Addresses emerging statistical challenges in epidemiological, biomedical, and pharmaceutical research · Methods for assessing Biomarkers, analysis of competing risks · Clinical trials including sequential and group sequential, crossover designs, cluster randomized, and adaptive designs · Structural equations modelling and longitudinal data analysis

## Frailty Models in Survival Analysis

Author | : Andreas Wienke |

Publsiher | : CRC Press |

Total Pages | : 324 |

Release | : 2010-07-26 |

ISBN 10 | : 9781420073911 |

ISBN 13 | : 1420073915 |

Language | : EN, FR, DE, ES & NL |

**Frailty Models in Survival Analysis Book Review:**

The concept of frailty offers a convenient way to introduce unobserved heterogeneity and associations into models for survival data. In its simplest form, frailty is an unobserved random proportionality factor that modifies the hazard function of an individual or a group of related individuals. Frailty Models in Survival Analysis presents a comprehensive overview of the fundamental approaches in the area of frailty models. The book extensively explores how univariate frailty models can represent unobserved heterogeneity. It also emphasizes correlated frailty models as extensions of univariate and shared frailty models. The author analyzes similarities and differences between frailty and copula models; discusses problems related to frailty models, such as tests for homogeneity; and describes parametric and semiparametric models using both frequentist and Bayesian approaches. He also shows how to apply the models to real data using the statistical packages of R, SAS, and Stata. The appendix provides the technical mathematical results used throughout. Written in nontechnical terms accessible to nonspecialists, this book explains the basic ideas in frailty modeling and statistical techniques, with a focus on real-world data application and interpretation of the results. By applying several models to the same data, it allows for the comparison of their advantages and limitations under varying model assumptions. The book also employs simulations to analyze the finite sample size performance of the models.

## Handbook of Infectious Disease Data Analysis

Author | : Leonhard Held,Niel Hens,Philip D O'Neill,Jacco Wallinga |

Publsiher | : CRC Press |

Total Pages | : 554 |

Release | : 2019-11-04 |

ISBN 10 | : 1351839314 |

ISBN 13 | : 9781351839310 |

Language | : EN, FR, DE, ES & NL |

**Handbook of Infectious Disease Data Analysis Book Review:**

Recent years have seen an explosion in new kinds of data on infectious diseases, including data on social contacts, whole genome sequences of pathogens, biomarkers for susceptibility to infection, serological panel data, and surveillance data. The Handbook of Infectious Disease Data Analysis provides an overview of many key statistical methods that have been developed in response to such new data streams and the associated ability to address key scientific and epidemiological questions. A unique feature of the Handbook is the wide range of topics covered. Key features Contributors include many leading researchers in the field Divided into four main sections: Basic concepts, Analysis of Outbreak Data, Analysis of Seroprevalence Data, Analysis of Surveillance Data Numerous case studies and examples throughout Provides both introductory material and key reference material