Survival Analysis with Long-Term Survivors

1996
 |  Mathematics
Survival Analysis with Long-Term Survivors

Author: Ross A. Maller

Publisher: John Wiley & Sons

ISBN: UVA:X004104251

Category: Mathematics

Page: 312

View: 417

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The aim of this book is to suggest and exemplify a systematic methodology for analysing survival data which contains "immune", or "cured" individuals, denoted generically as "long-term survivors". Such data occurs in medical and epidemiological applications, where the intention may be to identify whether or not cured or immune individuals are present in a population, perhaps as a result of treatments given; in the analysis of recidivism data in criminology, where the intentions are similar with respect to prisoners released from and possibly returning to prison; and in many other areas where followup data is available on individuals, with the possibility that not all suffer the event under investigation. Both nonparametric and parametric methods are proposed and developed. The effects of covariate information can be assessed via a kind of generalised linear framework in the parametric analyses. The proposed methodologies are supported by asymptotic analyses and simulations of real situations. While these theoretical underpinnings are presented in reasonable rigour and detail, the book is aimed very much at the practitioner who wishes to analyse survival data with (or even without) immunes.

Analysis of Interval-Censored Failure Time Data with Long-Term Survivors

2017-01-26
 | 
Analysis of Interval-Censored Failure Time Data with Long-Term Survivors

Author: Kin-Yau Wong

Publisher:

ISBN: 1361279419

Category:

Page:

View: 859

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This dissertation, "Analysis of Interval-censored Failure Time Data With Long-term Survivors" by Kin-yau, Wong, 黃堅祐, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Failure time data analysis, or survival analysis, is involved in various research fields, such as medicine and public health. One basic assumption in standard survival analysis is that every individual in the study population will eventually experience the event of interest. However, this assumption is usually violated in practice, for example when the variable of interest is the time to relapse of a curable disease resulting in the existence of long-term survivors. Also, presence of unobservable risk factors in the group of susceptible individuals may introduce heterogeneity to the population, which is not properly addressed in standard survival models. Moreover, the individuals in the population may be grouped in clusters, where there are associations among observations from a cluster. There are methodologies in the literature to address each of these problems, but there is yet no natural and satisfactory way to accommodate the coexistence of a non-susceptible group and the heterogeneity in the susceptible group under a univariate setting. Also, various kinds of associations among survival data with a cure are not properly accommodated. To address the above-mentioned problems, a class of models is introduced to model univariate and multivariate data with long-term survivors. A semiparametric cure model for univariate failure time data with long-term survivors is introduced. It accommodates a proportion of non-susceptible individuals and the heterogeneity in the susceptible group using a compound- Poisson distributed random effect term, which is commonly called a frailty. It is a frailty-Cox model which does not place any parametric assumption on the baseline hazard function. An estimation method using multiple imputation is proposed for right-censored data, and the method is naturally extended to accommodate interval-censored data. The univariate cure model is extended to a multivariate setting by introducing correlations among the compound- Poisson frailties for individuals from the same cluster. This multivariate cure model is similar to a shared frailty model where the degree of association among each pair of observations in a cluster is the same. The model is further extended to accommodate repeated measurements from a single individual leading to serially correlated observations. Similar estimation methods using multiple imputation are developed for the multivariate models. The univariate model is applied to a breast cancer data and the multivariate models are applied to the hypobaric decompression sickness data from National Aeronautics and Space Administration, although the methodologies are applicable to a wide range of data sets. DOI: 10.5353/th_b4819947 Subjects: Failure time data analysis Survival analysis (Biometry)

Handbook of Survival Analysis

2016-04-19
 |  Mathematics
Handbook of Survival Analysis

Author: John P. Klein

Publisher: CRC Press

ISBN: 9781466555679

Category: Mathematics

Page: 656

View: 741

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Handbook of Survival Analysis presents modern techniques and research problems in lifetime data analysis. This area of statistics deals with time-to-event data that is complicated by censoring and the dynamic nature of events occurring in time. With chapters written by leading researchers in the field, the handbook focuses on advances in survival analysis techniques, covering classical and Bayesian approaches. It gives a complete overview of the current status of survival analysis and should inspire further research in the field. Accessible to a wide range of readers, the book provides: An introduction to various areas in survival analysis for graduate students and novices A reference to modern investigations into survival analysis for more established researchers A text or supplement for a second or advanced course in survival analysis A useful guide to statistical methods for analyzing survival data experiments for practicing statisticians

Survival Data Analysis with Heavy-censoring and Long-term Survivors

2014
 | 
Survival Data Analysis with Heavy-censoring and Long-term Survivors

Author: Lucas López Segovia

Publisher:

ISBN: OCLC:1120392001

Category:

Page: 126

View: 839

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The research developed in this thesis has been motivated by two datasets, which are introduced in Chapter 2, one concerning the mortality of calves from birth to weaning while the other refers to survival of patients diagnosed with melanoma. In both cases the percentage of censoring is high, it is very likely to have immune individuals and proper analysis accounting for the possibility of a not negligible proportion of cured individuals has to be performed. Cure models are introduced in Chapter 3 together with the available software to perform the analysis, such as SAS, R and STATA, among others. We investigate the effect that heavy censoring could have on the estimation of the regression coefficients in the Cox model via a simulation study which considers several scenarios given by different sample sizes and censoring levels, results presented in Chapter 4. An application of a mixture cure model, which includes a Cox model for the survival part and a logistic model for the cure part of patients with melanoma, is described in Chapter 5. In addition, discussions about test for sufficient follow-up and censoring levels are also presented for this data. The data analysis is carried out using the macro in SAS: PSPMCM. The results show that patients with Sentinel Lymph Node (SLN): negative status to biopsy, Clark's level of invasion I-III, Histopathological of Malignant Melanoma subtype: Superficial Spreading Melanoma (SSM), younger than 46 years, and female, are more likely to be cured, whereas patients with melanoma in head and neck, Breslow's micrometric depth = 4mm and ulceration presents, are patients with increased risk of relapse. In particular, patients with Breslow's micrometric depth = 4mm are at higher risk for death. Furthermore, since mixture cure models do not have the property of proportional hazards for the entire population, they can be extended to non-mixture cure models by means of nonlinear transformation models as defined in Tsodikov (2003). An application of the extended hazard models is presented for the mortality of calves in Chapter 6. The methodology allows to get estimates for the cure rate as well as for genetic and environmental effects for each herd. A relevant feature of the non-mixture cure models is that they model, separately, factors which could affect survival from those affecting the cure model, making the interpretation of these models relatively easy. Results are shown in section 6.3.1, and were obtained using the library NLTM of the statistical package R. The short (mortality) and long term (survivors) effects are determined for each factors, as well as its statistical significance in each herd. For example in the herd 1, we find that calving month and difficulty at birth is the set of statistically significant factors for the nonsusceptible (long-term survivors) proportion. Calves born in the period march-august have lower probability of survive than those born in September-February; and the probability of survive is much lower for those that have difficulties at calving for herd 1. For herd 7 the effect of difficulty at calving is different as for herd 1, here only is significative the category strongly assisted. Calves that born from strongly assisted calving have lower probability of survive that calves from without assistance calving. Regarding short-term (mortality) effects, we only find statistically significant predictors in herd 7 where the risk of death of calves born from older mothers, hence with a longer reproductive life, is twice the risk of death of calves born from younger mothers. The obtained results have been compared with those coming from standard survival models. It is also included, a discussion about the likely erroneous conclusions that may yield from standard models, without taking into account the cure.

Bayesian Survival Analysis

2013-03-09
 |  Medical
Bayesian Survival Analysis

Author: Joseph G. Ibrahim

Publisher: Springer Science & Business Media

ISBN: 9781475734478

Category: Medical

Page: 480

View: 346

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Survival analysis arises in many fields of study including medicine, biology, engineering, public health, epidemiology, and economics. This book provides a comprehensive treatment of Bayesian survival analysis. It presents a balance between theory and applications, and for each class of models discussed, detailed examples and analyses from case studies are presented whenever possible. The applications are all from the health sciences, including cancer, AIDS, and the environment.

Applied Survival Analysis

2011-09-23
 |  Mathematics
Applied Survival Analysis

Author: David W. Hosmer, Jr.

Publisher: John Wiley & Sons

ISBN: 9781118211588

Category: Mathematics

Page: 416

View: 506

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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.

Frailty Models in Survival Analysis

2010-07-26
 |  Mathematics
Frailty Models in Survival Analysis

Author: Andreas Wienke

Publisher: CRC Press

ISBN: 1420073915

Category: Mathematics

Page: 324

View: 902

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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.

Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life

2013-11-11
 |  Mathematics
Parametric and Semiparametric Models with Applications to Reliability, Survival Analysis, and Quality of Life

Author: M.S. Nikulin

Publisher: Springer Science & Business Media

ISBN: 9780817682064

Category: Mathematics

Page: 555

View: 868

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Parametric and semiparametric models are tools with a wide range of applications to reliability, survival analysis, and quality of life. This self-contained volume examines these tools in survey articles written by experts currently working on the development and evaluation of models and methods. While a number of chapters deal with general theory, several explore more specific connections and recent results in "real-world" reliability theory, survival analysis, and related fields. Specific topics covered include: * cancer prognosis using survival forests * short-term health problems related to air pollution: analysis using semiparametric generalized additive models * semiparametric models in the studies of aging and longevity This book will be of use as a reference text for general statisticians, theoreticians, graduate students, reliability engineers, health researchers, and biostatisticians working in applied probability and statistics.

Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications

2013-06-14
 |  Mathematics
Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications

Author: João Lita da Silva

Publisher: Springer Science & Business Media

ISBN: 9783642349041

Category: Mathematics

Page: 465

View: 359

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This volume of the Selected Papers from Portugal is a product of the Seventeenth Congress of the Portuguese Statistical Society, held at the beautiful resort seaside city of Sesimbra, Portugal, from September 30 to October 3, 2009. It covers a broad scope of theoretical, methodological as well as application-oriented articles in domains such as: Linear Models and Regression, Survival Analysis, Extreme Value Theory, Statistics of Diffusions, Markov Processes and other Statistical Applications.