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Survival Analysis With Long Term Survivors

Survival Analysis with Long Term Survivors PDF
Author: Ross A. Maller
Publisher: John Wiley & Sons
Release: 1996
Size: 77.39 MB
Format: PDF, Kindle
Category : Mathematics
Languages : un
Pages : 278
View: 4453

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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.

Survival Data Analysis With Heavy Censoring And Long Term Survivors

Survival Data Analysis with Heavy censoring and Long term Survivors PDF
Author: Lucas López Segovia
Publisher:
Release: 2014
Size: 26.63 MB
Format: PDF, Mobi
Category :
Languages : un
Pages : 126
View: 1386

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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.

Analysis Of Interval Censored Failure Time Data With Long Term Survivors

Analysis of Interval Censored Failure Time Data with Long Term Survivors PDF
Author: Kin-Yau Wong
Publisher:
Release: 2017-01-26
Size: 31.23 MB
Format: PDF, ePub
Category :
Languages : un
Pages :
View: 3846

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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)

Statistical Techniques In Life Testing Reliability Sampling Theory And Quality Control

Statistical Techniques in Life testing  Reliability  Sampling Theory and Quality Control PDF
Author: B. N. Pandey
Publisher: Narosa Publishing House
Release: 2007
Size: 46.89 MB
Format: PDF, ePub, Docs
Category : Mathematics
Languages : en
Pages : 215
View: 2060

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Statistical Techniques in Life-Testing, Reliability, Sampling Theory and Quality Control covers recent research in the unified branches of theoretical and applied Statistics on common platform. These generally involve the concept of probability theory, prior information (Bayes and Minimax both), distribution theory, order statistics censoring, Truncation, loss and risk function, conditional distribution (sufficiency and complete sufficiency), sampling techniques using non-response techniques and Post- Stratification etc. Log-normal models in survival data, Bernoulli's trials, regression, ration under non necessarily model, CUSUM techniques in control charts, and reliability models with wide applications in other disciplines such as biology, mining and coal, industries, agriculture population, science and technology, medical sciences, research organizations, engineering, operation research, cancer institute, defense organizations, etc. are also discussed.

Applied Survival Analysis

Applied Survival Analysis PDF
Author: David W. Hosmer, Jr.
Publisher: John Wiley & Sons
Release: 2011-09-23
Size: 55.91 MB
Format: PDF, ePub, Docs
Category : Mathematics
Languages : en
Pages : 416
View: 1492

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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.

Statistical Meta Analysis With Applications

Statistical Meta Analysis with Applications PDF
Author: Joachim Hartung
Publisher: John Wiley & Sons
Release: 2011-09-20
Size: 36.78 MB
Format: PDF
Category : Medical
Languages : en
Pages : 248
View: 6840

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An accessible introduction to performing meta-analysis acrossvarious areas of research The practice of meta-analysis allows researchers to obtainfindings from various studies and compile them to verify and formone overall conclusion. Statistical Meta-Analysis with Applicationspresents the necessary statistical methodologies that allow readersto tackle the four main stages of meta-analysis: problemformulation, data collection, data evaluation, and data analysisand interpretation. Combining the authors' expertise on the topicwith a wealth of up-to-date information, this book successfullyintroduces the essential statistical practices for making thoroughand accurate discoveries across a wide array of diverse fields,such as business, public health, biostatistics, and environmentalstudies. Two main types of statistical analysis serve as the foundationof the methods and techniques: combining tests of effect size andcombining estimates of effect size. Additional topics coveredinclude: Meta-analysis regression procedures Multiple-endpoint and multiple-treatment studies The Bayesian approach to meta-analysis Publication bias Vote counting procedures Methods for combining individual tests and combining individualestimates Using meta-analysis to analyze binary and ordinal categoricaldata Numerous worked-out examples in each chapter provide the readerwith a step-by-step understanding of the presented methods. Allexercises can be computed using the R and SAS software packages,which are both available via the book's related Web site. Extensivereferences are also included, outlining additional sources forfurther study. Requiring only a working knowledge of statistics, StatisticalMeta-Analysis with Applications is a valuable supplement forcourses in biostatistics, business, public health, and socialresearch at the upper-undergraduate and graduate levels. It is alsoan excellent reference for applied statisticians working inindustry, academia, and government.

Design And Analysis Of Clinical Trials

Design and Analysis of Clinical Trials PDF
Author: Shein-Chung Chow
Publisher: Wiley-Interscience
Release: 2004
Size: 79.31 MB
Format: PDF, Mobi
Category : Mathematics
Languages : en
Pages : 729
View: 6540

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Presentation of principles and methodologies for various clinical trials, and a well-balanced summary of current regulatory requirements. Emphasizes real-life examples and illustrations from clinical case studies, as well as numerous references.

Statistical Shape Analysis

Statistical Shape Analysis PDF
Author: Ian L.. Dryden
Publisher: Wiley-Blackwell
Release: 1998-09-16
Size: 49.16 MB
Format: PDF, ePub
Category : Mathematics
Languages : en
Pages : 347
View: 4882

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Thos book involves methods for the geometrical study of random objects where location, rotation and scale information.