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The last book dedicated to this topic was published in 1978, and since then there have been many developments in survival analysis, Competing Risks, and in statistical methods in general. The subject is now drawing increasing interest from engineers and biologists. Written by an acknowledged expert, this book thoroughly examines the probability framework and statistical analysis of data of Competing Risks. With a dearth of modern treatments of the subject and the importance of its methods, Classical Competing Risks fills a long-standing gap in the literature with a carefully organized exposition filled with real data sets, numerous examples, and clear, readable prose.

Mathematical statistics --- Competing risks --- Failure time data analysis --- Temps entre défaillances, Analyse des --- 519.2 --- Analysis, Failure time data --- Data analysis, Failure time --- Failure analysis (Engineering) --- Survival analysis (Biometry) --- Risks, Competing --- Distribution (Probability theory) --- Estimation theory --- Random variables --- Competing risks. --- Failure time data analysis. --- Temps entre défaillances, Analyse des

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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."--Publisher's description.

Mathematical statistics --- Failure time data analysis --- Survival analysis (Biometry) --- Mortality --- Demography --- Mathematics --- Mathematical models --- #SBIB:314H160 --- #SBIB:303H520 --- #SBIB:314H220 --- Historical demography --- Social sciences --- Population --- Vital statistics --- Mortality, Law of --- Death --- Death (Biology) --- Analysis, Survival (Biometry) --- Survivorship analysis (Biometry) --- Biometry --- Analysis, Failure time data --- Data analysis, Failure time --- Failure analysis (Engineering) --- Competing risks --- Bevolkingsstudies: onderzoeks- en analysemethoden --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Mortaliteit: algemeen --- Failure time data analysis - Mathematics --- Survival analysis (Biometry) - Mathematics --- Mortality - Mathematical models --- Demography - Mathematics

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Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many survival methods are extensions of techniques used in linear regression and categorical data, while other aspects of this field are unique to survival data. This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis. Because explaining survival analysis requires more advanced mathematics than many other statistical topics, this book is organized with basic concepts and most frequently used procedures covered in earlier chapters, with more advanced topics near the end and in the appendices. A background in basic linear regression and categorical data analysis, as well as a basic knowledge of calculus and the R system, will help the reader to fully appreciate the information presented. Examples are simple and straightforward while still illustrating key points, shedding light on the application of survival analysis in a way that is useful for graduate students, researchers, and practitioners in biostatistics. Clearly illustrates concepts of survival analysis principles and analyzes actual survival data using R, in addition to including an appendix with a basic introduction to R Organized via basic concepts and most frequently used procedures, with advanced topics toward the end of the book and in appendices Includes multiple original data sets that have not appeared in other textbooks Dirk F. Moore is Associate Professor of Biostatistics at the Rutgers School of Public Health and the Rutgers Cancer Institute of New Jersey. He received a Ph.D. in biostatistics from the University of Washington in Seattle and, prior to joining Rutgers, was a faculty member in the Statistics Department at Temple University. He has published numerous papers on the theory and application of survival analysis and other biostatistics methods to clinical trials and epidemiology studies.

Statistics. --- Epidemiology. --- Biostatistics. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistical Theory and Methods. --- Survival analysis (Biometry) --- Failure time data analysis. --- Analysis, Failure time data --- Data analysis, Failure time --- Analysis, Survival (Biometry) --- Survivorship analysis (Biometry) --- Biometry --- Failure time data analysis --- Failure analysis (Engineering) --- Mathematical statistics --- Competing risks --- Statistical methods. --- Mathematical statistics. --- Diseases --- Public health --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Statistics . --- Biological statistics --- Biology --- Biometrics (Biology) --- Biostatistics --- Biomathematics --- R (Computer program language). --- GNU-S (Computer program language) --- Domain-specific programming languages --- Analyse de la survie (Biométrie) --- Temps entre défaillances, Analyse des

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Quantitative methods in social research --- Mathematical statistics --- Failure time data analysis --- Martingalen (Wiskunde) --- Martingales (Mathematics) --- Martingales (Mathematiques) --- Point processes --- Processus ponctuels --- Punctuele processussen --- Temps entre défaillances [Analyse des ] --- Tijden tussen defecten [Analyse van de ] --- Temps entre défaillances, Analyse des --- Martingales (Mathématiques) --- Point processes. --- Failure time data analysis. --- 519.246 --- AA / International- internationaal --- 303.0 --- Processes, Point --- Stochastic processes --- Analysis, Failure time data --- Data analysis, Failure time --- Failure analysis (Engineering) --- Competing risks --- Survival analysis (Biometry) --- Statistics of stochastic processes. Estimation of stochastic processes. Hypothesis testing. Statistics of point processes. Time series analysis. Auto-correlation. Regression --- Statistische technieken in econometrie. Wiskundige statistiek (algemene werken en handboeken). --- 519.246 Statistics of stochastic processes. Estimation of stochastic processes. Hypothesis testing. Statistics of point processes. Time series analysis. Auto-correlation. Regression --- Temps entre défaillances, Analyse des --- Martingales (Mathématiques) --- Statistische technieken in econometrie. Wiskundige statistiek (algemene werken en handboeken) --- Statistique mathématique --- Statistique mathématique --- Mathematical statistics. --- Analyse de survie (biométrie)

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#SBIB:303H4 --- #SBIB:303H520 --- 681.3*E --- AA / International- internationaal --- 654 --- 10.02.d --- 10.03.c --- 681.3*E Data --- Data --- Informatica in de sociale wetenschappen --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Informatieverwerking. Bureautica. --- Statistieken Demografische statistieken --- Actuariaat Constructie (Sterfte)tafels --- Failure time data analysis --- Survival analysis (Biometry) --- Analysis, Survival (Biometry) --- Survivorship analysis (Biometry) --- Biometry --- Analysis, Failure time data --- Data analysis, Failure time --- Failure analysis (Engineering) --- Mathematical statistics --- Competing risks --- Data processing --- Statistieken ; Demografische statistieken --- Actuariaat ; Constructie (Sterfte)tafels --- Informatieverwerking. Bureautica --- SAS (Computer file) --- Statistical analysis system --- SAS system --- Programming --- Survival Analysis --- Automatic Data Processing --- Data processing. --- SAS (Computer file). --- Failure time data analysis - Data processing --- Survival analysis (Biometry) - Data processing --- QA 76.755 - Computer Software. Handbook --- SAS --- Electronic data processing