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Survival Analysis with Interval-Censored Data: A Practical Approach with Examples in R, SAS, and BUGS

Survival Analysis with Interval-Censored Data: A Practical Approach with Examples in R, SAS, and BUGS

Autorzy
Wydawnictwo Taylor & Francis Ltd
Data wydania 14/11/2017
Liczba stron 584
Forma publikacji książka w twardej oprawie
Poziom zaawansowania Dla profesjonalistów, specjalistów i badaczy naukowych
Język angielski
ISBN 9781420077476
Kategorie Prawdopodobieństwo i statystyka
389.55 PLN (z VAT)
$87.63 / €83.52 / £72.50 /
Produkt na zamówienie
Dostawa 5-6 tygodni
Ilość
Do schowka

Opis książki

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.


Arnost Komarek 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. "The authors succeeded in providing a practical text focused on the application of interval-censored data using various statistical software. Lastly, the authors wrote a text, which appeals to practitioners, because the text anticipates their needs and the foundational concepts and software to execute it."
~ Stephanie A. Besser


"All chapters spend a significant amount of time walking through examples with associated R code and results and do a very nice job explaining the initial CSE framework. Examples expand in complexity as the book progresses. As a biostatistician working in an academic setting, I am quite familiar with simulations used to construct new trials. However, the concept of CSE framework was brand new to me, and I think the strategies outlined in this book could definitely improve my approach to designing trial and analysis plans! This would also facilitate discussions with the clinical study team on how to proceed given our results. I would recommend this book to any clinical trial statistician who is interested in exploring simulations to better understand the implications of selected design and analysis strategies within their trials."
~Emily Dressler, Wake Forest School of Medicine


"To the best of my knowledge, this is the first book to provide a comprehensive treatment of the analysis of interval-censored data using common software such as SAS, R, and BUGS. I expect that applied statisticians and public health researchers with interest in statistical analysis of interval-censored data will find the book very useful. In addition, it seems well suited to be a reference book for a graduate-level survival analysis course. Overall, I enjoyed the presentation of the main idea of the methodology and the discussion of the strengths and limitations of approaches. If I had an opportunity to teach statistical methods for interval-censored data, I would select this book as a required text."
~ Minggen Lu, The American Statistician

Survival Analysis with Interval-Censored Data: A Practical Approach with Examples in R, SAS, and BUGS

Spis treści

List of Tables











List of Figures











Notation











Preface











I Introduction





Introduction





Survival concepts





Types of censoring





Right censoring





Interval and left censoring





Some special cases of interval censoring





Doubly interval censoring





Truncation





Ignoring interval censoring





Independent noninformative censoring





Independent noninformative right censoring





Independent noninformative interval censoring





Frequentist inference





Likelihood for interval-censored data





Maximum likelihood theory





Data sets and research questions





Homograft study





Breast cancer study





AIDS clinical trial





Sensory shelf life study





Survey on mobile phone purchases





Mastitis study





Signal Tandmobielr study





Censored data in R and SAS





R





SAS











Inference for right-censored data





Estimation of the survival function





Nonparametric maximum likelihood estimation





R solution





SAS solution





Comparison of two survival distributions





Review of signi_cance tests





R solution





SAS solution





Regression models





The proportional hazards model





Model description and estimation





Model checking





R solution





SAS solution





The accelerated failure time model





Model description and estimation





Model checking





R solution





SAS solution











II Frequentist methods for interval-censored data











Estimating the survival distribution





Nonparametric maximum likelihood





Estimation





Asymptotic results





R solution





SAS solution





Parametric modelling





Estimation





Model selection





Goodness of _t





R solution





SAS solution





Smoothing methods





Logspline density estimation





A smooth approximation to the density





Maximum likelihood estimation





R solution





Classical Gaussian mixture model





Penalized Gaussian mixture model





R solution





Concluding remarks











Comparison of two or more survival distributions





Nonparametric comparison of survival curves





The weighted log-rank test: derivation





The weighted log-rank test: linear form





The weighted log-rank test: derived from the linear





transformation model





The weighted log-rank test: the G family





The weighted log-rank test: significance testing





R solution





SAS solution





Sample size calculation





Concluding remarks











The proportional hazards model





Parametric approaches





Maximum likelihood estimation





R solution





SAS solution





Towards semiparametric approaches





The piecewise exponential baseline survival model





Model description and estimation





R solution





SAS solution





The SemiNonParametric approach





Model description and estimation





SAS solution





Spline-based smoothing approaches





Two spline-based smoothing approaches





R solution





SAS solution





Semiparametric approaches





Finkelstein's approach





Farrington's approach





The iterative convex minorant algorithm





The grouped proportional hazards model





Practical applications





Two examples





R solution





SAS solution





Multiple imputation approach





Data augmentation algorithm





Multiple imputation for interval-censored survival times





R solution





SAS solution





Model checking





Checking the PH model





R solution





SAS solution





Sample size calculation





Concluding remarks











The accelerated failure time model





The parametric model





Maximum likelihood estimation





R solution





SAS solution





The penalized Gaussian mixture model





Penalized maximum likelihood estimation





R solution





The SemiNonParametric approach





SAS solution





Model checking





Sample size calculation





Computational approach





SAS solution





Concluding remarks











Bivariate survival times





Nonparametric estimation of the bivariate survival function





The NPMLE of a bivariate survival function





R solution





SAS solution





Parametric models





Model description and estimation





R solution





SAS solution





Copula models





Background





Estimation procedures





R solution





Flexible survival models





The penalized Gaussian mixture model





SAS solution





Estimation of the association parameter





Measures of association





Estimating measures of association





R solution





SAS solution





Concluding remarks











Additional topics





Doubly interval-censored data





Background





R solution





Regression models for clustered data





Frailty models





R solution





SAS solution





A marginal approach to correlated survival times





Independence working model





SAS solution





A biplot for interval-censored data





The classical biplot





Extension to interval-censored observations





R solution





Concluding remarks











III Bayesian methods for interval-censored data











Bayesian concepts





Bayesian inference





Parametric versus nonparametric Bayesian approaches





Bayesian data augmentation





Markov chain Monte Carlo





Credible regions and contour probabilities





Selecting and checking the model





Sensitivity analysis





Nonparametric Bayesian inference





Bayesian nonparametric modelling of the hazard function





Bayesian nonparametric modelling of the distribution function





Bayesian software





WinBUGS and OpenBUGS





JAGS





R software





SAS procedures





Stan software





Applications for right-censored data





Parametric models





BUGS solution





SAS solution





Nonparametric Bayesian estimation of a survival curve





R solution





Semiparametric Bayesian survival analysis





BUGS solution





Concluding remarks











Bayesian estimation of the survival distribution for interval-censored observations





Bayesian parametric modelling





JAGS solution





SAS solution





Bayesian smoothing methods





Classical Gaussian mixture





R solution





Penalized Gaussian mixture





Nonparametric Bayesian estimation





The Dirichlet Process prior approach





R solution





The Dirichlet Process Mixture approach





R solution





Concluding remarks











The Bayesian proportional hazards model





The parametric PH model





JAGS solution





SAS solution





The PH model with exible baseline hazard





A Bayesian PH model with a smooth baseline hazard





R solution





A PH model with piecewise constant baseline hazard





R solution





The semiparametric PH model





Concluding remarks











The Bayesian accelerated failure time model





The Bayesian parametric AFT model





JAGS solution





SAS solution





AFT model with a classical Gaussian mixture as an error distribution





R solution





AFT model with a penalized Gaussian mixture as an error distribution





R solution





A Bayesian semiparametric AFT model





R solution





Concluding remarks











Additional topics





Hierarchical models





Parametric shared frailty models





JAGS solution





SAS solution





Flexible shared frailty models





R solution





Semiparametric shared frailty models





Multivariate models





Parametric bivariate models





JAGS solution





SAS solution





Bivariate copula models





Flexible bivariate models





R solution





Semiparametric bivariate models





R solution





The multivariate case





Doubly interval censoring





Parametric modelling of univariate DI-censored data





JAGS solution





Flexible modelling of univariate DI-censored data





R solution





Semiparametric modelling of univariate DI-censored data





R solution





Modelling of multivariate DI-censored data





Concluding remarks











IV Concluding part











Omitted topics and outlook





Omitted topics





Competing risks and multi-state models





Survival models with a cured subgroup





Multilevel models





Informative censoring





Interval-censored covariates





Joint longitudinal and survival models





Spatial-temporal models





Time points measured with error





Quantile regression





Outlook





V Appendices





A Data sets





A Homograft study





A AIDS clinical trial





A Survey on mobile phone purchases





A Mastitis study





A Signal Tandmobiel R study











B Distributions





B Log-normal LN(; _)





B Log-logistic LL(; _)





B Weibull W(; _)





B Exponential E(_)





B Rayleigh R(_)





B Gamma(; _)





B R solution





B SAS solution





B BUGS solution





B R and BUGS parametrizations











C Prior distributions





C Beta prior: Beta(_; _)





C Dirichlet prior: Dir (_)





C Gamma prior: G(_; _)





C Inverse gamma prior: IG(_; _)





C Wishart prior: Wishart(R; k)





C Inverse Wishart prior: Wishart(R; k)





C Link between Beta, Dirichlet and Dirichlet Process prior











D Description of selected R packages





D The icensBKL package





D The Icens package





D The interval package





D The survival package





D The logspline package





D The smoothSurv package





D The mixAK package





D The bayesSurv package





D The DPpackage package





D Other packages











E Description of selected SAS procedures





E PROC LIFEREG





E PROC RELIABILITY





E PROC ICLIFETEST





E PROC ICPHREG











F Technical details





F The Iterative Convex Minorant (ICM) algorithm





F Regions of possible support for bivariate interval-censored data





F The algorithm of Gentleman and Vandal ()





F The algorithm of Bogaerts and Lesa_re ()





F The height map algorithm of Maathuis ()





F Splines





F Polynomial _tting





F Polynomial splines





F Natural cubic splines





F Truncated power series





F B-splines





F M-splines and I-splines





F Penalized splines (P-splines)











References











Author Index











Subject Index

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