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.

"This book describes methods and software implementations for the analysis of interval-censored data. The authors present the theoretical background for all methods and apply the methods to real data sets. They also provide the R, SAS, and WinBUGS code for all the examples, enabling readers to modify the code and use the methods to solve their own practical problems. In addition, most of the data sets used in the text are available online."--Provided by publisher.

Abstract: Background Common variable immunodeficiency disorders (CVID) are a group of rare innate disorders characterized by specific antibody deficiency and increased rates of infections, comorbidities and mortality. The burden of CVID in Europe has not been previously estimated. We performed a retrospective analysis of the European Society for Immunodeficiencies (ESID) registry data on the subset of patients classified by their immunologist as CVID and treated between 2004 and 2014. The registered deaths and comorbidities were used to calculate the annual average age-standardized rates of Years of Life Lost to premature death (YLL), Years Lost to Disability (YLD) and Disability Adjusted Life Years (DALY=YLL + YLD). These outcomes were expressed as a rate per 105 of the CVID cohort (the individual disease burden), and of the general population (the societal disease burden). Results Data of 2700 patients from 23 countries were analysed. Annual comorbidity rates: bronchiectasis, 21.9%; autoimmunity, 23.2%; digestive disorders, 15.6%; solid cancers, 5.5%; lymphoma, 3.8%, exceeded the prevalence in the general population by a factor of 34.0, 7.6, 8.1, 2.4 and 32.6, respectively. The comorbidities of CVID caused 8722 (6069; 12,363) YLD/105 in this cohort, whereas 44% of disability burden was attributable to infections and bronchiectasis. The total individual burden of CVID was 36,785 (33,078, 41,380) DALY/105. With estimated CVID prevalence of ~ 1/ 25,000, the societal burden of CVID ensued 1.5 (1.3, 1.7) DALY/105 of the general population. In exploratory analysis, increased mortality was associated with solid tumor, HR (95% CI): 2.69 (1.10; 6.57) p = 0.030, lymphoma: 5.48 (2.36; 12.71) p .0001 and granulomatous-lymphocytic interstitial lung disease: 4.85 (1.63; 14.39) p = 0.005. Diagnostic delay (median: 4 years) was associated with a higher risk of death: 1.04 (1.02; 1.06) p = .0003, bronchiectasis: 1.03 (1.01; 1.04) p = .0001, solid tumor: 1.08 (1.04; 1.11) p .0001 and enteropathy: 1.02 (1.00; 1.05) p = .0447 and stayed unchanged over four decades (p = .228).br