Interval-Censored Time-to-Event Data

Preface The aim of this book is to present in a single volume an overview and latest developments in time-to-event interval-censored methods along with application of such methods. The book is divided into three parts. Part I provides an introduction and overview of time-to-event methods for interval-censored data. Methodology is presented in Part II. Applications and related software appear in Part III. Part I consists of two chapters. In Chapter 1, Sun and Li present an overview of recent developments, with attention to nonparametric estimation and comparison of survival functions, regression analysis, analysis of multivariate clustered- and analysis of competing risks interval-censored data. In Chapter 2, Yu and Hsu provide a review of models for interval-censored (IC) data, including: independent interval censorship models, the full likelihood model, various models for C1, C2, and MIC data as well as multivariate IC models. Part II consists of seven chapters (3-9). Chapters 3, 4 and 5 deal with interval-censored methods for current status data. In Chapter 3, Banerjee presents: likelihood based inference, more general forms of interval censoring, competing risks, smoothed estimators, inference on a grid, outcome misclassi- cation, and semiparametric models. In Chapter 4, Zhang presents regression analyses using the proportional hazards model, the proportional odds model, and a linear transformation model, as well as considering bivariate current status data with the proportional odds model. In Chapter 5, Kim, Kim, Nam and Kim develop statistical analysis methods for dependent current status data and utilize the R Package CSD to analyze such data--