Survival analysis, the analysis of failure time data, is a rapid developing area and a number
of books on the topic have been published in last twenty-five years. However, all of these …

Competing risks arise commonly in the analysis of cancer studies. Most common are the
competing risks of relapse and death in remission. These two risks are the primary reason …

We provide an overview of semiparametric models commonly used in survival analysis,
including proportional hazards model, proportional odds models and linear transformation …

Competing risks arise when a subject is exposed to many causes of failure. Data consist of
the time the subject failed and an indicator of which risk caused the subject to fail. Examples …

Marginal additive hazards models are considered for multivariate survival data in which
individuals may experience events of several types and there may also be correlation …

Advances in flow cytometry bioinformatics have resulted in a wide variety of clustering,
classification and visualization techniques. To objectively evaluate the performance of such …

High-dose chemotherapy followed by stem cell recovery, more commonly called a bone
marrow transplant, is a common treatment for a number of diseases. This article examines …

Passage through the digestive tract of frugivorous vertebrates likely influences seed
dormancy, time to germination, and/or future seedling growth. Effects of gut passage on …

The literature on the statistical analysis of interval-censored failure time data has grown
rapidly in last twenty years or so and among others, one relatively complete review is given …

Covariate measurement error has attracted extensive interest in survival analysis. Since
Prentice, a large number of inference methods have been developed to handle error-prone …