We propose a new procedure for estimating the survival function of a time-to-event random variable under arbitrary patterns of censoring. Under mild smoothness assumptions, this procedure allows a unified approach to handling different kinds of censoring, while in many cases increasing...

A variety of complications arise when imperfect measurements, W, are observed in place of a true variable of interest, X. In the context of linear and non-linear regression models where X is a covariate, regression parameter estimators obtained when W is substituted for X may be substantially...

In many clinical trials related to diseases such as cancers and HIV, patients are treated by different combinations of therapies. This leads to two-stage designs, where patients are initially randomized to a primary therapy and then depending on disease remission and patients' consent, a...

In survival data analysis, the proportional hazards (PH), accelerated failure time(AFT), and proportional odds (PO) models are commonly used semiparametric models forthe comparison of survivability in subjects. These models assume that the survival curvesdo not cross. However, in some clinical...

In many clinical studies, researchers are interested in theeffects of a set of prognostic factors on the hazard of death from a specific disease even though patients may die from other competing causes. Often the time to relapse is right-censored for some individuals due to incomplete follow-up....

The accelerated failure time (AFT) model is a popular model for time-to-event data. It provides a useful alternative when the proportional hazards assumption is in question and it provides an intuitive linear regression interpretation where the logarithm of the survival time is regressed on the...

In many longitudinal studies, it is of interest to characterize the relationship between a time-to-event (e.g. survival) and time-dependent and time-independent covariates. Time-dependent covariates are generally observed intermittently and with error.For a single time-dependent covariate, a...

In longitudinal studies, data are often missing despite every attempt made to collect complete data. When the missingness is informative and hence not ignorable, it is generally difficult to analyze non-ignorable missing (NIM) data since the distributional assumptions about missing data are not...