Censoring of a longitudinal outcome often occurs when data are collected in a biomedical study and where the interest is in the survival and or longitudinal experiences of a study population. In the setting considered herein, we encountered upper and lower censored data as the result of...

In a longitudinal study of biomarker data collected during a hospital stay, observations may be missing due to administrative reasons, the death of the subject or the subject's discharge from the hospital, resulting in non-ignorable missing data. Standard likelihood-based methods for the...

Longitudinal censoring is a common artifact when evaluating biomarkers and an obstacle to overcome when jointly investigating the longitudinal nature of the data and the impact on the survival prognoses of a study population. To fully appreciate the complexity of this scenario one has to devise...

The Genetic Markers of Inflammation Study (GenIMS) was conceived to investigate the role of severe sepsis, which is typically defined as system-wide multi-organ failure, on survival. One major hypothesis for this systemic collapse, and reduction in survival, is a cascade of pro-inflammatory and...

Many medical studies collect biomarker data to gain insight into the biological mechanisms underlying both acute and chronic diseases. These markers may be obtained at a single point in time to aid in the diagnosis of an illness or may be collected longitudinally to provide information on the...

Tree-structured survival analysis (TSSA) is used to recursively detect covariate values that best divide the sample into subsequent subsets with respect to a time to event outcome. The result is a set of empirical classification groups, each of which identifies individuals with more homogeneous...

Survival analysis has been used to estimate underlying survival or failure probabilities and to estimate the effects of covariates on survival times. The Cox proportional hazards regression model is the most commonly used approach. However, in practical situations, the assumption of proportional...