The survival function NPMLE for combined right-censored and length-biased right-censored failure time data: properties and applications.

Many cohort studies in survival analysis have imbedded in them subcohorts consisting of incident cases and prevalent cases. Instead of analysing the data from the incident and prevalent cohorts alone, there are surely advantages to combining the data from these two subcohorts. In this paper, we discuss a survival function nonparametric maximum likelihood estimator (NPMLE) using both length-biased right-censored prevalent cohort data and right-censored incident cohort data.

Survival Modelling For Data From Combined Cohorts: Opening the Door to Meta Survival Analyses and Survival Analysis using Electronic Health Records.

Non-parametric estimation of the survival function using observed failure time data depends on the underlying data generating mechanism, including the ways in which the data may be censored and/or truncated. For data arising from a single source or collected from a single cohort, a wide range of estimators have been proposed and compared in the literature. Often, however, it may be possible, and indeed advantageous, to combine and then analyze survival data that have been collected under different study designs.

A nonparametric test for equality of survival medians using right-censored prevalent cohort survival data.

The median is a robust summary commonly used for comparison between populations. The existing literature falls short in testing for equality of survival medians when the collected data do not form representative samples from their respective target populations and are subject to right censoring. Such data commonly occur in prevalent cohort studies with follow-up.

Stacked survival models for residual lifetime data.

When modelling the survival distribution of a disease for which the symptomatic progression of the associated condition is insidious, it is not always clear how to measure the failure/censoring times from some true date of disease onset. In a prevalent cohort study with follow-up, one approach for removing any potential influence from the uncertainty in the measurement of the true onset dates is through the utilization of only the residual lifetimes. As the residual lifetimes are measured from a well-defined screening date (prevalence day) to failure/censoring, these observed time durations are essentially error free.