title: Empirical likelihood-based confidence intervals for length-biased data.
abstract: Logistic or other constraints often preclude the possibility of conducting incident cohort studies. A feasible alternative in such cases is to conduct a cross-sectional prevalent cohort study for which we recruit prevalent cases, that is, subjects who have already experienced the initiating event, say the onset of a disease. When the interest lies in estimating the lifespan between the initiating event and a terminating event, say death for instance, such subjects may be followed prospectively until the terminating event or loss to follow-up, whichever happens first. It is well known that prevalent cases have, on average, longer lifespans. As such, they do not constitute a representative random sample from the target population; they comprise a biased sample. If the initiating events are generated from a stationary Poisson process, the so-called stationarity assumption, this bias is called length bias. The current literature on length-biased sampling lacks a simple method for estimating the margin of errors of commonly used summary statistics. We fill this gap by using the empirical likelihood-based confidence intervals by adapting this method to right-censored length-biased survival data. Both large and small sample behaviors of these confidence intervals are studied. We illustrate our method by using a set of data on survival with dementia, collected as part of the Canadian Study of Health and Aging.
authors.affiliation: Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA. jning@mdanderson.org
authors.forename: J
authors.initials: J
authors.lastname: Ning
authors.forename: J
authors.initials: J
authors.lastname: Qin
authors.forename: M
authors.initials: M
authors.lastname: Asgharian
authors.forename: Y
authors.initials: Y
authors.lastname: Shen
mesh_terms: D000544:Alzheimer Disease / Q000401:mortality; D002170:Canada; D015331:Cohort Studies; D003198:Computer Simulation; D016001:Confidence Intervals; D003430:Cross-Sectional Studies; D006801:Humans; D016013:Likelihood Functions; D016019:Survival Analysis