title: Regression analysis for current status data using the EM algorithm.
abstract: We propose new expectation-maximization algorithms to analyze current status data under two popular semiparametric regression models: the proportional hazards (PH) model and the proportional odds (PO) model. Monotone splines are used to model the baseline cumulative hazard function in the PH model and the baseline odds function in the PO model. The proposed algorithms are derived by exploiting a data augmentation based on Poisson latent variables. Unlike previous regression work with current status data, our PH and PO model fitting methods are fast, flexible, easy to implement, and provide variance estimates in closed form. These techniques are evaluated using simulation and are illustrated using uterine fibroid data from a prospective cohort study on early pregnancy.
authors.affiliation: Department of Mathematical Sciences, Clemson University, Clemson, SC 29634 USA.
authors.forename: Christopher S
authors.initials: CS
authors.lastname: McMahan
authors.forename: Lianming
authors.initials: L
authors.lastname: Wang
authors.forename: Joshua M
authors.initials: JM
authors.lastname: Tebbs
mesh_terms: D001741:Black or African American / Q000706:statistics & numerical data; D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D005260:Female; D006801:Humans; D015994:Incidence; D007889:Leiomyoma / Q000000981:diagnostic imaging / Q000453:epidemiology*; D016013:Likelihood Functions; D009657:North Carolina / Q000453:epidemiology; D016012:Poisson Distribution; D011247:Pregnancy; D011252:Pregnancy Complications, Neoplastic / Q000000981:diagnostic imaging / Q000453:epidemiology*; D011261:Pregnancy Trimester, First; D016016:Proportional Hazards Models; D011446:Prospective Studies; D012044:Regression Analysis; D013714:Tennessee / Q000453:epidemiology; D013781:Texas / Q000453:epidemiology; D014463:Ultrasonography; D044465:White People / Q000706:statistics & numerical data
keywords: data augmentation; maximum likelihood; monotone splines; proportional hazards model; proportional odds model; semiparametric regression; survival analysis