title: Sensitivity analysis of informatively coarsened data using pattern mixture models.
abstract: We use the framework of coarsened data to motivate performing sensitivity analysis in the presence of incomplete data. To perform the sensitivity analysis, we specify pattern-mixture models to allow departures from the assumption of coarsening at random, a generalization of missing at random and independent censoring. We apply the concept of coarsening to address potential bias from missing data and interval-censored data in a randomized controlled trial of an herbal treatment for acute hepatitis. Computer code using SAS PROC NLMIXED for fitting the models is provided.
authors.affiliation: Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA.
authors.forename: Michelle
authors.initials: M
authors.lastname: Shardell
authors.forename: Samer S
authors.initials: SS
authors.lastname: El-Kamary
mesh_terms: D000410:Alanine Transaminase / Q000097:blood; D001663:Bilirubin / Q000097:blood; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005500:Follow-Up Studies; D006505:Hepatitis / Q000188:drug therapy; D006801:Humans; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic
chemical_list: D000410:Alanine Transaminase; D001663:Bilirubin