Sensitivity analysis of informatively coarsened data using pattern mixture models.
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.
Shardell
Michelle
M
Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA.
El-Kamary
Samer S
SS
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
D000410:Alanine Transaminase; D001663:Bilirubin