Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1148550 | Journal of Statistical Planning and Inference | 2007 | 11 Pages |
Abstract
When analyzing incomplete longitudinal clinical trial data, it is often inappropriate to assume that the occurrence of missingness is at random, especially in cases where visits are entirely missed. We present a framework that simultaneously models multivariate incomplete longitudinal data and a non-ignorable missingness mechanism using a Bayesian approach. A criterion measure is presented for comparing models. We demonstrate the feasibility of the methodology through reanalysis of two of the longitudinal measures from a clinical trial of penicillamine treatment for scleroderma patients. We compare the results for univariate and bivariate, ignorable and non-ignorable missingness models.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
W. John Boscardin, Xiaohong Yan, Weng Kee Wong,