This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Hsu, C.-H. Search for Related Content Social Bookmarking                   What's this?

Joint modelling of recurrence and progression of adenomas: a latent variable approach

Mel and Enid Zuckerman College of Public Health and Arizona Cancer Center, Tucson, AZ, USA, phsu{at}azcc.arizona.edu

In this paper, we treat the number of recurrent adenomatous polyps as a latent variable and then use a mixture distribution to model the number of observed recurrent adenomatous polyps. This approach is equivalent to zero-inflated Poisson regression, which is a method used to analyse count data with excess zeros. In a zero-inflated Poisson model, a count response variable is assumed to be distributed as a mixture of a Poisson distribution and a distribution with point mass of one at zero. In many cancer studies, patients often have variable follow-up. When the disease of interest is subject to late onset, ignoring the length of follow-up will underestimate the recurrence rate. In this paper, we modify zero-inflated Poisson regression through a weight function to incorporate the length of follow-up into analysis. We motivate, develop, and illustrate the methods described here with an example from a colon cancer study.

Key Words: latent variable • measurement error • mixture distribution • robust weight function • variable follow-up • zero-inflated poisson

CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?