This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 312
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract - #308833
Title: Estimating Colorectal Cancer Screening in the Presence of Missing Data in a Population with a Resistant Subset and Multiple Observations
Author(s): Yolanda Cecile Hagar*+ and Laurel Beckett and Danielle Harvey and Joshua Fenton
Companies: University of California, Davis and University of California, Davis and University of California, Davis School of Medicine and University of California, Davis School of Medicine
Address: One Shields Ave, Davis, CA, 95616,
Keywords: multivariate survival analysis ; Bayesian survival analysis ; Gibbs sampler ; SEER/Medicare ; colorectal cancer screening
Abstract:

We use SEER/Medicare data to estimate colorectal cancer screening adherence rates. Analysis of this data set poses substantial challenges, as it is both left truncated and right censored, and individuals have zero to many screening observations. We have developed a Bayesian multivariate model to estimate time to screening. We assume that the number of screenings an individual will receive is a latent random variable. Conditioning on the number of screenings, we calculate likelihoods based on the observed data and estimate parameters with an adapted Gibbs sampler. Simulations have shown acceptable performance for realistic sample sizes. Preliminary results from SEER/Medicare data estimate that approximately half of individuals will never be screened. Age and gender significantly affect likelihood of screening, but to date results do not differ by race/ethnicity.


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