COBRA Notification

• All announcements
• By author, series, or subject in COBRA
• By subject area in COBRA
• RSS Feed

Most Popular Papers

• View the most frequently downloaded papers

Institutions: Join COBRA

• Add your collection to COBRA

About COBRA

• COBRA
• About bepress

COVARIATE-ADJUSTED NONPARAMETRIC ANALYSIS OF MAGNETIC RESONANCE IMAGES USING MARKOV CHAIN MONTE CARLO

• Haley Hedlin, Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
• Brian S. Caffo, Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
• Ziyad Mahfoud, American University of Beirut, Department of Epidemiology and Population Health
• Susan Spear Bassett, Johns Hopkins University School of Medicine, Psychiatric Neuroimaging

Download the Paper Forward to a colleague

Abstract:

Permutation tests are useful for drawing inferences from imaging data because of their flexibility and ability to capture features of the brain that are difficult to capture parametrically. However, most implementations of permutation tests ignore important confounding covariates. To employ covariate control in a nonparametric setting we have developed a Markov chain Monte Carlo (MCMC) algorithm for conditional permutation testing using propensity scores. We present the first use of this methodology for imaging data. Our MCMC algorithm is an extension of algorithms developed to approximate exact conditional probabilities in contingency tables, logit, and log-linear models. An application of our non-parametric method to remove potential bias due to the observed covariates is presented.

Subject Area:

Medical Specialties

Suggested Citation:

Haley Hedlin, Brian S. Caffo, Ziyad Mahfoud, and Susan Spear Bassett, "COVARIATE-ADJUSTED NONPARAMETRIC ANALYSIS OF MAGNETIC RESONANCE IMAGES USING MARKOV CHAIN MONTE CARLO" (May 2009). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 187.
http://www.bepress.com/jhubiostat/paper187