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

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.

Understanding the Continual Reassessment Method for Dose Finding Studies: An Overview for Non-Statisticians

The Continual Reassessment Method (CRM) has gained popularity since its proposal by O’Quigley et al. [1]. Many variations have been published and discussed in the statistical literature, but there has been little attention to making the design considerations accessible to non-statisticians. As a result, some clinicians or reviewers of clinical trials tend to be wary of the CRM due to safety concerns. This paper presents the CRM in a non-technical way, describing the original CRM with some of its modified versions. It also describes the specifications that define a CRM design, along with two simulated examples of CRMs for illustration.