I decided to pull everything into an e-book to create a resource I can easily update without having to re-issue new editions. For the price of a cup of coffee you have a dynamic and timely resource for storytelling (patterns in datasets of interest), identifying practice gaps, research pipelines, clinical trial data, and reporting health policy and CMS released datasets.
Simon Rogers discusses a math-light appreciation of stories hidden in statistics and numbers. The short TEDx format contextualizes similar challenges inherent in medical content development. Play around with the data and send any questions. I will be creating the next issue to address getting your data ready for analyses...
Questioning anything and everything, to me, is punk rock.--Henry Rollins
Use of Brain MRI Atlases to Determine Boundaries of Age-Related Pathology: The Importance of Statistical Method
Neurodegenerative disease diagnoses may be supported by the comparison of an individual patient’s brain magnetic resonance image (MRI) with a voxel-based atlas of normal brain MRI. Most current brain MRI atlases are of young to middle-aged adults and parametric, e.g., mean ±standard deviation (SD); these atlases require data to be Gaussian. Brain MRI data, e.g., grey matter (GM) proportion images, from normal older subjects are apparently not Gaussian. We created a nonparametric and a parametric atlas of the normal limits of GM proportions in older subjects and compared their classifications of GM proportions in Alzheimer’s disease (AD) patients.
Here is a quick reminder of parametric and nonparametric tests. Statistical tests are either based on assumptions that follow a Guassian or normal distribution (parametric)--like the t test and analysis of variance or nonparametric tests. My favorite description of parametric tests refers to normal distributions as data points produced by taking sums or averages of samples drawn from a larger population. Nonparametric tests do not make assumptions about the population distribution. Often we consider a product (or geometric mean) rather than a sum expecting log-normal distributions rather than normal. Commonly used nonparametric tests rank the outcome variable from low to high and then analyze the ranks (Wilcoxon, Mann-Whitney test, and Kruskal-Wallis tests, for example).
To our knowledge, we have presented the first nonparametric brain MRI atlas. In conditions where there is increasing variability in brain structure, such as in old age, nonparametric brain MRI atlases may represent the limits of normal brain structure more accurately than parametric approaches. Therefore, we conclude that the statistical method used for construction of brain MRI atlases should be selected taking into account the population and aim under study. Parametric methods are generally robust for defining central tendencies, e.g., means, of brain structure. Nonparametric methods are advisable when studying the limits of brain structure in ageing and neurodegenerative disease.