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The article outlines ten simple rules regarding how to effectively run a statistical analysis. It suggests that statistical methods should enable data to answer scientific questions. It mentions that signals always come with noise wherein it makes statistical interferences possible. It notes that statistical models help link data to goals by shifting attention to theoretical quantities of interest.

To evaluate evidence for motor impairment specificity in autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). Children completed performance-based assessment of motor functioning (Movement Assessment Battery for Children: MABC-2). Logistic regression models were used to predict group membership. In the models comparing typically developing and developmental disability (DD), all three MABC subscale scores were significantly negatively associated with having a DD. In the models comparing ADHD and ASD, catching and static balance items were associated with ASD group membership, with a 1 point decrease in performance increasing odds of ASD by 36 and 39 %, respectively. Impairments in motor skills requiring the coupling of visual and temporal feedback to guide and adjust movement appear specifically deficient in ASD. [ABSTRACT FROM AUTHOR]

We demonstrate that many current approaches for marginal modelling of correlated binary outcomes produce likelihoods that are equivalent to the copula-based models herein. These general copula models of underlying latent threshold random variables yield likelihood-based models for marginal fixed effects estimation and interpretation in the analysis of correlated binary data with exchangeable correlation structures. Moreover, we propose a nomenclature and set of model relationships that substantially elucidates the complex area of marginalised random-intercept models for binary data. A diverse collection of didactic mathematical and numerical examples are given to illustrate concepts. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Functional magnetic resonance imaging (fMRI) is a thriving field that plays an important role in medical imaging analysis, biological and neuroscience research and practice. This manuscript gives a didactic introduction to the statistical analysis of fMRI data using the R project, along with the relevant R code. The goal is to give statisticians who would like to pursue research in this area a quick tutorial for programming with fMRI data. References of relevant packages and papers are provided for those interested in more advanced analysis. [ABSTRACT FROM AUTHOR]

Abstract: The article “Ten ironic rules for non-statistical reviewers” (Friston, 2012) shares some commonly heard frustrations about the peer-review process that all researchers can identify with. Though we found the article amusing, we have some concerns about its description of a number of statistical issues. In this commentary we address these issues, as well as the premise of the article. [Copyright &y& Elsevier]

Introduction: In multiple sclerosis (MS), spinal cord imaging can help in diagnosis and follow-up evaluation. However, spinal cord magnetic resonance imaging (MRI) is technically challenging, and image quality, particularly in the axial plane, is typically poor compared to brain MRI. Because gradient-recalled echo (GRE) images might offer improved contrast resolution within the spinal cord at high magnetic field strength, both without and with a magnetization transfer prepulse, we compared them to T2-weighted fast-spin-echo (T2-FSE) images for the detection of MS lesions in the cervical cord at 3T. Methods: On a clinical 3T MRI scanner, we studied 62 MS cases and 19 healthy volunteers. Axial 3D GRE sequences were performed without and with off-resonance radiofrequency irradiation. To mimic clinical practice, all images were evaluated in conjunction with linked images from a sagittal short tau inversion recovery scan, which is considered the gold standard for lesion detection in MS. Two experienced observers recorded image quality, location and size of focal lesions, atrophy, swelling, and diffuse signal abnormality independently at first and then in consensus. Results: The number and volume of lesions detected with high confidence was more than three times as high on both GRE sequences compared to T2-FSE ( p < 0.0001). Approximately 5 % of GRE scans were affected by artifacts that interfered with image interpretation, not significantly different from T2W-FSE. Conclusions: Axial 3D GRE sequences are useful for MS lesion detection when compared to 2D T2-FSE sequences in the cervical spinal cord at 3T and should be considered when examining intramedullary spinal cord lesions. [ABSTRACT FROM AUTHOR]

Patients with type I myotonic muscular dystrophy (DM1) are at risk for sudden death due to atrioventricular conduction block. We sought to characterize the trends and predictors of time-dependent electrocardiographic (ECG) variations in patients with DM1. Seventy patients with DM1 underwent standard electrocardiography at first evaluation and routine and symptom prompted follow-up. Individual variations in ECG conduction intervals were assessed using spaghetti plots. Clinical predictors of conduction disease progression were assessed using multivariate random effects regression models of panel data clustered by patient and adjusted for heart rate. Substantial individual variability was noted in time-dependent changes in PR, QRS, and QTc intervals of patients with DM1 . Changes in the QTc interval were closely associated with prolongation of the QRS interval. Age, the presence of paroxysmal atrial flutter or fibrillation, and the number of cytosine-thymine-guanine (CTG) repeats were independent positive predictors of time-dependent PR and QRS prolongation during long-term follow-up. Female sex was negatively associated with PR prolongation but positively associated with QTc prolongation. Lower left ventricular ejection fraction was associated with greater QRS interval progression during long-term follow-up but was not predictive of PR interval progression. Patients with DM1 can develop rapid changes in cardiac conduction intervals. Paroxysmal atrial flutter or fibrillation, older age, and larger CTG expansions predict greater time-dependent PR and QRS interval prolongation and warrant particular attention in the arrhythmic evaluation of this high risk patient subset. (PACE 2011; 34:171-176) [ABSTRACT FROM AUTHOR]

The ideal observer (IO) employs complete knowledge of the available data statistics and sets an upper limit on observer performance on a binary classification task. However, the IO test statistic cannot be calculated analytically, except for cases where object statistics are extremely simple. Kupinski et al. have developed a Markov chain Monte Carlo (MCMC) based technique to compute the IO test statistic for, in principle, arbitrarily complex objects and imaging systems. In this work, we applied MCMC to estimate the IO test statistic in the context of myocardial perfusion SPECT (MPS). We modeled the imaging system using an analytic SPECT projector with attenuation, distant-dependent detector-response modeling and Poisson noise statistics. The object is a family of parameterized torso phantoms with variable geometric and organ uptake parameters. To accelerate the imaging simulation process and thus enable the MCMC IO estimation, we used discretized anatomic parameters and continuous uptake parameters in defining the objects. The imaging process simulation was modeled by precomputing projections for each organ for a finite number of discretely-parameterized anatomic parameters and taking linear combinations of the organ projections based on continuous sampling of the organ uptake parameters. The proposed method greatly reduces the computational burden and allows MCMC IO estimation for a realistic MPS imaging simulation. We validated the proposed IO estimation technique by estimating IO test statistics for a large number of input objects. The properties of the first- and second-order statistics of the IO test statistics estimated using the MCMC IO estimation technique agreed well with theoretical predictions. Further, as expected, the IO had better performance, as measured by the receiver operating characteristic (ROC) curve, than the Hotelling observer. This method is developed for SPECT imaging. However, it can be adapted to any linear imaging system. [ABSTRACT FROM AUTHOR]

Logically defined outcomes are commonly used in medical diagnoses and epidemiological research. When missing values in the original outcomes exist, the method of handling the missingness can have unintended consequences, even if the original outcomes are missing completely at random. In this note, we consider 2 binary original outcomes, which are missing completely at random. For estimating the prevalence of a logically defined "or" outcome, we discuss the properties of 4 estimators: the complete-case estimator, the available-case estimator, the maximum likelihood estimator (MLE), and a moment-based estimator. With the exception of the available-case case estimator, all the estimators are consistent. The MLE exhibits superior performance and should be generally adopted. [ABSTRACT FROM AUTHOR]

Summary The advent of modern data collection and processing techniques has seen the size, scale and complexity of data grow exponentially. A seminal step in leveraging these rich datasets for downstream inference is understanding the characteristics of the data which are repeatable—the aspects of the data that are able to be identified under duplicated analyses. Conflictingly, the utility of traditional repeatability measures, such as the intra‐class correlation coefficient, under these settings is limited. In recent work, novel data repeatability measures have been introduced in the context where a set of subjects are measured twice or more, including: fingerprinting, rank sums and generalisations of the intra‐class correlation coefficient. However, the relationships between, and the best practices among, these measures remains largely unknown. In this manuscript, we formalise a novel repeatability measure, discriminability. We show that it is deterministically linked with the intra‐class correlation coefficients under univariate random effect models and has the desired property of optimal accuracy for inferential tasks using multivariate measurements. Additionally, we overview and systematically compare existing repeatability statistics with discriminability, using both theoretical results and simulations. We show that the rank sum statistic is deterministically linked to a consistent estimator of discriminability. The statistical power of permutation tests derived from these measures are compared numerically under Gaussian and non‐Gaussian settings, with and without simulated batch effects. Motivated by both theoretical and empirical results, we provide methodological recommendations for each benchmark setting to serve as a resource for future analyses. We believe these recommendations will play an important role towards improving repeatability in fields such as functional magnetic resonance imaging, genomics, pharmacology and more. [ABSTRACT FROM AUTHOR]