Hutchinson–Gilford progeria syndrome (HGPS) is an extremely rare, fatal, segmental premature aging syndrome caused by a mutation in LMNA that produces the farnesylated aberrant lamin A protein, progerin. This multisystem disorder causes failure to thrive and accelerated atherosclerosis leading to early death. Farnesyltransferase inhibitors have ameliorated disease phenotypes in preclinical studies. Twenty-five patients with HGPS received the farnesyltransferase inhibitor lonafarnib for a minimum of 2 y. Primary outcome success was predefined as a 50% increase over pretherapy in estimated annual rate of weight gain, or change from pretherapy weight loss to statistically significant on-study weight gain. Nine patients experienced a ≥50% increase, six experienced a ≥50% decrease, and 10 remained stable with respect to rate of weight gain. Secondary outcomes included decreases in arterial pulse wave velocity and carotid artery echodensity and increases in skeletal rigidity and sensorineural hearing within patient subgroups. All patients improved in one or more of these outcomes. Results from this clinical treatment trial for children with HGPS provide preliminary evidence that lonafarnib may improve vascular stiffness, bone structure, and audiological status.

Hutchinson-Gilford progeria syndrome (HGPS) is a rare, segmental premature aging syndrome of accelerated atherosclerosis and early death from myocardial infarction or stroke. This study sought to establish comprehensive characterization of the fatal vasculopathy in HGPS and its relevance to normal aging. We performed cardiovascular assessments at a single clinical site on the largest prospectively studied cohort to date. Carotid-femoral pulse wave velocity was dramatically elevated (mean 13.00±3.83 m/s). Carotid duplex ultrasound echobrightness, assessed in predefined tissue sites as a measure of arterial wall density, was significantly greater than age- and gender-matched controls in the intima-media (P<0.02), near adventitia (P<0.003) and deep adventitia (P<0.01), as was internal carotid artery mean flow velocity (p<0.0001). Ankle-brachial indices were abnormal in 78% of patients. Effective disease treatments may be heralded by normalizing trends of these noninvasive cardiovascular measures. The data demonstrates that, along with peripheral vascular occlusive disease, accelerated vascular stiffening is an early and pervasive mechanism of vascular disease in HGPS. There is considerable overlap with cardiovascular changes of normal aging, which reinforces the view that defining mechanisms of cardiovascular disease in HGPS provides a unique opportunity to isolate a subset of factors influencing cardiovascular disease in the general aging population.

In multidimensional item response theory (MIRT), it is possible for the estimate of a subject's ability in some dimension to decrease after they have answered a question correctly. This paper investigates how and when this type of paradoxical result can occur. We demonstrate that many response models and statistical estimates can produce paradoxical results and that in the popular class of linearly compensatory models, maximum likelihood estimates are guaranteed to do so. In light of these findings, the appropriateness of multidimensional item response methods for assigning scores in high-stakes testing is called into question.

Summary This article develops a unified theoretical and computational framework for false discovery control in multiple testing of spatial signals. We consider both point-wise and cluster-wise spatial analyses, and derive oracle procedures which optimally control the false discovery rate, false discovery exceedance and false cluster rate, respectively. A data-driven finite approximation strategy is developed to mimic the oracle procedures on a continuous spatial domain. Our multiple testing procedures are asymptotically valid and can be effectively implemented using Bayesian computational algorithms for analysis of large spatial data sets. Numerical results show that the proposed procedures lead to more accurate error control and better power performance than conventional methods. We demonstrate our methods for analyzing the time trends in tropospheric ozone in eastern US.

The objective of this paper is to quantify the effect of correlation in false discovery rate analysis. Specifically, we derive approximations for the mean, variance, distribution and quantiles of the standard false discovery rate estimator for arbitrarily correlated data. This is achieved using a negative binomial model for the number of false discoveries, where the parameters are found empirically from the data. We show that correlation may increase the bias and variance of the estimator substantially with respect to the independent case, and that in some cases, such as an exchangeable correlation structure, the estimator fails to be consistent as the number of tests becomes large.

Diffusion tensor imaging (DTI) data differ fundamentally from most brain imaging data in that values at each voxel are not scalars but 3 ؋ 3 positive definite matrices also called diffusion tensors. Frequently, investigators simplify the data analysis by reducing the tensor to a scalar, such as fractional anisotropy (FA). New statistical methods are needed for analyzing vector and tensor valued imaging data. A statistical model is proposed for the principal eigenvector of the diffusion tensor based on the bipolar Watson distribution. Methods are presented for computing mean direction and dispersion of a sample of directions and for testing whether two samples of directions (e.g., same voxel across two groups of subjects) have the same mean. False discovery rate theory is used to identify voxels for which the two-sample test is significant. These methods are illustrated in a DTI data set collected to study reading ability. It is shown that comparison of directions reveals differences in gross anatomic structure that are invisible to FA. Magn Reson Med 53:1423-1431, 2005.

Comparison of five diffusion models with in vivo data by assessment of their generalization error, from the article by .

Let {f(t) : t ∈ T} be a smooth Gaussian random field over a parameter space T, where T may be a subset of Euclidean space or, more generally, a Riemannian manifold. We provide a general formula for the distribution of the height of a local maximum P{f(t0)>u∣t0 is a local maximum of f(t)} when f is non-stationary. Moreover, we establish asymptotic approximations for the overshoot distribution of a local maximum P{f(t0)>u+v∣t0 is a local maximum of f(t) and f(t0) > v} as v → ∞. Assuming further that f is isotropic, we apply techniques from random matrix theory related to the Gaussian orthogonal ensemble to compute such conditional probabilities explicitly when T is Euclidean or a sphere of arbitrary dimension. Such calculations are motivated by the statistical problem of detecting peaks in the presence of smooth Gaussian noise.