Treatment of Girls and Boys with McCune-Albright Syndrome with Precocious Puberty - Update 2017

The most common endocrinopathy associated with McCune-Albright Syndrome (MAS) is peripheral precocious puberty (PP) which occurs far more often in girls than in boys. We will discuss the latest advancements in the treatment of precocious puberty in MAS that have been achieved during the past 10 years. However, due to the rarity of the condition and the heterogeneity of the disease, research in this field is limited particularly in regards to treatment in boys. In girls, a period of watchful waiting is recommended prior to initiating therapy due to extreme variability in the clinical course. This article will review in detail current pharmacologic treatment in girls, which typically consists of either inhibiting estrogen production or blocking estrogen action at the level of the end-organ. The two treatments with the most evidence at this time are Tamoxifen (which is an estrogen receptor modulator) and Letrozole (which is a 3rd generation aromatase inhibitor). This article will also review the current treatment strategies in boys which typically include using an androgen receptor blocker and an aromatase inhibitor. Due to the rarity of the condition, large multicenter collaborative studies are needed to further investigate efficacy and safety with the goal of establishing the gold standard for treatment of PP in children with MAS

Exploratory Analysis of Benchmark Experiments -- An Interactive Approach

The analysis of benchmark experiments consists in a large part of exploratory methods, especially visualizations. In Eugster et al. [2008] we presented a comprehensive toolbox including the bench plot. This plot visualizes the behavior of the algorithms on the individual drawn learning and test samples according to specific performance measures. In this paper we show an interactive version of the bench plot can easily uncover details and relations unseen with the static version

Bench Plot and Mixed Effects Models: First steps toward a comprehensive benchmark analysis toolbox

Benchmark experiments produce data in a very specific format. The observations are drawn from the performance distributions of the candidate algorithms on resampled data sets. In this paper we introduce new visualisation techniques and show how formal test procedures can be used to evaluate the results. This is the first step towards a comprehensive toolbox of exploratory and inferential analysis methods for benchmark experiments

How often are clinicians performing genital exams in children with disorders of sex development?

Background: We sought to determine the frequency with which genital exams (GEs) are performed in children with disorders of sex development (DSD) and ambiguous genitalia (AG) during routine visits to the pediatric endocrine clinic. Methods: Medical records of children with DSD and AG seen at one large academic center since 2007 were reviewed. Data analyzed included diagnosis, sex of rearing, age, initial or follow up visit, number of individuals present and sex of the pediatric endocrinologist. Repeated measures analysis was performed to evaluate associations between GEs and patient/physician factors. Results: Eighty-two children with DSD and AG who had a total of 632 visits were identified. Sex of rearing was female in 78% and the most common diagnosis was congenital adrenal hyperplasia (CAH) (68%). GEs were performed in 35.6% of visits. GEs were more likely in patients with male sex of rearing (odds ratio [OR] 17.81, p=0.006), during initial vs. follow-up visits (OR 5.99, p=0.012), and when the examining endocrinologist was female (OR 3.71, p=0.014). As patients aged, GEs were less likely (OR 0.76, p<0.0001). Conclusions: GEs were performed in approximately one-third of clinic visits in children with DSD and AG. Male sex of rearing, initial visits and female pediatric endocrinologist were associated with more frequent GEs

Probabilistic Archetypal Analysis

Archetypal analysis represents a set of observations as convex combinations of pure patterns, or archetypes. The original geometric formulation of finding archetypes by approximating the convex hull of the observations assumes them to be real valued. This, unfortunately, is not compatible with many practical situations. In this paper we revisit archetypal analysis from the basic principles, and propose a probabilistic framework that accommodates other observation types such as integers, binary, and probability vectors. We corroborate the proposed methodology with convincing real-world applications on finding archetypal winter tourists based on binary survey data, archetypal disaster-affected countries based on disaster count data, and document archetypes based on term-frequency data. We also present an appropriate visualization tool to summarize archetypal analysis solution better.Comment: 24 pages; added literature review and visualizatio

Spider-Man, the Child and the Trickster -- Archetypal Analysis in R

Archetypal analysis has the aim to represent observations in a multivariate data set as convex combinations of extremal points. This approach was introduced by Cutler and Breiman (1994); they defined the concrete problem, laid out the theoretical foundations and presented an algorithm written in Fortran, which is available on request. In this paper we present the R package archetypes which is available on the Comprehensive R Archive Network. The package provides an implementation of the archetypal analysis algorithm within R and different exploratory tools to analyze the algorithm during its execution and its final result. The application of the package is demonstrated on two examples

Weighted and Robust Archetypal Analysis

Archetypal analysis represents observations in a multivariate data set as convex combinations of a few extremal points lying on the boundary of the convex hull. Data points which vary from the majority have great influence on the solution; in fact one outlier can break down the archetype solution. This paper adapts the original algorithm to be a robust M-estimator and presents an iteratively reweighted least squares fitting algorithm. As required first step, the weighted archetypal problem is formulated and solved. The algorithm is demonstrated using both an artificial and a real world example

Measuring Concentration in Data with an Exogenous Order

Concentration measures order the statistical units under observation according to their market share. However, there are situations where an order according to an exogenous variable is more appropriate or even required. The present article introduces a generalized definition of market concentration and defines a corresponding concentration measure. It is shown that this generalized concept of market concentration satisfies the common axioms of (classical) concentration measures. In an application example, the proposed approach is compared with classical concentration measures; the data are transfer spendings of German Bundesliga soccer teams, the ``obvious'' exogenous order of the teams is the league ranking

From Spider-Man to Hero - Archetypal Analysis in R

Archetypal analysis has the aim to represent observations in a multivariate data set as convex combinations of extremal points. This approach was introduced by Cutler and Breiman (1994); they defined the concrete problem, laid out the theoretical foundations and presented an algorithm written in Fortran. In this paper we present the R package archetypes which is available on the Comprehensive R Archive Network. The package provides an implementation of the archetypal analysis algorithm within R and different exploratory tools to analyze the algorithm during its execution and its final result. The application of the package is demonstrated on two examples.

Having the Second Leg At Home - Advantage in the UEFA Champions League Knockout Phase?

In soccer knockout ties which are played in a two-legged format the team having the return match at home is usually seen as advantaged. For checking this common belief, we analyzed matches of the UEFA Champions League knockout phase since 1995. It is shown that the observed differences in frequencies of winning between teams first playing away and those which are first playing at home can be completely explained by their performances on the group stage and - more importantly - by the teams' general strength