Individual Differences in Women's Salivary Testosterone and Estradiol Following Sexual Activity in a Nonlaboratory Setting

Objectives: The current study evaluated women's salivary testosterone and estradiol levels before and after exposure to sexual stimuli in a U.S. sex club. Methods: Behavioral data and salivary samples were collected from 19 women during semistructured interviews. Results: Findings demonstrate substantial individual differences in the magnitude and direction of women's hormonal changes following sexual activity. Conclusions: In an age of individualized medicine, these findings highlight the need to better understand factors shaping variation in physiological responses to sexual activity. Findings contribute to a relatively small and contradictory literature on women's hormonal responses to sexual stimuli.     

A Statistical Framework for Hypothesis Testing in Real Data Comparison Studies

In computational sciences, including computational statistics, machine learning, and bioinformatics, it is often claimed in articles presenting new supervised learning methods that the new method performs better than existing methods on real data, for instance in terms of error rate. However, these claims are often not based on proper statistical tests and, even if such tests are performed, the tested hypothesis is not clearly defined and poor attention is devoted to the Type I and Type II errors. In the present article, we aim to fill this gap by providing a proper statistical framework for hypothesis tests that compare the performances of supervised learning methods based on several real datasets with unknown underlying distributions. After giving a statistical interpretation of ad hoc tests commonly performed by computational researchers, we devote special attention to power issues and outline a simple method of determining the number of datasets to be included in a comparison study to reach an adequate power. These methods are illustrated through three comparison studies from the literature and an exemplary benchmarking study using gene expression microarray data. All our results can be reproduced using R codes and datasets available from the companion website http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/compstud2013.     

A New Test for Short Memory in Long Memory Time Series

This article considers short memory characteristics in a long memory process. We derive new asymptotic results for the sample autocorrelation difference ratios. We used these results to develop a new portmanteau test that determines if short memory parameters are statistically significant. In simulations, the new test can detect short memory components more often than the Ljung-Box test when these short memory components are in fact within a long memory process. Interestingly, our test finds short memory autocorrelations in U.S. inflation rate data, whereas the Ljung-Box test fails to find these autocorrelations. Modeling these short memory autocorrelations of the inflation rate data leads to improved model accuracy and more precise prediction.     

Generalized Sobol sensitivity indices for dependent variables: numerical methods

The hierarchically orthogonal functional decomposition of any measurable function η of a random vector X=(X₁,?…?, X_p) consists in decomposing η(X) into a sum of increasing dimension functions depending only on a subvector of X. Even when X₁,?…?, X_p are assumed to be dependent, this decomposition is unique if the components are hierarchically orthogonal. That is, two of the components are orthogonal whenever all the variables involved in one of the summands are a subset of the variables involved in the other. Setting Y=η(X), this decomposition leads to the definition of generalized sensitivity indices able to quantify the uncertainty of Y due to each dependent input in X [Chastaing G, Gamboa F, Prieur C. Generalized Hoeffding–Sobol decomposition for dependent variables – application to sensitivity analysis. Electron J Statist. 2012;6:2420–2448]. In this paper, a numerical method is developed to identify the component functions of the decomposition using the hierarchical orthogonality property. Furthermore, the asymptotic properties of the components estimation is studied, as well as the numerical estimation of the generalized sensitivity indices of a toy model. Lastly, the method is applied to a model arising from a real-world problem.     

Small Area Estimation Using Estimated Population Level Auxiliary Data

Unit level linear mixed models are often used in small area estimation (SAE), and the empirical best linear unbiased prediction (EBLUP) is widely used for the estimation of small area means under such models. However, EBLUP requires population level auxiliary data, atleast area specific aggregated values. Sometimes population level auxiliary data is either not available or not consistent with the survey data. We describe a SAE method that uses estimated population auxiliary information. Empirical results show that proposed method for SAE produces an efficient set of small area estimates.     

Is Psychological Well-Being Linked to the Consumption of Fruit and Vegetables?

Little is known about the influence of people’s diet on their psychological well-being. This study provides evidence of a link between the consumption of fruit and vegetables and high well-being. In cross-sectional data, happiness and mental health rise in an approximately dose–response way with the number of daily portions of fruit and vegetables. Well-being peaks at approximately 7 portions per day. We document this relationship in three data sets, covering approximately 80,000 randomly selected British individuals, and for seven measures of well-being (life satisfaction, WEMWBS mental well-being, GHQ mental disorders, self-reported health, happiness, nervousness, and feeling low). The pattern is robust to adjustment for a large number of other demographic, social and economic variables. Reverse causality and problems of confounding remain possible. We discuss the strengths and weaknesses of our analysis, how government policy-makers might wish to react to it, and what kinds of further research—especially randomized trials—would be valuable.     

Bias and precision of measures of survival gain from right‐censored data

In cost‐effectiveness analyses of drugs or health technologies, estimates of life years saved or quality‐adjusted life years saved are required. Randomised controlled trials can provide an estimate of the average treatment effect; for survival data, the treatment effect is the difference in mean survival. However, typically not all patients will have reached the endpoint of interest at the close‐out of a trial, making it difficult to estimate the difference in mean survival. In this situation, it is common to report the more readily estimable difference in median survival. Alternative approaches to estimating the mean have also been proposed. We conducted a simulation study to investigate the bias and precision of the three most commonly used sample measures of absolute survival gain – difference in median, restricted mean and extended mean survival – when used as estimates of the true mean difference, under different censoring proportions, while assuming a range of survival patterns, represented by Weibull survival distributions with constant, increasing and decreasing hazards. Our study showed that the three commonly used methods tended to underestimate the true treatment effect; consequently, the incremental cost‐effectiveness ratio (ICER) would be overestimated. Of the three methods, the least biased is the extended mean survival, which perhaps should be used as the point estimate of the treatment effect to be inputted into the ICER, while the other two approaches could be used in sensitivity analyses. More work on the trade‐offs between simple extrapolation using the exponential distribution and more complicated extrapolation using other methods would be valuable. Copyright © 2015 John Wiley & Sons, Ltd.     

The role of dualities in arbitrating continuity and change in forms of organizing

A substantial body of literature on new forms of organizing has forecast the end of bureaucracy. More recent empirical studies, however, indicate that high‐performing organizations are adopting dual forms of organizing in which the controllability advantages associated with traditional forms work to complement and support the responsiveness attributes of new forms of organizing. The paradox is that, if organizations discard the key planning, co‐ordinating and direction‐setting mechanisms of traditional forms of organizing, they also remove the stabilizing dimensions of organizational form that are essential in periods of uncertainty and change. The challenge for organizations lies in learning how to manage the tensions or dualities between traditional and new forms of organizing, a process demanding the arbitration of continuity and change. This paper explores the concept of dualities and its salience in the management of organizing forms. First, the nature of dualities is explained; secondly, a set of characteristics is developed to describe the behaviour of dualities; and thirdly, suggestions are presented for arbitrating the tensions that exist in organizing form dualities. These three contributions are relevant because they signal the route to the effective creation and management of organizing form dualities, the benefit of which is the constructive combination of dynamic capabilities (underpinning innovation and responsiveness, the hallmarks of new forms of organizing) and operational capabilities (underpinning stability and efficiency, the hallmarks of traditional forms of organizing).