R Markdown: Integrating A Reproducible Analysis Tool into Introductory Statistics

Nolan and Temple Lang argue that “the ability to express statistical computations is an es- sential skill.” A key related capacity is the ability to conduct and present data analysis in a way that another person can understand and replicate. The copy-and-paste workflow that is an artifact of antiquated user-interface design makes reproducibility of statistical analysis more difficult, especially as data become increasingly complex and statistical methods become increasingly sophisticated. R Markdown is a new technology that makes creating fully-reproducible statistical analysis simple and painless. It provides a solution suitable not only for cutting edge research, but also for use in an introductory statistics course. We present experiential and statistical evidence that R Markdown can be used effectively in introductory statistics courses, and discuss its role in the rapidly-changing world of statistical computation

A mean score method for sensitivity analysis to departures from the missing at random assumption in randomised trials

Most analyses of randomised trials with incomplete outcomes make untestable assumptions and should therefore be subjected to sensitivity analyses. However, methods for sensitivity analyses are not widely used. We propose a mean score approach for exploring global sensitivity to departures from missing at random or other assumptions about incomplete outcome data in a randomised trial. We assume a single outcome analysed under a generalised linear model. One or more sensitivity parameters, specified by the user, measure the degree of departure from missing at random in a pattern mixture model. Advantages of our method are that its sensitivity parameters are relatively easy to interpret and so can be elicited from subject matter experts; it is fast and non-stochastic; and its point estimate, standard error and confidence interval agree perfectly with standard methods when particular values of the sensitivity parameters make those standard methods appropriate. We illustrate the method using data from a mental health trial

Prevalence of purging at age 16 and associations with negative outcomes among girls in three community-based cohorts.

The comorbidity of purging behaviours, such as vomiting, inappropriate use of laxatives, diuretics or slimming medications, has been examined in literature. However, most studies do not include adolescents, individuals who purge in the absence of binge eating, or those purging at subclinical frequency. This study examines the prevalence of purging among 16-year-old girls across three countries and their association with substance use and psychological comorbidity

Male Eating Disorder Symptom Patterns and Health Correlates From 13 to 26 Years of Age

Objective: Research on the manifestations and health correlates of eating disorder symptoms among males is lacking. This study identified patterns of appearance concerns and eating disorder behaviors from adolescence through young adulthood and their health correlates. / Method: Participants were 7,067 males from the prospective Growing Up Today Study. Surveys from 1999 to 2007 (spanning ages 13−26 years) provided repeated measures data on muscularity and leanness concerns, eating disorder behaviors (purging, overeating, binge eating, use of muscle-building products), and health correlates (obesity, non-marijuana drug use, binge drinking, and depressive symptoms). / Results: Latent class analyses of observations at ages 13 to 15, 16 to 18, 19 to 22, and 23 to 26 years identified 1 large Asymptomatic class and 4 symptomatic patterns: Body Image Disturbance (high appearance concerns, low eating disorder behaviors; 1.0%−6.0% per age period); Binge Eating/Purging (binge eating and purging, use of muscle-building products, low appearance concerns; 0.1%−2.5%); Mostly Asymptomatic (low levels of muscularity concern, product use, and overeating; 3.5%−5.0%); and Muscularity Concerns (high muscularity concerns and use of products; 0.6%−1.0%). The Body Image Disturbance class was associated with high estimated prevalence of depressive symptoms. Males in the Binge Eating/Purging and Muscularity Concerns classes had high prevalence of binge drinking and drug use. Despite exhibiting modestly greater appearance concerns and eating disorder behaviors than the Asymptomatic class, being in the Mostly Asymptomatic class was prospectively associated with adverse health outcomes. / Conclusion: Results underscore the importance of measuring concerns about leanness, muscularity, and use of muscle-building products when assessing eating disorder presentations among males in research and clinical settings

The effects of supernovae on the dynamical evolution of binary stars and star clusters

In this chapter I review the effects of supernovae explosions on the dynamical evolution of (1) binary stars and (2) star clusters. (1) Supernovae in binaries can drastically alter the orbit of the system, sometimes disrupting it entirely, and are thought to be partially responsible for `runaway' massive stars - stars in the Galaxy with large peculiar velocities. The ejection of the lower-mass secondary component of a binary occurs often in the event of the more massive primary star exploding as a supernova. The orbital properties of binaries that contain massive stars mean that the observed velocities of runaway stars (10s - 100s km s$^{-1}$) are consistent with this scenario. (2) Star formation is an inherently inefficient process, and much of the potential in young star clusters remains in the form of gas. Supernovae can in principle expel this gas, which would drastically alter the dynamics of the cluster by unbinding the stars from the potential. However, recent numerical simulations, and observational evidence that gas-free clusters are observed to be bound, suggest that the effects of supernova explosions on the dynamics of star clusters are likely to be minimal.Comment: 16 pages, to appear in the 'Handbook of Supernovae', eds. Paul Murdin and Athem Alsabti. This version replaces an earlier version that contained several typo

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR

Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases

Part 1: CT characterisation of pancreatic neoplasms: a pictorial essay

The pancreas is a site of origin of a diverse range of benign and malignant tumours, and these are frequently detected, diagnosed and staged with computed tomography (CT). Knowledge of the typical appearance of these neoplasms as well as the features of locoregional invasion is fundamental for all general and abdominal radiologists. This pictorial essay aims to outline the characteristic CT appearances of the spectrum of pancreatic neoplasms, as well as important demographic and clinical information that aids diagnosis. The second article in this series addresses common mimics of pancreatic neoplasia

How do you say ‘hello’? Personality impressions from brief novel voices

On hearing a novel voice, listeners readily form personality impressions of that speaker. Accurate or not, these impressions are known to affect subsequent interactions; yet the underlying psychological and acoustical bases remain poorly understood. Furthermore, hitherto studies have focussed on extended speech as opposed to analysing the instantaneous impressions we obtain from first experience. In this paper, through a mass online rating experiment, 320 participants rated 64 sub-second vocal utterances of the word ‘hello’ on one of 10 personality traits. We show that: (1) personality judgements of brief utterances from unfamiliar speakers are consistent across listeners; (2) a two-dimensional ‘social voice space’ with axes mapping Valence (Trust, Likeability) and Dominance, each driven by differing combinations of vocal acoustics, adequately summarises ratings in both male and female voices; and (3) a positive combination of Valence and Dominance results in increased perceived male vocal Attractiveness, whereas perceived female vocal Attractiveness is largely controlled by increasing Valence. Results are discussed in relation to the rapid evaluation of personality and, in turn, the intent of others, as being driven by survival mechanisms via approach or avoidance behaviours. These findings provide empirical bases for predicting personality impressions from acoustical analyses of short utterances and for generating desired personality impressions in artificial voices