Impact-induced acceleration by obstacles

We explore a surprising phenomenon in which an obstruction accelerates, rather than decelerates, a moving flexible object. It has been claimed that the right kind of discrete chain falling onto a table falls \emph{faster} than a free-falling body. We confirm and quantify this effect, reveal its complicated dependence on angle of incidence, and identify multiple operative mechanisms. Prior theories for direct impact onto flat surfaces, which involve a single constitutive parameter, match our data well if we account for a characteristic delay length that must impinge before the onset of excess acceleration. Our measurements provide a robust determination of this parameter. This supports the possibility of modeling such discrete structures as continuous bodies with a complicated constitutive law of impact that includes angle of incidence as an input.Comment: small changes and corrections, added reference

Multiple Imputation by Chained Equations (MICE): Implementation in Stata

Missing data are a common occurrence in real datasets. For epidemiological and prognostic factors studies in medicine, multiple imputation is becoming the standard route to estimating models with missing covariate data under a missing-at-random assumption. We describe ice, an implementation in Stata of the MICE approach to multiple imputation. Real data from an observational study in ovarian cancer are used to illustrate the most important of the many options available with ice. We remark briefly on the new database architecture and procedures for multiple imputation introduced in releases 11 and 12 of Stata

The estimation and use of predictions for the assessment of model performance using large samples with multiply imputed data.

Multiple imputation can be used as a tool in the process of constructing prediction models in medical and epidemiological studies with missing covariate values. Such models can be used to make predictions for model performance assessment, but the task is made more complicated by the multiple imputation structure. We summarize various predictions constructed from covariates, including multiply imputed covariates, and either the set of imputation-specific prediction model coefficients or the pooled prediction model coefficients. We further describe approaches for using the predictions to assess model performance. We distinguish between ideal model performance and pragmatic model performance, where the former refers to the model's performance in an ideal clinical setting where all individuals have fully observed predictors and the latter refers to the model's performance in a real-world clinical setting where some individuals have missing predictors. The approaches are compared through an extensive simulation study based on the UK700 trial. We determine that measures of ideal model performance can be estimated within imputed datasets and subsequently pooled to give an overall measure of model performance. Alternative methods to evaluate pragmatic model performance are required and we propose constructing predictions either from a second set of covariate imputations which make no use of observed outcomes, or from a set of partial prediction models constructed for each potential observed pattern of covariate. Pragmatic model performance is generally lower than ideal model performance. We focus on model performance within the derivation data, but describe how to extend all the methods to a validation dataset.Angela Wood part supported by MRC grant G0701619. Ian White from MRC _Biostatistics Unit with unit programme number U105260558This is the final version. It was first published by Wiley at http://onlinelibrary.wiley.com/doi/10.1002/bimj.201400004/abstract;jsessionid=144424FA52D50041821329D8A7741BFD.f02t0

Anxiety characteristics in individuals with Williams syndrome

BACKGROUND: Williams syndrome anxiety research predominantly focuses on disorder prevalence and symptomatology, categorised using standardised mental health classifications. However, the use of these assessments may not fully capture the phenotypic features of anxiety in Williams syndrome. In this study, we examined characteristics of anxiety using a formulation framework. METHOD: A semi-structured interview was conducted with thirteen parents of individuals with Williams syndrome (median age: 19, age range: 12-45, 8 females). RESULTS: Various anxiety triggers were reported, including anxiety triggered by phobias, uncertainty and negative emotions in others. The range of described behaviours was diverse with both avoidant and active coping strategies for anxiety management reported. CONCLUSIONS: Many of the characteristics described were consistent with findings in the intellectual disability and typically developing literature, although novel information was identified. The study demonstrates the utility of a formulation framework to explore anxiety characteristics in atypical populations and has outlined new avenues for research

EDITORIAL Water, water, every where, but rarely any drop to drink

I would like to give all readers a very warm welcome to 2014 and the first issue of the tenth volume of Metabolomics. As you may be able to work out: the front cover is a celebration of this achievement, and I thank my colleague Dr Steve O’Hagan for his artistry. I am delighted that the journal is in such good shape and this is due to the excellent papers that are submitted and published, and of course the very valuable reviewing that many of you do. Metabolomics has an excellent Editorial board and I am also very grateful to them for their valuable support. You may be pondering over the title, so let me explain. Whilst I have somewhat moderated the quote from ‘‘The Rime of the Ancient Mariner’ ’ by Samuel Taylor Coleridge written in 1797–1798, the water does not refer to any liquid substance per se, nor does the drinking to the ‘dryathlon 1 ’ that I did early last year and will be doing so again to combat any Christmas excesses. Rather the water is an analogy to data—both metabolomics and metadata. Water here is a very apt comparison, as it seems rather ironic that a typical metabolomics experiments generates so much data that it is often referred to in terms of natural disasters—like data floods, data torrents or even data tsunamis. Yet even more ironic that very rarely do we make publicly available the metabolomics data (raw or processed) and the associated metadata with our publications. These metadata are as important as the metabolite data as these refer to the data about the data. We mainly think of these in terms of the important traits or features that we may want to predict, but these also refer to our experimental protocols that ar

Generic, simple risk stratification model for heart valve surgery

BACKGROUND: Heart valve surgery has an associated in-hospital mortality rate of 4% to 8%. This study aims to develop a simple risk model to predict the risk of in-hospital mortality for patients undergoing heart valve surgery to provide information to patients and clinicians and to facilitate institutional comparisons.METHODS AND RESULTS: Data on 32 839 patients were obtained from the Society of Cardiothoracic Surgeons of Great Britain and Ireland on patients who underwent heart valve surgery between April 1995 and March 2003. Data from the first 5 years (n=16 679) were used to develop the model; its performance was evaluated on the remaining data ( n=16 160). The risk model presented here is based on the combined data. The overall in-hospital mortality was 6.4%. The risk model included, in order of importance (all P < 0.01), operative priority, age, renal failure, operation sequence, ejection fraction, concomitant tricuspid valve surgery, type of valve operation, concomitant CABG surgery, body mass index, preoperative arrhythmias, diabetes, gender, and hypertension. The risk model exhibited good predictive ability (Hosmer-Lemeshow test, P=0.78) and discriminated between high- and low-risk patients reasonably well (receiver-operating characteristics curve area, 0.77). CONCLUSIONS: This is the first risk model that predicts in-hospital mortality for aortic and/or mitral heart valve patients with or without concomitant CABG. Based on a large national database of heart valve patients, this model has been evaluated successfully on patients who had valve surgery during a subsequent time period. It is simple to use, includes routinely collected variables, and provides a useful tool for patient advice and institutional comparisons

Multiple imputation for an incomplete covariate that is a ratio.

We are concerned with multiple imputation of the ratio of two variables, which is to be used as a covariate in a regression analysis. If the numerator and denominator are not missing simultaneously, it seems sensible to make use of the observed variable in the imputation model. One such strategy is to impute missing values for the numerator and denominator, or the log-transformed numerator and denominator, and then calculate the ratio of interest; we call this 'passive' imputation. Alternatively, missing ratio values might be imputed directly, with or without the numerator and/or the denominator in the imputation model; we call this 'active' imputation. In two motivating datasets, one involving body mass index as a covariate and the other involving the ratio of total to high-density lipoprotein cholesterol, we assess the sensitivity of results to the choice of imputation model and, as an alternative, explore fully Bayesian joint models for the outcome and incomplete ratio. Fully Bayesian approaches using Winbugs were unusable in both datasets because of computational problems. In our first dataset, multiple imputation results are similar regardless of the imputation model; in the second, results are sensitive to the choice of imputation model. Sensitivity depends strongly on the coefficient of variation of the ratio's denominator. A simulation study demonstrates that passive imputation without transformation is risky because it can lead to downward bias when the coefficient of variation of the ratio's denominator is larger than about 0.1. Active imputation or passive imputation after log-transformation is preferable

Correcting for optimistic prediction in small data sets.

The C statistic is a commonly reported measure of screening test performance. Optimistic estimation of the C statistic is a frequent problem because of overfitting of statistical models in small data sets, and methods exist to correct for this issue. However, many studies do not use such methods, and those that do correct for optimism use diverse methods, some of which are known to be biased. We used clinical data sets (United Kingdom Down syndrome screening data from Glasgow (1991-2003), Edinburgh (1999-2003), and Cambridge (1990-2006), as well as Scottish national pregnancy discharge data (2004-2007)) to evaluate different approaches to adjustment for optimism. We found that sample splitting, cross-validation without replication, and leave-1-out cross-validation produced optimism-adjusted estimates of the C statistic that were biased and/or associated with greater absolute error than other available methods. Cross-validation with replication, bootstrapping, and a new method (leave-pair-out cross-validation) all generated unbiased optimism-adjusted estimates of the C statistic and had similar absolute errors in the clinical data set. Larger simulation studies confirmed that all 3 methods performed similarly with 10 or more events per variable, or when the C statistic was 0.9 or greater. However, with lower events per variable or lower C statistics, bootstrapping tended to be optimistic but with lower absolute and mean squared errors than both methods of cross-validation

A Comparison of the Ovulation Method With the CUE Ovulation Predictor in Determining the Fertile Period

The purpose of this study was to compare the CUE Ovulation Predictor with the ovulation method in determining the fertile period. Eleven regularly ovulating women measured their salivary and vaginal electrical resistance (ER) with the CUE, observed their cervical-vaginal mucus, and measured their urine for a luteinizing hormone (LH) surge on a daily basis. Data from 21 menstrual cycles showed no statistical difference (T= 0.33, p= 0.63) between the CUE fertile period, which ranged from 5 to 10 days (mean = 6.7 days, SD = 1.6), and the fertile period of the ovulation method, which ranged from 4 to 9 days (mean = 6.5 days, SD = 2.0). The CUE has potential as an adjunctive device in the learning and use of natural family planning methods