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

Synthesis, x-ray structure and anion binding properties of a cryptand-like hybrid calixpyrrole

The novel cryptand in/out-3, containing two tripyrrolemethane units briged by three 1,3- diisopropylidenbenzene arms was readily synthesized by a convergent three-step synthesis. It binds fluoride by inclusion with excellent selectivity with respect to a number of other tested anions. The structure of the free receptor and that of its fluoride complex were investigated in solution by NMR spectroscopy. The solid state X-ray structure of the free cryptand 3 was also determined

Nonuniversality of the dispersion interaction: analytic benchmarks for van der Waals energy functionals

We highlight the non-universality of the asymptotic behavior of dispersion forces, such that a sum of inverse sixth power contributions is often inadequate. We analytically evaluate the cross-correlation energy Ec between two pi-conjugated layers separated by a large distance D within the electromagnetically non-retarded Random Phase Approximation, via a tight-binding model. For two perfect semimetallic graphene sheets at T=0K we find Ec = C D^{-3}, in contrast to the "insulating" D^{-4} dependence predicted by currently accepted approximations. We also treat the case where one graphene layer is replaced by a thin metal, a model relevant to the exfoliation of graphite. Our general considerations also apply to nanotubes, nanowires and layered metals.Comment: 4 pages, 0 fig

You Look So Good for Your Age: First Steps of Creating a Measure of Daily Experiences of Ageism

Ageism refers to the combined stereotypes, prejudice, and discriminatory behaviors based on someone’s presumed age. Older adults are viewed with mixed (ambivalent) perceptions, such that they are viewed as being warm, but not very capable. Thus, there are multiple forms of ageism. Hostile ageism refers to the overtly negative ageism, whereas benevolent ageism refers to the seemingly kind form of ageism that still reinforces negative stereotypes (e.g., giving your seat to someone, assuming they are fragile or weak). Most research on ageism has referred to attitudes, both inter-and intra-group, directed toward older adults and aging. This work has found that hostile attitudes and views of aging predict worse health and well-being, totaling a cost of up to $63 billion per year (Levy et al., 2020). However, little research has examined how ageism is experienced across a lifespan sample (younger, middle aged, and older adults) and presently no current research has examined how ageism is experienced in a daily diary method. The goal of our research is to create a way to measure daily experiences of ageism to use in a future daily diary study

Simulating avian species and foraging group responses to fuel reduction treatments in coniferous forests

Over a century of fire suppression activities have altered the structure and composition of mixed conifer forests throughout the western United States. In the absence of fire, fuels have accumulated in these forests causing concerns over the potential for catastrophic wildfires. Fuel reduction treatments are being used on federal and state lands to reduce the threat of wildfire by mechanically removing biomass. Although these treatments result in a reduction in fire hazard, their impact on wildlife is less clear. We use a multi-species occupancy modeling approach to build habitat-suitability models for 46 upland forest birds found in the Lake Tahoe Basin in the Sierra Nevada based on forest structure and abiotic variables. Using a Bayesian hierarchical framework, we predict species-specific and community-level responses to changes in forest structure and make inferences about responses of important avian foraging guilds. Disparities within and among foraging group responses to canopy cover, tree size and shrub cover emphasized the complexities in managing forests to meet biodiversity goals. Based on our species-specific model results, we predicted changes in species richness and community similarity under forest prescriptions representing three management practices: no active management, a typical fuel reduction treatment that emphasizes spacing between trees, and a thinning prescription that creates structural heterogeneity. Simulated changes to structural components of the forest analogous to management practices to reduce fuel loads clearly affected foraging groups differentially despite variability in responses within guilds. Although species richness was predicted to decrease slightly under both simulated fuels reduction treatments, the prescription that incorporated structural heterogeneity retained marginally higher species richness. The composition of communities supported by different management alternatives was influenced by urbanization and management practice, emphasizing the importance of creating heterogeneity at the landscape scale

Enhanced dispersion interaction between quasi-one dimensional conducting collinear structures

Recent investigations have highlighted the failure of a sum of $R^{-6}$ terms to represent the dispersion interaction in parallel metallic, anisotropic, linear or planar nanostructures [J. F. Dobson, A. White, and A. Rubio, Phys. Rev. Lett. 96, 073201 (2006) and references therein]. By applying a simple coupled plasmon approach and using electron hydrodynamics, we numerically evaluate the dispersion (non-contact van der Waals) interaction between two conducting wires in a collinear pointing configuration. This case is compared to that of two insulating wires in an identical geometry, where the dispersion interaction is modelled both within a pairwise summation framework, and by adding a pinning potential to our theory leading to a standard oscillator-type model of insulating dielectric behavior. Our results provide a further example of enhanced dispersion interaction between two conducting nanosystems compared to the case of two insulating ones. Unlike our previous work, this calculation explores a region of relatively close coupling where, although the electronic clouds do not overlap, we are still far from the asymptotic region where a single power law describes the dispersion energy. We find that strong differences in dispersion attraction between metallic and semiconducting / insulating cases persist into this non-asymptotic region. While our theory will need to be supplemented with additional short-ranged terms when the electronic clouds overlap, it does not suffer from the short-distance divergence exhibited by purely asymptotic theories, and gives a natural saturation of the dispersion energy as the wires come into contact.Comment: 10 pages, 5 figures. Added new extended numerical calculations, new figures, extra references and heavily revised tex

Creation and characterization of vortex clusters in atomic Bose-Einstein condensates

We show that a moving obstacle, in the form of an elongated paddle, can create vortices that are dispersed, or induce clusters of like-signed vortices in 2D Bose-Einstein condensates. We propose new statistical measures of clustering based on Ripley's K-function which are suitable to the small size and small number of vortices in atomic condensates, which lack the huge number of length scales excited in larger classical and quantum turbulent fluid systems. The evolution and decay of clustering is analyzed using these measures. Experimentally it should prove possible to create such an obstacle by a laser beam and a moving optical mask. The theoretical techniques we present are accessible to experimentalists and extend the current methods available to induce 2D quantum turbulence in Bose-Einstein condensates.Comment: 9 pages, 9 figure

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 review of published analyses of case-cohort studies and recommendations for future reporting.

The case-cohort study design combines the advantages of a cohort study with the efficiency of a nested case-control study. However, unlike more standard observational study designs, there are currently no guidelines for reporting results from case-cohort studies. Our aim was to review recent practice in reporting these studies, and develop recommendations for the future. By searching papers published in 24 major medical and epidemiological journals between January 2010 and March 2013 using PubMed, Scopus and Web of Knowledge, we identified 32 papers reporting case-cohort studies. The median subcohort sampling fraction was 4.1% (interquartile range 3.7% to 9.1%). The papers varied in their approaches to describing the numbers of individuals in the original cohort and the subcohort, presenting descriptive data, and in the level of detail provided about the statistical methods used, so it was not always possible to be sure that appropriate analyses had been conducted. Based on the findings of our review, we make recommendations about reporting of the study design, subcohort definition, numbers of participants, descriptive information and statistical methods, which could be used alongside existing STROBE guidelines for reporting observational studies.SJS was supported by the Medical Research Council www.mrc.ac.uk [Unit Programme number MC_UU_12015/1]. IRW was supported by the Medical Research Council www.mrc.ac.uk [Unit Programme number U105260558]. MP, SGT and AMW were supported by the British Heart Foundation www.bhf.org.uk [grant number CH/12/2/29428].This is the final published version distributed under a Creative Commons Attribution License 2.0, which can also be viewed on the publisher's website at: http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.010117

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