A general algorithm for covariance modeling of discrete data

We propose an algorithm that generalizes to discrete data any given covariance modeling algorithm originally intended for Gaussian responses, via a Gaussian copula approach. Covariance modeling is a powerful tool for extracting meaning from multivariate data, and fast algorithms for Gaussian data, such as factor analysis and Gaussian graphical models, are widely available. Our algorithm makes these tools generally available to analysts of discrete data and can combine any likelihood-based covariance modeling method for Gaussian data with any set of discrete marginal distributions. Previously, tools for discrete data were generally specific to one family of distributions or covariance modeling paradigm, or otherwise did not exist. Our algorithm is more flexible than alternate methods, takes advantage of existing fast algorithms for Gaussian data, and simulations suggest that it outperforms competing graphical modeling and factor analysis procedures for count and binomial data. We additionally show that in a Gaussian copula graphical model with discrete margins, conditional independence relationships in the latent Gaussian variables are inherited by the discrete observations. Our method is illustrated with a graphical model and factor analysis on an overdispersed ecological count dataset of species abundances

Untangling direct species associations from indirect mediator species effects with graphical models

Ecologists often investigate co‐occurrence patterns in multi‐species data in order to gain insight into the ecological causes of observed co‐occurrences. Apart from direct associations between the two species of interest, they may co‐occur because of indirect effects, where both species respond to another variable, whether environmental or biotic (e.g. a mediator species). A wide variety of methods are now available for modelling how environmental filtering drives species distributions. In contrast, methods for studying other causes of co‐occurence are much more limited. “Graphical” methods, which can be used to study how mediator species impact co‐occurrence patterns, have recently been proposed for use in ecology. However, available methods are limited to presence/absence data or methods assuming multivariate normality, which is problematic when analysing abundances. We propose Gaussian copula graphical models (GCGMs) for studying the effect of mediator species on co‐occurence patterns. GCGMs are a flexible type of graphical model which naturally accommodates all data types, for example binary (presence/absence), counts, as well as ordinal data and biomass, in a unified framework. Simulations demonstrate that GCGMs can be applied to a much broader range of data types than the methods currently used in ecology, and perform as well as or better than existing methods in many settings. We apply GCGMs to counts of hunting spiders, in order to visualise associations between species. We also analyse abundance data of New Zealand native forest cover (on an ordinal scale) to show how GCGMs can be used analyse large and complex datasets. In these data, we were able to reproduce known species relationships as well as generate new ecological hypotheses about species associations.F.K.C.H. is supported by an ANU cross‐disciplinary research grant. D.I.W. was supported by an Australian Research Council Future Fellowship (FT120100501). G.C.P. was supported by the Australia Postgraduate Award and ARC Discovery Project scheme (DP180103543). A.T.M. is supported by an Australia Research Council Discovery Grant (DP180100836). F.J.T. is supported from the Marsden Fast‐Start Fund and the Royal Society of New Zealand

CCNE1 and survival of patients with tubo-ovarian high-grade serous carcinoma: An Ovarian Tumor Tissue Analysis consortium study

BACKGROUND: Cyclin E1 (CCNE1) is a potential predictive marker and therapeutic target in tubo-ovarian high-grade serous carcinoma (HGSC). Smaller studies have revealed unfavorable associations for CCNE1 amplification and CCNE1 overexpression with survival, but to date no large-scale, histotype-specific validation has been performed. The hypothesis was that high-level amplification of CCNE1 and CCNE1 overexpression, as well as a combination of the two, are linked to shorter overall survival in HGSC. METHODS: Within the Ovarian Tumor Tissue Analysis consortium, amplification status and protein level in 3029 HGSC cases and mRNA expression in 2419 samples were investigated. RESULTS: High-level amplification (>8 copies by chromogenic in situ hybridization) was found in 8.6% of HGSC and overexpression (>60% with at least 5% demonstrating strong intensity by immunohistochemistry) was found in 22.4%. CCNE1 high-level amplification and overexpression both were linked to shorter overall survival in multivariate survival analysis adjusted for age and stage, with hazard stratification by study (hazard ratio [HR], 1.26; 95% CI, 1.08-1.47, p = .034, and HR, 1.18; 95% CI, 1.05-1.32, p = .015, respectively). This was also true for cases with combined high-level amplification/overexpression (HR, 1.26; 95% CI, 1.09-1.47, p = .033). CCNE1 mRNA expression was not associated with overall survival (HR, 1.00 per 1-SD increase; 95% CI, 0.94-1.06; p = .58). CCNE1 high-level amplification is mutually exclusive with the presence of germline BRCA1/2 pathogenic variants and shows an inverse association to RB1 loss. CONCLUSION: This study provides large-scale validation that CCNE1 high-level amplification is associated with shorter survival, supporting its utility as a prognostic biomarker in HGSC

Antipsychotic Deprescription for Older Adults in Long-term Care: The HALT Study

OBJECTIVES: Despite limited efficacy and significant safety concerns, antipsychotic medications are frequently used to treat behavioral and psychological symptoms of dementia (BPSD) in long-term residential care. This study evaluates the sustained reduction of antipsychotic use for BPSD through a deprescribing intervention and education of health care professionals. DESIGN: Repeated-measures, longitudinal, single-arm study. SETTING: Long-term residential care of older adults. PARTICIPANTS: Nursing staff from 23 nursing homes recruited 139 residents taking regular antipsychotic medication for ≥3 months, without primary psychotic illness, such as schizophrenia or bipolar disorder, or severe BPSD. INTERVENTION: An antipsychotic deprescribing protocol was established. Education of general practitioners, pharmacists, and residential care nurses focused on nonpharmacological prevention and management of BPSD. MEASUREMENTS: The primary outcome was antipsychotic use over 12-month follow-up; secondary outcomes were BPSD (Neuropsychiatric Inventory, Cohen-Mansfield Agitation Inventory, and social withdrawal) and adverse outcomes (falls, hospitalizations, and cognitive decline). RESULTS: The number of older adults on regular antipsychotics over 12 months reduced by 81.7% (95% confidence interval: 72.4-89.0). Withdrawal was not accompanied by drug substitution or a significant increase in pro-re-nata antipsychotic or benzodiazepine administration. There was no change in BPSD or in adverse outcomes. CONCLUSION: In a selected sample of older adults living in long-term residential care, sustained reduction in regular antipsychotic use is feasible without an increase of BPSD.status: publishe

Human lower leg muscles grow asynchronously

Muscle volume must increase substantially during childhood growth to generate the power required to propel the growing body. One unresolved but fundamental question about childhood muscle growth is whether muscles grow at equal rates; that is, if muscles grow in synchrony with each other. In this study, we used magnetic resonance imaging (MRI) and advances in artificial intelligence methods (deep learning) for medical image segmentation to investigate whether human lower leg muscles grow in synchrony. Muscle volumes were measured in 10 lower leg muscles in 208 typically developing children (eight infants aged less than 3 months and 200 children aged 5 to 15 years). We tested the hypothesis that human lower leg muscles grow synchronously by investigating whether the volume of individual lower leg muscles, expressed as a proportion of total lower leg muscle volume, remains constant with age. There were substantial age-related changes in the relative volume of most muscles in both boys and girls (p < 0.001). This was most evident between birth and five years of age but was still evident after five years. The medial gastrocnemius and soleus muscles, the largest muscles in infancy, grew faster than other muscles in the first five years. The findings demonstrate that muscles in the human lower leg grow asynchronously. This finding may assist early detection of atypical growth and allow targeted muscle-specific interventions to improve the quality of life, particularly for children with neuromotor conditions such as cerebral palsy.</p

Four principles for improved statistical ecology

1. Increasing attention has been drawn to the misuse of statistical methods over recent years, with particular concern about the prevalence of practices such as poor experimental design, cherry picking and inadequate reporting. These failures are largely unintentional and no more common in ecology than in other scientific disciplines, with many of them easily remedied given the right guidance. 2. Originating from a discussion at the 2020 International Statistical Ecology Conference, we show how ecologists can build their research following four guiding principles for impactful statistical research practices: (1) define a focussed research question, then plan sampling and analysis to answer it; (2) develop a model that accounts for the distribution and dependence of your data; (3) emphasise effect sizes to replace statistical significance with ecological relevance; and (4) report your methods and findings in sufficient detail so that your research is valid and reproducible. 3. These principles provide a framework for experimental design and reporting that guards against unsound practices. Starting with a well-defined research question allows researchers to create an efficient study to answer it, and guards against poor research practices that lead to poor estimation of the direction, magnitude, and uncertainty of ecological relationships, and to poor replicability. Correct and appropriate statistical models give sound conclusions. Good reporting practices and a focus on ecological relevance make results impactful and replicable. 4. Illustrated with two examples—an experiment to study the impact of disturbance on upland wetlands, and an observational study on blue tit colouring—this paper explains the rationale for the selection and use of effective statistical practices and provides practical guidance for ecologists seeking to improve their use of statistical methods.Publisher PDFPeer reviewe

Four principles for improved statistical ecology

Increasing attention has been drawn to the misuse of statistical methods over recent years, with particular concern about the prevalence of practices such as poor experimental design, cherry-picking and inadequate reporting. These failures are largely unintentional and no more common in ecology than in other scientific disciplines, with many of them easily remedied given the right guidance. Originating from a discussion at the 2020 International Statistical Ecology Conference, we show how ecologists can build their research following four guiding principles for impactful statistical research practices: 1. Define a focused research question, then plan sampling and analysis to answer it; 2. Develop a model that accounts for the distribution and dependence of your data; 3. Emphasise effect sizes to replace statistical significance with ecological relevance; 4. Report your methods and findings in sufficient detail so that your research is valid and reproducible. Listed in approximate order of importance, these principles provide a framework for experimental design and reporting that guards against unsound practices. Starting with a well-defined research question allows researchers to create an efficient study to answer it, and guards against poor research practices that lead to false positives and poor replicability. Correct and appropriate statistical models give sound conclusions, good reporting practices and a focus on ecological relevance make results impactful and replicable. Illustrated with an example from a recent study into the impact of disturbance on upland swamps, this paper explains the rationale for the selection and use of effective statistical practices and provides practical guidance for ecologists seeking to improve their use of statistical methods

Is Australia weird? A cross-continental comparison of biological, geological and climatological features

Australia’s distinctive biogeography means that it is sometimes considered an ecologically unique continent with biological and abiotic features that are not comparable to those observed in the rest of the world. This leaves some researchers unclear as to whether findings from Australia apply to systems elsewhere (or vice-versa), which has consequences for the development of ecological theory and the application of ecological management principles. We analyzed 594,612 observations spanning 85 variables describing global climate, soil, geochemistry, plants, animals, and ecosystem function to test if Australia is broadly different to the other continents and compare how different each continent is from the global mean. We found significant differences between Australian and global means for none of 15 climate variables, only seven of 25 geochemistry variables, three of 16 soil variables, five of 12 plant trait variables, four of 11 animal variables, and one of five ecosystem function variables. Seven of these differences remained significant when we adjusted for multiple hypothesis testing: high soil pH, high soil concentrations of sodium and strontium, a high proportion of nitrogen-fixing plants, low plant leaf nitrogen concentration, low annual production rate to birth in mammals, and low marine productivity. Our analyses reveal numerous similarities between Australia and Africa and highlight dissimilarities between continents in the northern vs. southern hemispheres. Australia ranked the most distinctive continent for 26 variables, more often than Europe (15 variables), Africa (13 variables), Asia (12 variables each), South America (11 variables) or North America (8 variables). Australia was distinctive in a range of soil conditions and plant traits, and a few bird and mammal traits, tending to sit at a more extreme end of variation for some variables related to resource availability. However, combined analyses revealed that, overall, Australia is not significantly more different to the global mean than Africa, South America, or Europe. In conclusion, while Australia does have some unique and distinctive features, this is also true for each of the other continents, and the data do not support the idea that Australia is an overall outlier in its biotic or abiotic characteristics