Two new methods to fit models for network meta-analysis with random inconsistency effects.

BACKGROUND: Meta-analysis is a valuable tool for combining evidence from multiple studies. Network meta-analysis is becoming more widely used as a means to compare multiple treatments in the same analysis. However, a network meta-analysis may exhibit inconsistency, whereby the treatment effect estimates do not agree across all trial designs, even after taking between-study heterogeneity into account. We propose two new estimation methods for network meta-analysis models with random inconsistency effects. METHODS: The model we consider is an extension of the conventional random-effects model for meta-analysis to the network meta-analysis setting and allows for potential inconsistency using random inconsistency effects. Our first new estimation method uses a Bayesian framework with empirically-based prior distributions for both the heterogeneity and the inconsistency variances. We fit the model using importance sampling and thereby avoid some of the difficulties that might be associated with using Markov Chain Monte Carlo (MCMC). However, we confirm the accuracy of our importance sampling method by comparing the results to those obtained using MCMC as the gold standard. The second new estimation method we describe uses a likelihood-based approach, implemented in the metafor package, which can be used to obtain (restricted) maximum-likelihood estimates of the model parameters and profile likelihood confidence intervals of the variance components. RESULTS: We illustrate the application of the methods using two contrasting examples. The first uses all-cause mortality as an outcome, and shows little evidence of between-study heterogeneity or inconsistency. The second uses "ear discharge" as an outcome, and exhibits substantial between-study heterogeneity and inconsistency. Both new estimation methods give results similar to those obtained using MCMC. CONCLUSIONS: The extent of heterogeneity and inconsistency should be assessed and reported in any network meta-analysis. Our two new methods can be used to fit models for network meta-analysis with random inconsistency effects. They are easily implemented using the accompanying R code in the Additional file 1. Using these estimation methods, the extent of inconsistency can be assessed and reported

Practical Software Quality: a Guide in Progress

Presentation intended to define software quality objectives when performing static code analysis on critical software to maximize assurance

Best-worst scaling improves measurement of first impressions

This research was supported by the Australian Research Council (ARC) Centre of Excellence for Cognition and its Disorders (CE110001021), an ARC Discovery Project grant to GR and CS (DP170104602) and an ARC Discovery Outstanding Researcher Award to GR (DP130102300). The datasets analysed during the current study are available from the corresponding author on reasonable request.Peer reviewedPublisher PD

Color, 3D simulated images with shapelets

We present a method to simulate color, 3-dimensional images taken with a space-based observatory by building off of the established shapelets pipeline. The simulated galaxies exhibit complex morphologies, which are realistically correlated between, and include, known redshifts. The simulations are created using galaxies from the 4 optical and near-infrared bands (B, V, i and z) of the Hubble Ultra Deep Field (UDF) as a basis set to model morphologies and redshift. We include observational effects such as sky noise and pixelization and can add astronomical signals of interest such as weak gravitational lensing. The realism of the simulations is demonstrated by comparing their morphologies to the original UDF galaxies and by comparing their distribution of ellipticities as a function of redshift and magnitude to wider HST COSMOS data. These simulations have already been useful for calibrating multicolor image analysis techniques and for better optimizing the design of proposed space telescopes.Comment: 14 pages, 15 figures, accepted to Astroparticle Physic

Planning guidelines for koala conservation and recovery: A guide to best planning practice

The information contained in the guide is a synthesis of four years research into the conservation and restoration of koala populations in fragmented landscapes of eastern Australia. The guidelines also capture a decade of practical research and planning experience by the Australian Koala Foundation in mapping koala habitat and developing koala conservation and management plans for local government areas in New South Wales. They draw on the collective knowledge of researchers who wanted to see their results put into action with practical outcomes for koala conservation

Implementing informative priors for heterogeneity in meta-analysis using meta-regression and pseudo data.

Many meta-analyses combine results from only a small number of studies, a situation in which the between-study variance is imprecisely estimated when standard methods are applied. Bayesian meta-analysis allows incorporation of external evidence on heterogeneity, providing the potential for more robust inference on the effect size of interest. We present a method for performing Bayesian meta-analysis using data augmentation, in which we represent an informative conjugate prior for between-study variance by pseudo data and use meta-regression for estimation. To assist in this, we derive predictive inverse-gamma distributions for the between-study variance expected in future meta-analyses. These may serve as priors for heterogeneity in new meta-analyses. In a simulation study, we compare approximate Bayesian methods using meta-regression and pseudo data against fully Bayesian approaches based on importance sampling techniques and Markov chain Monte Carlo (MCMC). We compare the frequentist properties of these Bayesian methods with those of the commonly used frequentist DerSimonian and Laird procedure. The method is implemented in standard statistical software and provides a less complex alternative to standard MCMC approaches. An importance sampling approach produces almost identical results to standard MCMC approaches, and results obtained through meta-regression and pseudo data are very similar. On average, data augmentation provides closer results to MCMC, if implemented using restricted maximum likelihood estimation rather than DerSimonian and Laird or maximum likelihood estimation. The methods are applied to real datasets, and an extension to network meta-analysis is described. The proposed method facilitates Bayesian meta-analysis in a way that is accessible to applied researchers. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd

A Universal Model of Global Civil Unrest

Civil unrest is a powerful form of collective human dynamics, which has led to major transitions of societies in modern history. The study of collective human dynamics, including collective aggression, has been the focus of much discussion in the context of modeling and identification of universal patterns of behavior. In contrast, the possibility that civil unrest activities, across countries and over long time periods, are governed by universal mechanisms has not been explored. Here, we analyze records of civil unrest of 170 countries during the period 1919-2008. We demonstrate that the distributions of the number of unrest events per year are robustly reproduced by a nonlinear, spatially extended dynamical model, which reflects the spread of civil disorder between geographic regions connected through social and communication networks. The results also expose the similarity between global social instability and the dynamics of natural hazards and epidemics.Comment: 8 pages, 3 figure