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102. False discovery rate regression: an application to neural synchrony detection in primary visual cortex

Author

Scott, James G., Kelly, Ryan C., Smith, Matthew A., Zhou, Pengcheng, and Kass, Robert E.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Many approaches for multiple testing begin with the assumption that all tests in a given study should be combined into a global false-discovery-rate analysis. But this may be inappropriate for many of today's large-scale screening problems, where auxiliary information about each test is often available, and where a combined analysis can lead to poorly calibrated error rates within different subsets of the experiment. To address this issue, we introduce an approach called false-discovery-rate regression that directly uses this auxiliary information to inform the outcome of each test. The method can be motivated by a two-groups model in which covariates are allowed to influence the local false discovery rate, or equivalently, the posterior probability that a given observation is a signal. This poses many subtle issues at the interface between inference and computation, and we investigate several variations of the overall approach. Simulation evidence suggests that: (1) when covariate effects are present, FDR regression improves power for a fixed false-discovery rate; and (2) when covariate effects are absent, the method is robust, in the sense that it does not lead to inflated error rates. We apply the method to neural recordings from primary visual cortex. The goal is to detect pairs of neurons that exhibit fine-time-scale interactions, in the sense that they fire together more often than expected due to chance. Our method detects roughly 50% more synchronous pairs versus a standard FDR-controlling analysis. The companion R package FDRreg implements all methods described in the paper.

Published
2013

103. Expectation-maximization for logistic regression

Author

Scott, James G. and Sun, Liang

Subjects
Statistics - Computation, Mathematics - Statistics Theory, Statistics - Machine Learning
Abstract

We present a family of expectation-maximization (EM) algorithms for binary and negative-binomial logistic regression, drawing a sharp connection with the variational-Bayes algorithm of Jaakkola and Jordan (2000). Indeed, our results allow a version of this variational-Bayes approach to be re-interpreted as a true EM algorithm. We study several interesting features of the algorithm, and of this previously unrecognized connection with variational Bayes. We also generalize the approach to sparsity-promoting priors, and to an online method whose convergence properties are easily established. This latter method compares favorably with stochastic-gradient descent in situations with marked collinearity.

Published
2013

104. Nonparametric Bayesian testing for monotonicity

Author

Scott, James G., Shively, Thomas S., and Walker, Stephen G.

Subjects
Statistics - Methodology, Mathematics - Statistics Theory
Abstract

This paper studies the problem of testing whether a function is monotone from a nonparametric Bayesian perspective. Two new families of tests are constructed. The first uses constrained smoothing splines, together with a hierarchical stochastic-process prior that explicitly controls the prior probability of monotonicity. The second uses regression splines, together with two proposals for the prior over the regression coefficients. The finite-sample performance of the tests is shown via simulation to improve upon existing frequentist and Bayesian methods. The asymptotic properties of the Bayes factor for comparing monotone versus non-monotone regression functions in a Gaussian model are also studied. Our results significantly extend those currently available, which chiefly focus on determining the dimension of a parametric linear model.

Published
2013

111. Bayesian inference for logistic models using Polya-Gamma latent variables

Author

Polson, Nicholas G., Scott, James G., and Windle, Jesse

Subjects
Statistics - Methodology, Statistics - Computation, Statistics - Machine Learning
Abstract

We propose a new data-augmentation strategy for fully Bayesian inference in models with binomial likelihoods. The approach appeals to a new class of Polya-Gamma distributions, which are constructed in detail. A variety of examples are presented to show the versatility of the method, including logistic regression, negative binomial regression, nonlinear mixed-effects models, and spatial models for count data. In each case, our data-augmentation strategy leads to simple, effective methods for posterior inference that: (1) circumvent the need for analytic approximations, numerical integration, or Metropolis-Hastings; and (2) outperform other known data-augmentation strategies, both in ease of use and in computational efficiency. All methods, including an efficient sampler for the Polya-Gamma distribution, are implemented in the R package BayesLogit. In the technical supplement appended to the end of the paper, we provide further details regarding the generation of Polya-Gamma random variables; the empirical benchmarks reported in the main manuscript; and the extension of the basic data-augmentation framework to contingency tables and multinomial outcomes.

Published
2012

112. The partition problem: case studies in Bayesian screening for time-varying model structure

Author

Liu, Zesong, Windle, Jesse, and Scott, James G.

Subjects
Statistics - Applications
Abstract

This paper presents two case studies of data sets where the main inferential goal is to characterize time-varying patterns in model structure. Both of these examples are seen to be general cases of the so-called "partition problem," where auxiliary information (in this case, time) defines a partition over sample space, and where different models hold for each element of the partition. In the first case study, we identify time-varying graphical structure in the covariance matrix of asset returns from major European equity indices from 2006--2010. This structure has important implications for quantifying the notion of financial contagion, a term often mentioned in the context of the European sovereign debt crisis of this period. In the second case study, we screen a large database of historical corporate performance in order to identify specific firms with impressively good (or bad) streaks of performance.

Published
2011

113. An empirical test for Eurozone contagion using an asset-pricing model with heavy-tailed stochastic volatility

Author

Polson, Nicholas G. and Scott, James G.

Subjects
Quantitative Finance - Statistical Finance, Statistics - Applications
Abstract

This paper proposes an empirical test of financial contagion in European equity markets during the tumultuous period of 2008-2011. Our analysis shows that traditional GARCH and Gaussian stochastic-volatility models are unable to explain two key stylized features of global markets during presumptive contagion periods: shocks to aggregate market volatility can be sudden and explosive, and they are associated with specific directional biases in the cross-section of country-level returns. Our model repairs this deficit by assuming that the random shocks to volatility are heavy-tailed and correlated cross-sectionally, both with each other and with returns. The fundamental conclusion of our analysis is that great care is needed in modeling volatility if one wishes to characterize the relationship between volatility and contagion that is predicted by economic theory. In analyzing daily data, we find evidence for significant contagion effects during the major EU crisis periods of May 2010 and August 2011, where contagion is defined as excess correlation in the residuals from a factor model incorporating global and regional market risk factors. Some of this excess correlation can be explained by quantifying the impact of shocks to aggregate volatility in the cross-section of expected returns - but only, it turns out, if one is extremely careful in accounting for the explosive nature of these shocks. We show that global markets have time-varying cross-sectional sensitivities to these shocks, and that high sensitivities strongly predict periods of financial crisis. Moreover, the pattern of temporal changes in correlation structure between volatility and returns is readily interpretable in terms of the major events of the periods in question.

Published
2011

114. Default Bayesian analysis for multi-way tables: a data-augmentation approach

Author

Polson, Nicholas G. and Scott, James G.

Subjects
Statistics - Methodology, Mathematics - Statistics Theory, Statistics - Computation
Abstract

This paper proposes a strategy for regularized estimation in multi-way contingency tables, which are common in meta-analyses and multi-center clinical trials. Our approach is based on data augmentation, and appeals heavily to a novel class of Polya-Gamma distributions. Our main contributions are to build up the relevant distributional theory and to demonstrate three useful features of this data-augmentation scheme. First, it leads to simple EM and Gibbs-sampling algorithms for posterior inference, circumventing the need for analytic approximations, numerical integration, Metropolis--Hastings, or variational methods. Second, it allows modelers much more flexibility when choosing priors, which have traditionally come from the Dirichlet or logistic-normal family. For example, our approach allows users to incorporate Bayesian analogues of classical penalized-likelihood techniques (e.g. the lasso or bridge) in computing regularized estimates for log-odds ratios. Finally, our data-augmentation scheme naturally suggests a default strategy for prior selection based on the logistic-Z model, which is strongly related to Jeffreys' prior for a binomial proportion. To illustrate the method we focus primarily on the particular case of a meta-analysis/multi-center study (or a JxKxN table). But the general approach encompasses many other common situations, of which we will provide examples.

Published
2011

115. The Bayesian Bridge

Author

Polson, Nicholas G., Scott, James G., and Windle, Jesse

Subjects
Statistics - Methodology, Statistics - Computation, Statistics - Machine Learning
Abstract

We propose the Bayesian bridge estimator for regularized regression and classification. Two key mixture representations for the Bayesian bridge model are developed: (1) a scale mixture of normals with respect to an alpha-stable random variable; and (2) a mixture of Bartlett--Fejer kernels (or triangle densities) with respect to a two-component mixture of gamma random variables. Both lead to MCMC methods for posterior simulation, and these methods turn out to have complementary domains of maximum efficiency. The first representation is a well known result due to West (1987), and is the better choice for collinear design matrices. The second representation is new, and is more efficient for orthogonal problems, largely because it avoids the need to deal with exponentially tilted stable random variables. It also provides insight into the multimodality of the joint posterior distribution, a feature of the bridge model that is notably absent under ridge or lasso-type priors. We prove a theorem that extends this representation to a wider class of densities representable as scale mixtures of betas, and provide an explicit inversion formula for the mixing distribution. The connections with slice sampling and scale mixtures of normals are explored. On the practical side, we find that the Bayesian bridge model outperforms its classical cousin in estimation and prediction across a variety of data sets, both simulated and real. We also show that the MCMC for fitting the bridge model exhibits excellent mixing properties, particularly for the global scale parameter. This makes for a favorable contrast with analogous MCMC algorithms for other sparse Bayesian models. All methods described in this paper are implemented in the R package BayesBridge. An extensive set of simulation results are provided in two supplemental files., Comment: Supplemental files are available from the second author's website

Published
2011

116. On the half-Cauchy prior for a global scale parameter

Author

Polson, Nicholas G. and Scott, James G.

Subjects
Statistics - Methodology
Abstract

This paper argues that the half-Cauchy distribution should replace the inverse-Gamma distribution as a default prior for a top-level scale parameter in Bayesian hierarchical models, at least for cases where a proper prior is necessary. Our arguments involve a blend of Bayesian and frequentist reasoning, and are intended to complement the original case made by Gelman (2006) in support of the folded-t family of priors. First, we generalize the half-Cauchy prior to the wider class of hypergeometric inverted-beta priors. We derive expressions for posterior moments and marginal densities when these priors are used for a top-level normal variance in a Bayesian hierarchical model. We go on to prove a proposition that, together with the results for moments and marginals, allows us to characterize the frequentist risk of the Bayes estimators under all global-shrinkage priors in the class. These theoretical results, in turn, allow us to study the frequentist properties of the half-Cauchy prior versus a wide class of alternatives. The half-Cauchy occupies a sensible 'middle ground' within this class: it performs very well near the origin, but does not lead to drastic compromises in other parts of the parameter space. This provides an alternative, classical justification for the repeated, routine use of this prior. We also consider situations where the underlying mean vector is sparse, where we argue that the usual conjugate choice of an inverse-gamma prior is particularly inappropriate, and can lead to highly distorted posterior inferences. Finally, we briefly summarize some open issues in the specification of default priors for scale terms in hierarchical models.

Published
2011

117. Data augmentation for non-Gaussian regression models using variance-mean mixtures

Author

Polson, Nicholas G. and Scott, James G.

Subjects
Statistics - Methodology, Statistics - Computation
Abstract

We use the theory of normal variance-mean mixtures to derive a data-augmentation scheme for a class of common regularization problems. This generalizes existing theory on normal variance mixtures for priors in regression and classification. It also allows variants of the expectation-maximization algorithm to be brought to bear on a wider range of models than previously appreciated. We demonstrate the method on several examples, including sparse quantile regression and binary logistic regression. We also show that quasi-Newton acceleration can substantially improve the speed of the algorithm without compromising its robustness., Comment: Added a discussion of quasi-Newton acceleration

Published
2011

118. Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem

Author

Scott, James G. and Berger, James O.

Subjects
Mathematics - Statistics Theory
Abstract

This paper studies the multiplicity-correction effect of standard Bayesian variable-selection priors in linear regression. Our first goal is to clarify when, and how, multiplicity correction happens automatically in Bayesian analysis, and to distinguish this correction from the Bayesian Ockham's-razor effect. Our second goal is to contrast empirical-Bayes and fully Bayesian approaches to variable selection through examples, theoretical results and simulations. Considerable differences between the two approaches are found. In particular, we prove a theorem that characterizes a surprising aymptotic discrepancy between fully Bayes and empirical Bayes. This discrepancy arises from a different source than the failure to account for hyperparameter uncertainty in the empirical-Bayes estimate. Indeed, even at the extreme, when the empirical-Bayes estimate converges asymptotically to the true variable-inclusion probability, the potential for a serious difference remains., Comment: Published in at http://dx.doi.org/10.1214/10-AOS792 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Published
2010
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119. Parameter expansion in local-shrinkage models

Author

Scott, James G.

Subjects
Statistics - Computation, Statistics - Methodology
Abstract

This paper considers the problem of using MCMC to fit sparse Bayesian models based on normal scale-mixture priors. Examples of this framework include the Bayesian LASSO and the horseshoe prior. We study the usefulness of parameter expansion (PX) for improving convergence in such models, which is notoriously slow when the global variance component is near zero. Our conclusion is that parameter expansion does improve matters in LASSO-type models, but only modestly. In most cases this improvement, while noticeable, is less than what might be expected, especially compared to the improvements that PX makes possible for models very similar to those considered here. We give some examples, and we attempt to provide some intuition as to why this is so. We also describe how slice sampling may be used to update the global variance component. In practice, this approach seems to perform almost as well as parameter expansion. As a practical matter, however, it is perhaps best viewed not as a replacement for PX, but as a tool for expanding the class of models to which PX is applicable.

Published
2010

120. Good, great, or lucky? Screening for firms with sustained superior performance using heavy-tailed priors

Author

Polson, Nicholas G. and Scott, James G.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

This paper examines historical patterns of ROA (return on assets) for a cohort of 53,038 publicly traded firms across 93 countries, measured over the past 45 years. Our goal is to screen for firms whose ROA trajectories suggest that they have systematically outperformed their peer groups over time. Such a project faces at least three statistical difficulties: adjustment for relevant covariates, massive multiplicity, and longitudinal dependence. We conclude that, once these difficulties are taken into account, demonstrably superior performance appears to be quite rare. We compare our findings with other recent management studies on the same subject, and with the popular literature on corporate success. Our methodological contribution is to propose a new class of priors for use in large-scale simultaneous testing. These priors are based on the hypergeometric inverted-beta family, and have two main attractive features: heavy tails and computational tractability. The family is a four-parameter generalization of the normal/inverted-beta prior, and is the natural conjugate prior for shrinkage coefficients in a hierarchical normal model. Our results emphasize the usefulness of these heavy-tailed priors in large multiple-testing problems, as they have a mild rate of tail decay in the marginal likelihood $m(y)$---a property long recognized to be important in testing., Comment: Published in at http://dx.doi.org/10.1214/11-AOAS512 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Published
2010
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121. Local shrinkage rules, Levy processes, and regularized regression

Author

Polson, Nicholas G. and Scott, James G.

Subjects
Statistics - Methodology, Mathematics - Statistics Theory
Abstract

We use Levy processes to generate joint prior distributions, and therefore penalty functions, for a location parameter as p grows large. This generalizes the class of local-global shrinkage rules based on scale mixtures of normals, illuminates new connections among disparate methods, and leads to new results for computing posterior means and modes under a wide class of priors. We extend this framework to large-scale regularized regression problems where p>n, and provide comparisons with other methodologies.

Published
2010

122. Nonparametric Bayesian multiple testing for longitudinal performance stratification

Author

Scott, James G.

Subjects
Statistics - Applications
Abstract

This paper describes a framework for flexible multiple hypothesis testing of autoregressive time series. The modeling approach is Bayesian, though a blend of frequentist and Bayesian reasoning is used to evaluate procedures. Nonparametric characterizations of both the null and alternative hypotheses will be shown to be the key robustification step necessary to ensure reasonable Type-I error performance. The methodology is applied to part of a large database containing up to 50 years of corporate performance statistics on 24,157 publicly traded American companies, where the primary goal of the analysis is to flag companies whose historical performance is significantly different from that expected due to chance., Comment: Published in at http://dx.doi.org/10.1214/09-AOAS252 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Published
2010
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123. Benchmarking Historical Corporate Performance

Author

Scott, James G.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

This paper uses Bayesian tree models for statistical benchmarking in data sets with awkward marginals and complicated dependence structures. The method is applied to a very large database on corporate performance over the last four decades. The results of this study provide a formal basis for making cross-peer-group comparisons among companies in very different industries and operating environments. This is done by using models for Bayesian multiple hypothesis testing to determine which firms, if any, have systematically outperformed their peer groups over time. We conclude that systematic outperformance, while it seems to exist, is quite rare worldwide.

Published
2009

125. Attention deficit hyperactivity and oppositional defiant disorder symptoms in adolescence and risk of substance use disorders—A general population‐based birth cohort study

Author

Mustonen, Antti, primary, Rodriguez, Alina, additional, Scott, James G., additional, Vuori, Miika, additional, Hurtig, Tuula, additional, Halt, Anu‐Helmi, additional, Miettunen, Jouko, additional, Alakokkare, Anni‐Emilia, additional, and Niemelä, Solja, additional

Published
2023
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128. A Relationship between Early Language Skills and Adult Autistic-Like Traits: Evidence from a Longitudinal Population-Based Study

Author

Armstrong, Rebecca, Whitehouse, Andrew J. O., Scott, James G., Copland, David A., McMahon, Katie L., Fleming, Sophie, and Arnott, Wendy

Abstract

The current study examined the relationship between early language ability and autistic-like traits in adulthood, utilising data from 644 participants from a longitudinal study of the general population. Language performance at 2 years was measured with the Language Development Survey (LDS), and at 20 years the participants completed the Autism-Spectrum Quotient (AQ). Vocabulary size at 2 years was negatively associated with Total AQ score, as well as scores on the Communication, and Social Skills subscales. Adults who had been late talkers were also more likely to have "high" scores on the Communication subscale. This is the first study to show an association between early language ability and autistic-like traits in adulthood.

Published
2017
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129. Change in Receptive Vocabulary from Childhood to Adulthood: Associated Mental Health, Education and Employment Outcomes

Author

Armstrong, Rebecca, Arnott, Wendy, Copland, David A., McMahon, Katie, Khan, Asaduzzaman, Najman, Jake M., and Scott, James G.

Abstract

Background: Population-based studies have found that early language delays are associated with poorer long-term outcomes in adolescence and adulthood. Few studies have explored the influence of change in language ability over time on adult outcomes. Aim: To examine the educational, vocational and mental health outcomes for adults accounting for different vocabulary developmental profiles over a 16-year period. Methods & Procedures: A total of 1914 participants of the Mater-University of Queensland Study of Pregnancy (MUSP) were categorized into four groups based on their vocabulary skills at 5 and 21 years: (1) persistently good (n = 1679), (2) persistently poor (n = 33), (3) improved (n = 160), and (4) deteriorated (n = 42). The associations between vocabulary group and educational, vocational, and mental health outcomes at 21 years were investigated. Outcomes & Results: Adults with deteriorated and persistently poor vocabulary skills were less likely to have completed secondary school; with the improved, deteriorated and persistently poor groups being less likely to be engaged in education, employment or training at the 21-year follow-up, compared with the persistently good group. Adults with deteriorated performance were at an increased risk of affective disorders, as well as substance and alcohol abuse/misuse. Conclusions & Implications: These findings provide evidence that impaired vocabulary skills in adulthood, regardless of whether the deficit was acquired early or later, are associated with adverse outcomes. Clinicians and educators need to be aware of these adversities and ensure they are supporting children and adolescents in whom language difficulties exist or emerge so as to prevent poor long-term outcomes from occurring.

Published
2017
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131. Investigating the relationship between iron and depression

Author

Mills, Natalie T., Maier, Robert, Whitfield, John B., Wright, Margaret J., Colodro-Conde, Lucia, Byrne, Enda M., Scott, James G., Byrne, Gerard J., Hansell, Narelle K., Vinkhuyzen, Anna A.E., CouvyDuchesne, Baptiste, Montgomery, Grant W., Henders, Anjali K., Martin, Nicholas G., Wray, Naomi R., and Benyamin, Beben

Published
2017
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134. Factors Associated With Knowledge and Experience of Self-managed Abortion Among Patients Seeking Care at 49 US Abortion Clinics

Author

Aiken, Abigail R. A., primary, Tello-Pérez, Luisa Alejandra, additional, Madera, Melissa, additional, Starling, Jennifer E., additional, Johnson, Dana M., additional, Broussard, Kathleen, additional, Padron, Elisa, additional, Ze-Noah, Carol Armelle, additional, Baldwin, Aleta, additional, and Scott, James G., additional

Published
2023
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136. Child maltreatment and health service use: findings of the Australian Child Maltreatment Study

Author

Pacella, Rosana, primary, Nation, Alexandra, additional, Mathews, Ben, additional, Scott, James G, additional, Higgins, Daryl J, additional, Haslam, Divna M, additional, Dunne, Michael P, additional, Finkelhor, David, additional, Meinck, Franziska, additional, Erskine, Holly E, additional, Thomas, Hannah J, additional, Malacova, Eva, additional, Lawrence, David M, additional, and Monks, Claire, additional

Published
2023
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138. The Australian Child Maltreatment Study ( ACMS ), a national survey of the prevalence of child maltreatment and its correlates: methodology

Author

Haslam, Divna M, primary, Lawrence, David M, additional, Mathews, Ben, additional, Higgins, Daryl J, additional, Hunt, Anna, additional, Scott, James G, additional, Dunne, Michael P, additional, Erskine, Holly E, additional, Thomas, Hannah J, additional, Finkelhor, David, additional, Pacella, Rosana, additional, Meinck, Franziska, additional, and Malacova, Eva, additional

Published
2023
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139. The association between child maltreatment and mental disorders in the Australian Child Maltreatment Study

Author

Scott, James G, primary, Malacova, Eva, additional, Mathews, Ben, additional, Haslam, Divna M, additional, Pacella, Rosana, additional, Higgins, Daryl J, additional, Meinck, Franziska, additional, Dunne, Michael P, additional, Finkelhor, David, additional, Erskine, Holly E, additional, Lawrence, David M, additional, and Thomas, Hannah J, additional

Published
2023
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141. The association between child maltreatment and health risk behaviours and conditions throughout life in the Australian Child Maltreatment Study

Author

Lawrence, David M, primary, Hunt, Anna, additional, Mathews, Ben, additional, Haslam, Divna M, additional, Malacova, Eva, additional, Dunne, Michael P, additional, Erskine, Holly E, additional, Higgins, Daryl J, additional, Finkelhor, David, additional, Pacella, Rosana, additional, Meinck, Franziska, additional, Thomas, Hannah J, additional, and Scott, James G, additional

Published
2023
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142. The prevalence and nature of multi‐type child maltreatment in Australia

Author

Higgins, Daryl J, primary, Mathews, Ben, additional, Pacella, Rosana, additional, Scott, James G, additional, Finkelhor, David, additional, Meinck, Franziska, additional, Erskine, Holly E, additional, Thomas, Hannah J, additional, Lawrence, David M, additional, Haslam, Divna M, additional, Malacova, Eva, additional, and Dunne, Michael P, additional

Published
2023
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148. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015

Author

Vos, Theo, Allen, Christine, Arora, Megha, Barber, Ryan M, Bhutta, Zulfiqar A, Brown, Alexandria, Carter, Austin, Casey, Daniel C, Charlson, Fiona J, Chen, Alan Z, Coggeshall, Megan, Cornaby, Leslie, Dandona, Lalit, Dicker, Daniel J, Dilegge, Tina, Erskine, Holly E, Ferrari, Alize J, Fitzmaurice, Christina, Fleming, Tom, Forouzanfar, Mohammad H, Fullman, Nancy, Gething, Peter W, Goldberg, Ellen M, Graetz, Nicholas, Haagsma, Juanita A, Hay, Simon I, Johnson, Catherine O, Kassebaum, Nicholas J, Kawashima, Toana, Kemmer, Laura, Khalil, Ibrahim A, Kinfu, Yohannes, Kyu, Hmwe H, Leung, Janni, Liang, Xiaofeng, Lim, Stephen S, Lopez, Alan D, Lozano, Rafael, Marczak, Laurie, Mensah, George A, Mokdad, Ali H, Naghavi, Mohsen, Nguyen, Grant, Nsoesie, Elaine, Olsen, Helen, Pigott, David M, Pinho, Christine, Rankin, Zane, Reinig, Nikolas, Salomon, Joshua A, Sandar, Logan, Smith, Alison, Stanaway, Jeffrey, Steiner, Caitlyn, Teeple, Stephanie, Thomas, Bernadette A, Troeger, Christopher, Wagner, Joseph A, Wang, Haidong, Wanga, Valentine, Whiteford, Harvey A, Zoeckler, Leo, Abajobir, Amanuel Alemu, Abate, Kalkidan Hassen, Abbafati, Cristiana, Abbas, Kaja M, Abd-Allah, Foad, Abraham, Biju, Abubakar, Ibrahim, Abu-Raddad, Laith J, Abu-Rmeileh, Niveen M E, Ackerman, Ilana N, Adebiyi, Akindele Olupelumi, Ademi, Zanfina, Adou, Arsène Kouablan, Afanvi, Kossivi Agbelenko, Agardh, Emilie Elisabet, Agarwal, Arnav, Kiadaliri, Aliasghar Ahmad, Ahmadieh, Hamid, Ajala, Oluremi N, Akinyemi, Rufus Olusola, Akseer, Nadia, Al-Aly, Ziyad, Alam, Khurshid, Alam, Noore K M, Aldhahri, Saleh Fahed, Alegretti, Miguel Angel, Alemu, Zewdie Aderaw, Alexander, Lily T, Alhabib, Samia, Ali, Raghib, Alkerwi, Ala'a, Alla, François, Allebeck, Peter, Al-Raddadi, Rajaa, Alsharif, Ubai, Altirkawi, Khalid A, Alvis-Guzman, Nelson, Amare, Azmeraw T, Amberbir, Alemayehu, Amini, Heresh, Ammar, Walid, Amrock, Stephen Marc, Andersen, Hjalte H, Anderson, Gregory M, Anderson, Benjamin O, Antonio, Carl Abelardo T, Aregay, Atsede Fantahun, Ärnlöv, Johan, Artaman, Al, Asayesh, Hamid, Assadi, Reza, Atique, Suleman, Avokpaho, Euripide Frinel G Arthur, Awasthi, Ashish, Quintanilla, Beatriz Paulina Ayala, Azzopardi, Peter, Bacha, Umar, Badawi, Alaa, Balakrishnan, Kalpana, Banerjee, Amitava, Barac, Aleksandra, Barker-Collo, Suzanne L, Bärnighausen, Till, Barregard, Lars, Barrero, Lope H, Basu, Arindam, Bazargan-Hejazi, Shahrzad, Beghi, Ettore, Bell, Brent, Bell, Michelle L, Bennett, Derrick A, Bensenor, Isabela M, Benzian, Habib, Berhane, Adugnaw, Bernabé, Eduardo, Betsu, Balem Demtsu, Beyene, Addisu Shunu, Bhala, Neeraj, Bhatt, Samir, Biadgilign, Sibhatu, Bienhoff, Kelly, Bikbov, Boris, Biryukov, Stan, Bisanzio, Donal, Bjertness, Espen, Blore, Jed, Borschmann, Rohan, Boufous, Soufiane, Brainin, Michael, Brazinova, Alexandra, Breitborde, Nicholas J K, Brown, Jonathan, Buchbinder, Rachelle, Buckle, Geoffrey Colin, Butt, Zahid A, Calabria, Bianca, Campos-Nonato, Ismael Ricardo, Campuzano, Julio Cesar, Carabin, Hélène, Cárdenas, Rosario, Carpenter, David O, Carrero, Juan Jesus, Castañeda-Orjuela, Carlos A, Rivas, Jacqueline Castillo, Catalá-López, Ferrán, Chang, Jung-Chen, Chiang, Peggy Pei-Chia, Chibueze, Chioma Ezinne, Chisumpa, Vesper Hichilombwe, Choi, Jee-Young Jasmine, Chowdhury, Rajiv, Christensen, Hanne, Christopher, Devasahayam Jesudas, Ciobanu, Liliana G, Cirillo, Massimo, Coates, Matthew M, Colquhoun, Samantha M, Cooper, Cyrus, Cortinovis, Monica, Crump, John A, Damtew, Solomon Abrha, Dandona, Rakhi, Daoud, Farah, Dargan, Paul I, das Neves, José, Davey, Gail, Davis, Adrian C, Leo, Diego De, Degenhardt, Louisa, Gobbo, Liana C Del, Dellavalle, Robert P, Deribe, Kebede, Deribew, Amare, Derrett, Sarah, Jarlais, Don C Des, Dharmaratne, Samath D, Dhillon, Preet K, Diaz-Torné, Cesar, Ding, Eric L, Driscoll, Tim R, Duan, Leilei, Dubey, Manisha, Duncan, Bruce Bartholow, Ebrahimi, Hedyeh, Ellenbogen, Richard G, Elyazar, Iqbal, Endres, Matthias, Endries, Aman Yesuf, Ermakov, Sergey Petrovich, 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2016
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149. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015

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Forouzanfar, Mohammad H, Afshin, Ashkan, Alexander, Lily T, Anderson, H Ross, Bhutta, Zulfiqar A, Biryukov, Stan, Brauer, Michael, Burnett, Richard, Cercy, Kelly, Charlson, Fiona J, Cohen, Aaron J, Dandona, Lalit, Estep, Kara, Ferrari, Alize J, Frostad, Joseph J, Fullman, Nancy, Gething, Peter W, Godwin, William W, Griswold, Max, Hay, Simon I, Kinfu, Yohannes, Kyu, Hmwe H, Larson, Heidi J, Liang, Xiaofeng, Lim, Stephen S, Liu, Patrick Y, Lopez, Alan D, Lozano, Rafael, Marczak, Laurie, Mensah, George A, Mokdad, Ali H, Moradi-Lakeh, Maziar, Naghavi, Mohsen, Neal, Bruce, Reitsma, Marissa B, Roth, Gregory A, Salomon, Joshua A, Sur, Patrick J, Vos, Theo, Wagner, Joseph A, Wang, Haidong, Zhao, Yi, Zhou, Maigeng, Aasvang, Gunn Marit, Abajobir, Amanuel Alemu, Abate, Kalkidan Hassen, Abbafati, Cristiana, Abbas, Kaja M, Abd-Allah, Foad, Abdulle, Abdishakur M, Abera, Semaw Ferede, Abraham, Biju, Abu-Raddad, Laith J, Abyu, Gebre Yitayih, Adebiyi, Akindele Olupelumi, Adedeji, Isaac Akinkunmi, Ademi, Zanfina, Adou, Arsène Kouablan, Adsuar, José C, Agardh, Emilie Elisabet, Agarwal, Arnav, Agrawal, Anurag, Kiadaliri, Aliasghar Ahmad, Ajala, Oluremi N, Akinyemiju, Tomi F, Al-Aly, Ziyad, Alam, Khurshid, Alam, Noore K M, Aldhahri, Saleh Fahed, Aldridge, Robert William, Alemu, Zewdie Aderaw, Ali, Raghib, Alkerwi, Ala'a, Alla, François, Allebeck, Peter, Alsharif, Ubai, Altirkawi, Khalid A, Martin, Elena Alvarez, Alvis-Guzman, Nelson, Amare, Azmeraw T, Amberbir, Alemayehu, Amegah, Adeladza Kofi, Amini, Heresh, Ammar, Walid, Amrock, Stephen Marc, Andersen, Hjalte H, Anderson, Benjamin O, Antonio, Carl Abelardo T, Anwari, Palwasha, Ärnlöv, Johan, Artaman, Al, Asayesh, Hamid, Asghar, Rana Jawad, Assadi, Reza, Atique, Suleman, Avokpaho, Euripide Frinel G Arthur, Awasthi, Ashish, Quintanilla, Beatriz Paulina Ayala, Azzopardi, Peter, Bacha, Umar, Badawi, Alaa, Bahit, Maria C, Balakrishnan, Kalpana, Barac, Aleksandra, Barber, Ryan M, Barker-Collo, Suzanne L, Bärnighausen, Till, Barquera, 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Giancarlo, Lotufo, Paulo A, Lunevicius, Raimundas, MacIntyre, Michael, Mahdavi, Mahdi, Majdan, Marek, Majeed, Azeem, Malekzadeh, Reza, Malta, Deborah Carvalho, Manamo, Wondimu Ayele Ayele, Mapoma, Chabila C, Marcenes, Wagner, Martin, Randall V, Martinez-Raga, Jose, Masiye, Felix, Matsushita, Kunihiro, Matzopoulos, Richard, Mayosi, Bongani M, McGrath, John J, McKee, Martin, Meaney, Peter A, Medina, Catalina, Mehari, Alem, Mejia-Rodriguez, Fabiola, Mekonnen, Alemayehu B, Melaku, Yohannes Adama, Memish, Ziad A, Mendoza, Walter, Mensink, Gert B M, Meretoja, Atte, Meretoja, Tuomo J, Mesfin, Yonatan Moges, Mhimbira, Francis Apolinary, Millear, Anoushka, Miller, Ted R, Mills, Edward J, Mirarefin, Mojde, Misganaw, Awoke, Mock, Charles N, Mohammadi, Alireza, Mohammed, Shafiu, Mola, Glen Liddell D, Monasta, Lorenzo, Hernandez, Julio Cesar Montañez, Montico, Marcella, Morawska, Lidia, Mori, Rintaro, Mozaffarian, Dariush, Mueller, Ulrich O, Mullany, Erin, Mumford, John Everett, Murthy, 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Nikhil, Tanne, David, Tavakkoli, Mohammad, Taye, Belaynew Wasie, Taylor, Hugh R, Tedla, Bemnet Amare, Tefera, Worku Mekonnen, Tegegne, Teketo Kassaw, Tekle, Dejen Yemane, Terkawi, Abdullah Sulieman, Thakur, J S, Thomas, Bernadette A, Thomas, Matthew Lloyd, Thomson, Alan J, Thorne-Lyman, Andrew L, Thrift, Amanda G, Thurston, George D, Tillmann, Taavi, Tobe-Gai, Ruoyan, Tobollik, Myriam, Topor-Madry, Roman, Topouzis, Fotis, Towbin, Jeffrey Allen, Tran, Bach Xuan, Dimbuene, Zacharie Tsala, Tsilimparis, Nikolaos, Tura, Abera Kenay, Tuzcu, Emin Murat, Tyrovolas, Stefanos, Ukwaja, Kingsley N, Undurraga, Eduardo A, Uneke, Chigozie Jesse, Uthman, Olalekan A, van Donkelaar, Aaron, van Os, Jim, Varakin, Yuri Y, Vasankari, Tommi, Veerman, J Lennert, Venketasubramanian, Narayanaswamy, Violante, Francesco S, Vollset, Stein Emil, Wagner, Gregory R, Waller, Stephen G, Wang, Jian Li, Wang, Linhong, Wang, Yanping, Weichenthal, Scott, Weiderpass, Elisabete, Weintraub, Robert G, Werdecker, Andrea, 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2016
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