Whole-History Rating: A Bayesian Rating System for Players of Time-Varying Strength

International audienceWhole-History Rating (WHR) is a new method to estimate the time-varying strengths of players involved in paired comparisons. Like many variations of the Elo rating system, the whole-history approach is based on the dynamic Bradley-Terry model. But, instead of using incremental approximations, WHR directly computes the exact maximum a posteriori over the whole rating history of all players. This additional accuracy comes at a higher computational cost than traditional methods, but computation is still fast enough to be easily applied in real time to large-scale game servers (a new game is added in less than 0.001 second). Experiments demonstrate that, in comparison to Elo, Glicko, TrueSkill, and decayed-history algorithms, WHR produces better predictions

Recent trends and future directions for lung cancer mortality in Europe

Lung cancer mortality patterns throughout Europe are very heterogeneous and largely reflect past smoking habits. In order to clarify the changing patterns of lung cancer in Europe we have plotted the overall lung cancer trends among men and women for 20 countries from 1950 up to1998. Furthermore, using a Bayesian age-period-cohort approach, we have calculated 5 year projections of lung cancer rate up to 2003. Finally, we make some comments on probable future trends by analysing recent trends in adults aged <55 years. Lung cancer mortality rates up to age 75 years portray a general trend of decreasing lung cancer rates among men and increasing lung cancer rates among women. Exceptions to this decrease among men include Hungary where not only are current mortality rates much higher than previously observed in any other country (at 76.7 out of 100 000 in 1998) but they are projected to increase further in the short term. Rates among adults aged <55 years have recently peaked, indicating that overall rates are likely to peak in the next decade. Among women, rapid increases have been observed in Denmark, Netherlands, Hungary, Ireland and UK. Whereas Ireland and UK rates have started to decrease and are projected to continue falling, rates in the other three countries are projected to increase further. Trends in women aged <55 years indicate that rates in Danish women will peak in the next decade, whereas lung cancer rates among Dutch women are likely to continue increasing. Rates in Hungarian women are likely to increase and will surpass the current high rate observed in Denmark

A network-based dynamical ranking system for competitive sports

From the viewpoint of networks, a ranking system for players or teams in sports is equivalent to a centrality measure for sports networks, whereby a directed link represents the result of a single game. Previously proposed network-based ranking systems are derived from static networks, i.e., aggregation of the results of games over time. However, the score of a player (or team) fluctuates over time. Defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods. To account for this factor, we propose a dynamic variant of such a network-based ranking system and apply it to professional men's tennis data. We derive a set of linear online update equations for the score of each player. The proposed ranking system predicts the outcome of the future games with a higher accuracy than the static counterparts.Comment: 6 figure

Shared component modelling as an alternative to assess geographical variations in medical practice: gender inequalities in hospital admissions for chronic diseases

<p>Abstract</p> <p>Background</p> <p>Small area analysis is the most prevalent methodological approach in the study of unwarranted and systematic variation in medical practice at geographical level. Several of its limitations drive researchers to use disease mapping methods -deemed as a valuable alternative. This work aims at exploring these techniques using - as a case of study- the gender differences in rates of hospitalization in elderly patients with chronic diseases.</p> <p>Methods</p> <p>Design and study setting: An empirical study of 538,358 hospitalizations affecting individuals aged over 75, who were admitted due to a chronic condition in 2006, were used to compare Small Area Analysis (SAVA), the Besag-York-Mollie (BYM) modelling and the Shared Component Modelling (SCM). Main endpoint: Gender spatial variation was measured, as follows: SAVA estimated gender-specific utilization ratio; BYM estimated the fraction of variance attributable to spatial correlation in each gender; and, SCM estimated the fraction of variance shared by the two genders, and those specific for each one.</p> <p>Results</p> <p>Hospitalization rates due to chronic diseases in the elderly were higher in men (median per area 21.4 per 100 inhabitants, interquartile range: 17.6 to 25.0) than in women (median per area 13.7 per 100, interquartile range: 10.8 to 16.6). Whereas Utilization Ratios showed a similar geographical pattern of variation in both genders, BYM found a high fraction of variation attributable to spatial correlation in both men (71%, CI95%: 50 to 94) and women (62%, CI95%: 45 to 77). In turn, SCM showed that the geographical admission pattern was mainly shared, with just 6% (CI95%: 4 to 8) of variation specific to the women component.</p> <p>Conclusions</p> <p>Whereas SAVA and BYM focused on the magnitude of variation and on allocating where variability cannot be due to chance, SCM signalled discrepant areas where latent factors would differently affect men and women.</p

Bayesian Space-Time Patterns and Climatic Determinants of Bovine Anaplasmosis

Citation: Hanzlicek, G. A., Raghavan, R. K., Ganta, R. R., & Anderson, G. A. (2016). Bayesian Space-Time Patterns and Climatic Determinants of Bovine Anaplasmosis. Plos One, 11(3), 13. doi:10.1371/journal.pone.0151924The space-time pattern and environmental drivers (land cover, climate) of bovine anaplasmosis in the Midwestern state of Kansas was retrospectively evaluated using Bayesian hierarchical spatio-temporal models and publicly available, remotely-sensed environmental covariate information. Cases of bovine anaplasmosis positively diagnosed at Kansas State Veterinary Diagnostic Laboratory (n = 478) between years 2005-2013 were used to construct the models, which included random effects for space, time and space-time interaction effects with defined priors, and fixed-effect covariates selected a priori using an univariate screening procedure. The Bayesian posterior median and 95% credible intervals for the space-time interaction term in the best-fitting covariate model indicated a steady progression of bovine anaplasmosis over time and geographic area in the state. Posterior median estimates and 95% credible intervals derived for covariates in the final covariate model indicated land surface temperature (minimum), relative humidity and diurnal temperature range to be important risk factors for bovine anaplasmosis in the study. The model performance measured using the Area Under the Curve (AUC) value indicated a good performance for the covariate model (>0.7). The relevance of climatological factors for bovine anaplasmosis is discussed

Strategy selection and outcome prediction in sport using dynamic learning for stochastic processes

We study reliability equivalence factors of a system of independent and identical components with exponentiated Weibull lifetimes. The system has n subsystems connected in parallel and subsystem i has mi components connected in series, i=1,…,n. We consider improving the reliability of the system by (a) a reduction method and (b) several duplication methods: (i) hot duplication; (ii) cold duplication with perfect switching; (iii) cold duplication with imperfect switching. We compute two types of reliability equivalence factors: survival equivalence factors and mean equivalence factors. Although our methods adapt to allow for general lifetime models, we use the exponentiated Weibull distribution because it is flexible and enables comparisons with other reliability equivalence studies. The example we present demonstrates the potential for applying these methods to address specific questions that arise when attempting to improve the reliability of simple systems or simple configurations of possibly complex subsystems in many diverse applications

A semiparametric Bayesian proportional hazards model for interval censored data with frailty effects

<p>Abstract</p> <p>Background</p> <p>Multivariate analysis of interval censored event data based on classical likelihood methods is notoriously cumbersome. Likelihood inference for models which additionally include random effects are not available at all. Developed algorithms bear problems for practical users like: matrix inversion, slow convergence, no assessment of statistical uncertainty.</p> <p>Methods</p> <p>MCMC procedures combined with imputation are used to implement hierarchical models for interval censored data within a Bayesian framework.</p> <p>Results</p> <p>Two examples from clinical practice demonstrate the handling of clustered interval censored event times as well as multilayer random effects for inter-institutional quality assessment. The software developed is called survBayes and is freely available at CRAN.</p> <p>Conclusion</p> <p>The proposed software supports the solution of complex analyses in many fields of clinical epidemiology as well as health services research.</p

Bayesian Spatio-Temporal Modeling of Schistosoma japonicum Prevalence Data in the Absence of a Diagnostic ‘Gold’ Standard

Schistosomiasis is a serious public health problem in the People's Republic of China and elsewhere, and mapping of risk areas is important for guiding control interventions. Here, a 10-year surveillance database from Dangtu County in the southeastern part of the People's Republic of China was utilized for modeling the spatial and temporal distribution of infections in relation to environmental features and socioeconomic factors. Disease surveillance was done on the basis of a serological test, and we explicitly considered the imperfect sensitivity and specificity of the test when modeling the ‘true’ infection prevalence of Schistosoma japonicum. We then produced a risk map for S. japonicum transmission, which can assist decision making for local control interventions. Our work emphasizes the importance of accounting for the uncertainty in the diagnosis of schistosomiasis, and the potential of predicting the spatial and temporal distribution of the disease when using a Bayesian modeling framework. Our study can therefore serve as a template for future risk profiling of neglected tropical diseases studies, particularly when exploring spatial and temporal disease patterns in relation to environmental and socioeconomic factors, and how to account for the influence of diagnostic uncertainty

From spatial ecology to spatial epidemiology: Modeling spatial distributions of different cancer types with principal coordinates of neighbor matrices

Epidemiology and ecology share many fundamental research questions. Here we describe how principal coordinates of neighbor matrices (PCNM), a method from spatial ecology, can be applied to spatial epidemiology. PCNM is based on geographical distances among sites and can be applied to any set of sites providing a good coverage of a study area. In the present study, PCNM eigenvectors corresponding to positive autocorrelation were used as explanatory variables in linear regressions to model incidences of eight most common cancer types in Finnish municipalities (n = 320). The dataset was provided by the Finnish Cancer Registry and it included altogether 615,839 cases between 1953 and 2010. Results: PCNM resulted in 165 vectors with a positive eigenvalue. The first PCNM vector corresponded to the wavelength of hundreds of kilometers as it contrasted two main subareas so that municipalities located in southwestern Finland had the highest positive site scores and those located in midwestern Finland had the highest negative scores in that vector. Correspondingly, the 165thPCNM vector indicated variation mainly between the two small municipalities located in South Finland. The vectors explained 13 - 58% of the spatial variation in cancer incidences. The number of outliers having standardized residual > |3| was very low, one to six per model, and even lower, zero to two per model, according to Chauvenet's criterion. The spatial variation of prostate cancer was best captured (adjusted r 2= 0.579). Conclusions: PCNM can act as a complementary method to causal modeling to achieve a better understanding of the spatial structure of both the response and explanatory variables, and to assess the spatial importance of unmeasured explanatory factors. PCNM vectors can be used as proxies for demographics and causative agents to deal with autocorrelation, multicollinearity, and confounding variables. PCNM may help to extend spatial epidemiology to areas with limited availability of registers, improve cost-effectiveness, and aid in identifying unknown causative agents, and predict future trends in disease distributions and incidences. A large advantage of using PCNM is that it can create statistically valid reflectors of real predictors for disease incidence models with only little resources and background information