SUMMARYBUGS is a software package for Bayesian inference using Gibbs sampling. The software has been instrumental in raising awareness of Bayesian modelling among both academic and commercial communities internationally, and has enjoyed considerable success over its 20-year life span. Despite this, the software has a number of shortcomings and a principal aim of this paper is to provide a balanced critical appraisal, in particular highlighting how various ideas have led to unprecedented flexibility while at the same time producing negative side effects. We also present a historical overview of the BUGS project and some future perspectives.

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There is currently much interest in conducting spatial analyses of health outcomes at the small-area scale. This requires sophisticated statistical techniques, usually involving Bayesian models, to smooth the underlying risk estimates because the data are typically sparse. However, questions have been raised about the performance of these models for recovering the “true” risk surface, about the influence of the prior structure specified, and about the amount of smoothing of the risks that is actually performed. We describe a comprehensive simulation study designed to address these questions. Our results show that Bayesian disease-mapping models are essentially conservative, with high specificity even in situations with very sparse data but low sensitivity if the raised-risk areas have only a moderate (< 2-fold) excess or are not based on substantial expected counts (> 50 per area). Semiparametric spatial mixture models typically produce less smoothing than their conditional autoregressive counterpart when there is sufficient information in the data (moderate-size expected count and/or high true excess risk). Sensitivity may be improved by exploiting the whole posterior distribution to try to detect true raised-risk areas rather than just reporting and mapping the mean posterior relative risk. For the widely used conditional autoregressive model, we show that a decision rule based on computing the probability that the relative risk is above 1 with a cutoff between 70 and 80% gives a specific rule with reasonable sensitivity for a range of scenarios having moderate expected counts (~ 20) and excess risks (~1.5- to 2-fold). Larger (3-fold) excess risks are detected almost certainly using this rule, even when based on small expected counts, although the mean of the posterior distribution is typically smoothed to about half the true value.

With the advent of routine health data indexed at a fine geographical resolution, small area disease mapping studies have become an established technique in geographical epidemiology. The specific issues posed by the sparseness of the data and possibility for local spatial dependence belong to a generic class of statistical problems involving an underlying (latent) spatial process of interest corrupted by observational noise. These are naturally formulated within the framework of hierarchical models, and over the past decade, a variety of spatial models have been proposed for the latent level(s) of the hierarchy. In this article, we provide a comprehensive review of the main classes of such models that have been used for disease mapping within a Bayesian estimation paradigm, and report a performance comparison between representative models in these classes, using a set of simulated data to help illustrate their respective properties. We also consider recent extensions to model the joint spatial distribution of multiple disease or health indicators. The aim is to help the reader choose an appropriate structural prior for the second level of the hierarchical model and to discuss issues of sensitivity to this choice.

Background-Renal insufficiency in patients with ischemic heart disease and acquired heart failure is associated with higher mortality and morbidity. We studied the prevalence of renal dysfunction in adult patients with congenital heart disease (ACHD) and its relation to outcome. Methods and Results-A total of 1102 adult patients with congenital heart disease (age 36.0Ϯ14.2 years) attending our institution between 1999 and 2006 had creatinine concentration measured. Glomerular filtration rate (GFR) was calculated with the Modification of Diet in Renal Disease equation. Patients were divided into groups of normal GFR (Ն90 mL · min Ϫ1 · 1.73 m Ϫ2 ), mildly impaired GFR (60 to 89 mL · min Ϫ1 · 1.73 m Ϫ2), and moderately/severely impaired GFR (Ͻ60 mL · min Ϫ1 · 1.73 m Ϫ2). Survival was compared between GFR groups by Cox regression. Median follow-up was 4.1 years, during which 103 patients died. Renal dysfunction was mild in 41% of patients and moderate or severe in 9%. A decrease in GFR was more common among patients with Eisenmenger physiology, of whom 72% had reduced GFR (Ͻ90 mL · min Ϫ1 · 1.73 m Ϫ2 , PϽ0.0001 compared with the remainder), and in 18%, this was moderate or severe (Pϭ0.007). Renal dysfunction had a substantial impact on mortality (propensity score-weighted hazard ratio 3.25, 95% CI 1.54 to 6.86, Pϭ0.002 for moderately or severely impaired versus normal GFR). Conclusions-Deranged physiology in adult patients with congenital heart disease is not limited to the heart but also affects the kidney. Mortality is 3-fold higher than normal in the 1 in 11 patients who have moderate or severe GFR reduction. (Circulation. 2008;117:2320-2328.)Key Words: heart defects, congenital Ⅲ kidney Ⅲ renal function Ⅲ prognosis A s the number of patients with congenital heart disease reaching adulthood (ACHD) continues to increase, it is becoming clear that pathophysiological derangement occurs not only in the heart but in other organs as well. Renal dysfunction has been reported in ACHD patients, but its prevalence and relation to outcome in this population remain unknown. [1][2][3][4][5][6][7][8] In acquired heart disease, renal dysfunction is an ominous sign. 9 -14 We sought to assess the prevalence of renal dysfunction across the spectrum of ACHD and its predictors and impact on survival. Editorial p 2311 Clinical Perspective p 2328 MethodsAll ACHD patients attending our institution from 1999 to June 2006 and in whom serum creatinine concentration was measured were entered into the study. Only the first measurement was used if there were several. For each subject, an estimated glomerular filtration rate (GFR) was calculated from serum creatinine levels by the Modification of Diet in Renal Disease equation, which adjusts for age, gender, and race. 15 Patients were categorized into groups according to the cutoff values suggested by the National Kidney Foundation practice guidelines: GFR Ͼ90 mL · min Ϫ1 · 1.73 m Ϫ2 was considered normal, 60 to 89 mL · min Ϫ1 · 1.73 m Ϫ2 was considered mildly decreased, and Ͻ60 mL · min Ϫ1 ...