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Children born preterm and of very low birth weight have an increased incidence of learning difficulties, but little is known about the specific nature of their cognitive deficits and the underlying neuropathology. We hypothesized that their vulnerability to hypoxic, metabolic, and nutritional insults would lead to reduced hippocampal volumes and to deficits in memory because of the role of the hippocampus in this domain of cognition. Neuropsychological and magnetic resonance imaging methods were used to investigate this hypothesis in adolescents born preterm (< or = 30 wk gestation, n = 11) or full-term (n = 8). The preterm group had significantly smaller hippocampal volumes bilaterally, despite equivalent head size, and showed specific deficits in certain aspects of everyday memory, both on objective testing and as indicated by parental questionnaires. The preterm group also had a specific deficit in numeracy. The reduced hippocampal volumes and deficits in everyday memory have previously been unrecognized, but their prevalence in a group of neurologically normal children is striking.

We present a Bayesian hierarchical model for detecting differentially expressing genes that includes simultaneous estimation of array effects, and show how to use the output for choosing lists of genes for further investigation. We give empirical evidence that expression-level dependent array effects are needed, and explore different nonlinear functions as part of our model-based approach to normalization. The model includes gene-specific variances but imposes some necessary shrinkage through a hierarchical structure. Model criticism via posterior predictive checks is discussed. Modeling the array effects (normalization) simultaneously with differential expression gives fewer false positive results. To choose a list of genes, we propose to combine various criteria (for instance, fold change and overall expression) into a single indicator variable for each gene. The posterior distribution of these variables is used to pick the list of genes, thereby taking into account uncertainty in parameter estimates. In an application to mouse knockout data, Gene Ontology annotations over- and underrepresented among the genes on the chosen list are consistent with biological expectations.

Following several recent inquiries in the UK into medical malpractice and failures to deliver appropriate standards of health care, there is pressure to introduce formal monitoring of performance outcomes routinely throughout the National Health Service. Statistical process control (SPC) charts have been widely used to monitor medical outcomes in a variety of contexts and have been specifically advocated for use in clinical governance. However, previous applications of SPC charts in medical monitoring have focused on surveillance of a single process over time. We consider some of the methodological and practical aspects that surround the routine surveillance of health outcomes and, in particular, we focus on two important methodological issues that arise when attempting to extend SPC charts to monitor outcomes at more than one unit simultaneously (where a unit could be, for example, a surgeon, general practitioner or hospital): the need to acknowledge the inevitable between-unit variation in 'acceptable' performance outcomes due to the net effect of many small unmeasured sources of variation (e.g. unmeasured case mix and data errors) and the problem of multiple testing over units as well as time. We address the former by using quasi-likelihood estimates of overdispersion, and the latter by using recently developed methods based on estimation of false discovery rates. We present an application of our approach to annual monitoring 'all-cause' mortality data between 1995 and 2000 from 169 National Health Service hospital trusts in England and Wales. Copyright 2004 Royal Statistical Society.

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