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Background Testosterone therapy is increasingly promoted. No randomized placebo-controlled trial has been implemented to assess the effect of testosterone therapy on cardiovascular events, although very high levels of androgens are thought to promote cardiovascular disease. Methods A systematic review and meta-analysis was conducted of placebo-controlled randomized trials of testosterone therapy among men lasting 12+ weeks reporting cardiovascular-related events. We searched PubMed through the end of 2012 using “(“testosterone” or “androgen”) and trial and (“random*”)” with the selection limited to studies of men in English, supplemented by a bibliographic search of the World Health Organization trial registry. Two reviewers independently searched, selected and assessed study quality with differences resolved by consensus. Two statisticians independently abstracted and analyzed data, using random or fixed effects models, as appropriate, with inverse variance weighting. Results Of 1,882 studies identified 27 trials were eligible including 2,994, mainly older, men who experienced 180 cardiovascular-related events. Testosterone therapy increased the risk of a cardiovascular-related event (odds ratio (OR) 1.54, 95% confidence interval (CI) 1.09 to 2.18). The effect of testosterone therapy varied with source of funding ( P -value for interaction 0.03), but not with baseline testosterone level ( P -value for interaction 0.70). In trials not funded by the pharmaceutical industry the risk of a cardiovascular-related event on testosterone therapy was greater (OR 2.06, 95% CI 1.34 to 3.17) than in pharmaceutical industry funded trials (OR 0.89, 95% CI 0.50 to 1.60). Conclusions The effects of testosterone on cardiovascular-related events varied with source of funding. Nevertheless, overall and particularly in trials not funded by the pharmaceutical industry, exogenous testosterone increased the risk of cardiovascular-related events, with corresponding implications for the use of testosterone therapy.

Background Statins are extensively used for cardiovascular disease prevention. Statins reduce mortality rates more than other lipid-modulating drugs, although evidence from randomized controlled trials also suggests that statins unexpectedly increase the risk of diabetes and improve immune function. Physiologically, statins would be expected to lower androgens because statins inhibit production of the substrate for the local synthesis of androgens and statins' pleiotropic effects are somewhat similar to the physiological effects of lowering testosterone, so we hypothesized that statins lower testosterone. Methods A meta-analysis of placebo-controlled randomized trials of statins to test the a priori hypothesis that statins lower testosterone. We searched the PubMed, Medline and ISI Web of Science databases until the end of 2011, using '(Testosterone OR androgen) AND (CS-514 OR statin OR simvastatin OR atorvastatin OR fluvastatin OR lovastatin OR rosuvastatin OR pravastatin)' restricted to randomized controlled trials in English, supplemented by a bibliographic search. We included studies with durations of 2+ weeks reporting changes in testosterone. Two reviewers independently searched, selected and assessed study quality. Two statisticians independently abstracted and analyzed data, using random or fixed effects models, as appropriate, with inverse variance weighting. Results Of the 29 studies identified 11 were eligible. In 5 homogenous trials of 501 men, mainly middle aged with hypercholesterolemia, statins lowered testosterone by -0.66 nmol/l (95% confidence interval (CI) -0.14 to -1.18). In 6 heterogeneous trials of 368 young women with polycystic ovary syndrome, statins lowered testosterone by -0.40 nmol/l (95% CI -0.05 to -0.75). Overall statins lowered testosterone by -0.44 nmol/l (95% CI -0.75 to -0.13). Conclusions Statins may partially operate by lowering testosterone. Whether this is a detrimental side effect or mode of action warrants investigation given the potential implications for drug development and prevention of non-communicable chronic diseases. See commentary article here http://www.biomedcentral.com/1741-7015/11/58

Background. Previous studies have established that antibody titer measured by the hemagglutination-inhibiting (HAI) assay is correlated with protection against influenza virus infection, with an HAI titer of 1:40 generally associated with 50% protection. Methods. We recruited index cases with confirmed influenza virus infection from outpatient clinics, and followed up their household contacts for 7-10 days to identify secondary infections. Serum samples collected from a subset of household contacts were tested by HAI and microneutralization (MN) assays against prevalent influenza viruses. We analyzed the data using an individual hazard-based transmission model that adjusted for age and vaccination history. Results. Compared to a reference group with antibody titers <1:10, we found that HAI titers of 1:40 against influenza A(H1N1) and A(H3N2) were associated with 31% (95% confidence interval [CI], 13%-46%) and 31% (CI, 1%-53%) protection against polymerase chain reaction (PCR)-confirmed A(H1N1) and A(H3N2) virus infection, respectively, while an MN titer of 1:40 against A(H3N2) was associated with 49% (95% CI, 7%-81%) protection against PCR-confirmed A(H3N2) virus infection. Conclusions. An HAI titer of 1:40 was associated with substantially less than 50% protection against PCR-confirmed influenza virus infection within households, perhaps because of exposures of greater duration or intensity in that confined setting.

Human activity has had a profound negative impact on the structure and function of the earth’s ecosystems. However, with a growing awareness of the value of the services provided by intact ecosystems, restoration of degraded land is increasingly used as a means of reviving ecosystem function. Upland landscapes offer an excellent example of an environment heavily modified by human land use. Agriculture has been the key driver of ecosystem change, but as upland habitats such as peatlands can provide a number of highly valuable services, future change may focus on restoration in order to regain key ecosystem processes. However, as pastoral farming continues to dominate upland areas, ecosystem restoration has the potential to conflict with existing land use.This thesis attempts to assess differences in the agricultural productivity of the different habitat types present in upland pastures. Past and present land use have shaped the distribution of different upland habitat types, and future changes associated with ecosystem restoration are likely to lead to further change in vegetation communities.Three key contributors to agricultural productivity are examined. Firstly, variation in the nutritional quality of different upland habitats is assessed, in order to understand their value for grazing animals. Secondly, levels of livestock use in different habitats are compared in order to identify areas of particular importance for grazing. Finally, parasite populations are measured in different habitats in order provide an indication of which habitats pose the greatest potential risk of infection.It is shown that these factors appear to differ between habitats, meaning that agricultural productivity may show spatial variation in upland pastures. However, it appears that peatland restoration might have anegligible impact on farming in upland pastures due to apparent minor differences in the agricultural productivity of the habitats most likely to be affected.

Graphical models provide a very promising avenue for making sense of large, complex datasets. The most popular graphical models in use at the moment are Bayesian networks (BNs). This thesis shows, however, they are not always ideal factorisations of a system. Instead, I advocate for the use of a relatively new graphical model, the chain event graph (CEG), that is based on event trees. Event trees directly represent graphically the event space of a system. Chain event graphs reduce their potentially huge dimensionality by taking into account identical probability distributions on some of the event tree’s subtrees, with the added benefits of showing the conditional independence relationships of the system — one of the advantages of the Bayesian network representation that event trees lack — and implementation of causal hypotheses that is just as easy, and arguably more natural, than is the case with Bayesian networks, with a larger domain of implementation using purely graphical means. The trade-off for this greater expressive power, however, is that model specification and selection are much more difficult to undertake with the larger set of possible models for a given set of variables. My thesis is the first exposition of how to learn CEGs. I demonstrate that not only is conjugate (and hence quick) learning of CEGs possible, but I characterise priors that imply conjugate updating based on very reasonable assumptions that also have direct Bayesian network analogues. By re-casting CEGs as partition models, I show how established partition learning algorithms can be adapted for the task of learning CEGs. I then develop a robust yet flexible prediction machine based on CEGs for any discrete multivariate time series — the dynamic CEG model — which combines the power of CEGs, multi-process and steady modelling, lattice theory and Occam’s razor. This is also an exact method that produces reliable predictions without requiring much a priori modelling. I then demonstrate how easily causal analysis can be implemented with this model class that can express a wide variety of causal hypotheses. I end with an application of these techniques to real educational data, drawing inferences that would not have been possible simply using BNs.

Objectives: Ongoing developments in design have improved the outlook for left-ventricular assist device (LVAD) implantation as a therapy in end-stage heart failure. Nevertheless, early cost-effectiveness assessments, based on first-generation devices, have not been encouraging. Against this background, we set out (i) to examine the survival benefit that LVADs would need to generate before they could be deemed cost-effective; (ii) to provide insight into the likelihood that this benefit will be achieved; and (iii) from the perspective of a healthcare provider, to assess the value of discovering the actual size of this benefit by means of a Bayesian value of information analysis. Methods: Cost-effectiveness assessments are made from the perspective of the healthcare provider, using current UK norms for the value of a quality-adjusted life-year (QALY). The treatment model is grounded in published analyses of the Randomized Evaluation of Mechanical Assistance for the Treatment of Congestive Heart Failure (REMATCH) trial of first-generation LVADs, translated into a UK cost setting. The prospects for patient survival with second-generation devices is assessed using Bayesian prior distributions, elicited from a group of leading clinicians in the field. Results: Using established thresholds, cost-effectiveness probabilities under these priors are found to be low (∼.2 percent) for devices costing as much as £60,000. Sensitivity of the conclusions to both device cost and QALY valuation is examined. Conclusions: In the event that the price of the device in use would reduce to £40,000, the value of the survival information can readily justify investment in further trials.

The class of chain event graph models is a generalisation of the class of discrete Bayesian networks, retaining most of the structural advantages of the Bayesian network for model interrogation, propagation and learning, while more naturally encoding asymmetric state spaces and the order in which events happen. In this paper we demonstrate how with complete sampling, conjugate closed form model selection based on product Dirichlet priors is possible, and prove that suitable homogeneity assumptions characterise the product Dirichlet prior on this class of models. We demonstrate our techniques using two educational examples.