Do mammographic tumor features in breast cancer relate to breast density and invasiveness, tumor size, and axillary lymph node involvement?

Breast density and mammographic tumor features of breast cancer may carry prognostic information. The potential benefit of using the combined information obtained from breast density, mammographic tumor features, and pathological tumor characteristics has not been extensively studied

Thrive: Success Strategies for the Modern-Day Faculty Member

The THRIVE collection is intended to help faculty thrive in their roles as educators, scholars, researchers, and clinicians. Each section contains a variety of thought-provoking topics that are designed to be easily digested, guide personal reflection, and put into action. Please use the THRIVE collection to help: Individuals study topics on their own, whenever and wherever they want Peer-mentoring or other learning communities study topics in small groups Leaders and planners strategically insert faculty development into existing meetings Faculty identify campus experts for additional learning, grand rounds, etc. If you have questions or want additional information on a topic, simply contact the article author or email [email protected]://digitalcommons.unmc.edu/facdev_books/1000/thumbnail.jp

Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial

Background: Glucagon-like peptide 1 receptor agonists differ in chemical structure, duration of action, and in their effects on clinical outcomes. The cardiovascular effects of once-weekly albiglutide in type 2 diabetes are unknown. We aimed to determine the safety and efficacy of albiglutide in preventing cardiovascular death, myocardial infarction, or stroke. Methods: We did a double-blind, randomised, placebo-controlled trial in 610 sites across 28 countries. We randomly assigned patients aged 40 years and older with type 2 diabetes and cardiovascular disease (at a 1:1 ratio) to groups that either received a subcutaneous injection of albiglutide (30–50 mg, based on glycaemic response and tolerability) or of a matched volume of placebo once a week, in addition to their standard care. Investigators used an interactive voice or web response system to obtain treatment assignment, and patients and all study investigators were masked to their treatment allocation. We hypothesised that albiglutide would be non-inferior to placebo for the primary outcome of the first occurrence of cardiovascular death, myocardial infarction, or stroke, which was assessed in the intention-to-treat population. If non-inferiority was confirmed by an upper limit of the 95% CI for a hazard ratio of less than 1·30, closed testing for superiority was prespecified. This study is registered with ClinicalTrials.gov, number NCT02465515. Findings: Patients were screened between July 1, 2015, and Nov 24, 2016. 10 793 patients were screened and 9463 participants were enrolled and randomly assigned to groups: 4731 patients were assigned to receive albiglutide and 4732 patients to receive placebo. On Nov 8, 2017, it was determined that 611 primary endpoints and a median follow-up of at least 1·5 years had accrued, and participants returned for a final visit and discontinuation from study treatment; the last patient visit was on March 12, 2018. These 9463 patients, the intention-to-treat population, were evaluated for a median duration of 1·6 years and were assessed for the primary outcome. The primary composite outcome occurred in 338 (7%) of 4731 patients at an incidence rate of 4·6 events per 100 person-years in the albiglutide group and in 428 (9%) of 4732 patients at an incidence rate of 5·9 events per 100 person-years in the placebo group (hazard ratio 0·78, 95% CI 0·68–0·90), which indicated that albiglutide was superior to placebo (p<0·0001 for non-inferiority; p=0·0006 for superiority). The incidence of acute pancreatitis (ten patients in the albiglutide group and seven patients in the placebo group), pancreatic cancer (six patients in the albiglutide group and five patients in the placebo group), medullary thyroid carcinoma (zero patients in both groups), and other serious adverse events did not differ between the two groups. There were three (<1%) deaths in the placebo group that were assessed by investigators, who were masked to study drug assignment, to be treatment-related and two (<1%) deaths in the albiglutide group. Interpretation: In patients with type 2 diabetes and cardiovascular disease, albiglutide was superior to placebo with respect to major adverse cardiovascular events. Evidence-based glucagon-like peptide 1 receptor agonists should therefore be considered as part of a comprehensive strategy to reduce the risk of cardiovascular events in patients with type 2 diabetes. Funding: GlaxoSmithKline

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Bayesian modelling of spatial data using Markov random fields, with application to elemental composition of forest soil

Spatial datasets are common in the environmental sciences. In this study we suggest a hierarchical model for a spatial stochastic field. The main focus of this article is to approximate a stochastic field with a Gaussian Markov Random Field (GMRF) to exploit computational advantages of the Markov field, concerning predictions, etc. The variation of the stochastic field is modelled as a linear trend plus microvariation in the form of a GMRF defined on a lattice. To estimate model parameters we adopt a Bayesian perspective, and use Monte Carlo integration with samples from Markov Chain simulations. Our methods does not demand lattice, or near-lattice data, but are developed for a general spatial data-set, leaving the lattice to be specified by the modeller. The model selection problem that comes with the artificial grid is in this article addressed with cross-validation, but we also suggest other alternatives. From the application of the methods to a data set of elemental composition of forest soil, we obtained predictive distributions at arbitrary locations as well as estimates of model parameters

Spatial Statistics and Ancestral Recombination Graphs with Applications in Gene Mapping and Geostatistics

This thesis explores models and algorithms in geostatistics and gene mapping. The first part deals with the use of computationally effective lattice models for inference of data with a continuous spatial index. The fundamental idea is to approximate a Gaussian field with a Gaussian Markov random field (GMRF) on a lattice, and then to conduct a bilinear interpolation of this at non-lattice locations. The resulting model is used for spatial interpolation, both in a Bayesian approach using Markov chain Monte Carlo (MCMC), and in kriging. The second part of the thesis concerns genetic association analysis, particularly multi-locus gene mapping using case-control samples. The algorithms utilize the fact that a population based sample of haplotypes (a collection of alleles at closely linked loci on the same chromosome) mirrors the population history of shared ancestry, mutation, recombination etc. Around the disease locus chromosomes carrying the disease mutation will be more similar than chromosomes that do not carry the disease mutation (on account of increased levels of shared ancestry). Two models and corresponding algorithms for gene mapping are presented. The first explicitly models the genealogy taking the over-sampling of cases into account. Under certain model approximations, a permutation-based test for genetic association is developed that is computationally feasible, even when haplotype phase is unknown. It contends with arbitrary phenotypes and genetic models, allows for neutral mutations, and adapts to marker allele frequencies. The second model utilizes concepts and algorithms from both spatial statistics and statistical genetics. A spatial smoothing model is used for haplotypes, such that structurally similar haplotypes have risk parameters with high correlation. The disease locus is then searched as the place where a local similarity measure produces risk parameters that can discriminate between cases and controls. Different covariance structures and similarity metrics are suggested and compared

Spatial inference for non-lattice data using Markov Random fields

This thesis deals with how computationally effective lattice models could be used for inference of data with a continuous spatial index. The fundamental idea is to approximate a Gaussian field with a Gaussian Markov random field (GMRF) on a lattice. Using a bilinear interpolation at non-lattice locations we get a reasonable model also at non-lattice locations. We can thus exploit the computational benefits of a lattice model even for data with continuous spatial index. In Paper A, a GMRF model is used in a Bayesian approach for prediction of a spatial random field. A hierarchical parametric model is setup, and inference is made by Markov Chain Monte Carlo simulations. In this way we obtain predictors and estimated prediction uncertainties as well as estimates of model parameters. The spatial correlation is modelled as a GMRF on a lattice which is interpolated between lattice points. The methods are tested on a data set of Calcium content in forest soils of southern Sweden. In Paper B, we develop a methodology for kriging large data sets. By approximating a full Gaussian model with an interpolated GMRF the kriging weights can be calculated with less computation. For n observations and a full model, calculation of the kriging weights requires inversion of an n x n covariance matrix. Approximating the model with a GMRF defined on an N x N lattice, the computations can be reduced to inversion of an NxN band limited matrix. For large data sets the full n x n matrix might not be possible to invert, and the GMRF approximation is then not only time saving, but is what makes it possible to perform kriging with the full data set

Learning Statistics by Doing statistics - med datorn som hjälp att förbättra inlärningen

Relevanta tillämpningar och ett konsekvent datoranvändande genomsyrar grundkursen i matematisk statistik på W-programmet. För en lyckosam integrering av datorn krävs, förutom en lämplig blandning av uppgifter, att hänsyn tas till andra aspekter som t.ex. studenternas ''ägandeskap'' vid sin inlärning

Studenter lär varandra, erfarenheter från en grundkurs i matematisk statistik

Går det att undvika passiva studenter som fokuserar på tentaläsning? Räcker det att låta studenterna arbeta med problemlösning, laborationer och projektarbeten, eller måste kanske undervisningen omorganiseras? Kan man få studenterna att själva ta ansvar för sin inlärning, att läsa mer i boken och att arbeta mer aktivt med materialet? På en av våra grundkurser i matematisk statistik i Lund har vi med goda resultat frångått det vanliga upplägget med föreläsningar och räkneövningar. Istället har vi sedan tre år tillämpat samarbetslärande i smågrupper, en metod som använts i varianter i åtskilliga år i USA och Australien. I Sverige har vår form av metoden utvecklats i Luleå av Andrejs Dunkels och Kerstin Vännman

How can we use computers to enhance learning?

How do we, in a basic statistics course, create a learning environment where the computer is a natural tool? Different aspects to be considered are the students' feeling of ownership and the importance of integrating the computer exercises in the course syllabus. The teacher has different tools to achieve a computer-supported learning environment and we give some examples. The actual design of the exercises is of great importance and we have identified three types of computer exercises, each of which we give several examples. A computer-supported basic statistics course for environmental engineers with a focus on applications will serve as a course example. For this course we discuss students' outcome, considering both students' attitudes and learning outcome, using the SOLO-taxonomy