The Performance of the Turek-Fletcher Model Averaged Confidence Interval

We consider the model averaged tail area (MATA) confidence interval proposed by Turek and Fletcher, CSDA, 2012, in the simple situation in which we average over two nested linear regression models. We prove that the MATA for any reasonable weight function belongs to the class of confidence intervals defined by Kabaila and Giri, JSPI, 2009. Each confidence interval in this class is specified by two functions b and s. Kabaila and Giri show how to compute these functions so as to optimize these intervals in terms of satisfying the coverage constraint and minimizing the expected length for the simpler model, while ensuring that the expected length has desirable properties for the full model. These Kabaila and Giri "optimized" intervals provide an upper bound on the performance of the MATA for an arbitrary weight function. This fact is used to evaluate the MATA for a broad class of weights based on exponentiating a criterion related to Mallows' C_P. Our results show that, while far from ideal, this MATA performs surprisingly well, provided that we choose a member of this class that does not put too much weight on the simpler model

Fletcher-Turek Model Averaged Profile Likelihood Confidence Intervals

We evaluate the model averaged profile likelihood confidence intervals proposed by Fletcher and Turek (2011) in a simple situation in which there are two linear regression models over which we average. We obtain exact expressions for the coverage and the scaled expected length of the intervals and use these to compute these quantities in particular situations. We show that the Fletcher-Turek confidence intervals can have coverage well below the nominal coverage and expected length greater than that of the standard confidence interval with coverage equal to the same minimum coverage. In these situations, the Fletcher-Turek confidence intervals are unfortunately not better than the standard confidence interval used after model selection but ignoring the model selection process

Applying metabolomics to cardiometabolic intervention studies and trials: past experiences and a roadmap for the future

Metabolomics and lipidomics are emerging methods for detailed phenotyping of small molecules in samples. It is hoped that such data will: (i) enhance baseline prediction of patient response to pharmacotherapies (beneficial or adverse); (ii) reveal changes in metabolites shortly after initiation of therapy that may predict patient response, including adverse effects, before routine biomarkers are altered; and( iii) give new insights into mechanisms of drug action, particularly where the results of a trial of a new agent were unexpected, and thus help future drug development. In these ways, metabolomics could enhance research findings from intervention studies. This narrative review provides an overview of metabolomics and lipidomics in early clinical intervention studies for investigation of mechanisms of drug action and prediction of drug response (both desired and undesired). We highlight early examples from drug intervention studies associated with cardiometabolic disease. Despite the strengths of such studies, particularly the use of state-of-the-art technologies and advanced statistical methods, currently published studies in the metabolomics arena are largely underpowered and should be considered as hypothesis-generating. In order for metabolomics to meaningfully improve stratified medicine approaches to patient treatment, there is a need for higher quality studies, with better exploitation of biobanks from randomized clinical trials i.e. with large sample size, adjudicated outcomes, standardized procedures, validation cohorts, comparison witth routine biochemistry and both active and control/placebo arms. On the basis of this review, and based on our research experience using clinically established biomarkers, we propose steps to more speedily advance this area of research towards potential clinical impact

Inflammatory markers as novel predictors of cardiovascular disease

Inflammation is widely considered to be an important contributing factor in atherogenesis and the risk of atherothrombotic complications. Baseline measurements of some inflammatory markers are known to be predictive risk factors for future cardiovascular disease (CVD) events in prospective epidemiological studies. Inflammatory markers dominant in the literature are acute phase response (APR)-associated and include fibrinogen, C-reactive protein (CRP) and, more recently, interleukin- (IL-) 6. This thesis reviews the literature and suggests the need for further research into novel inflammatory markers of CVD risk. The broad aim was to expand on limited existing data and ascertain if circulating levels of four novel inflammatory markers (tumour necrosis factor alpha [TNF alpha], IL-18, soluble CD40 ligand [sCD40L], and matrix metalloproteinase-9 [MMP-9]) are associated with classical cardiovascular risk factors, and with future CVD events in several epidemiological studies. In studies of pre-analytical variables, all four markers had commercially available assays acceptable for epidemiological use, but only IL-18 and TNF alpha displayed assay stability and the ability to be measured in plasma or serum. Due to limited serum samples, MMP-9 and sCD40L were less extensively measured. Results suggest a moderate positive association of MMP-9 with coronary heart disease (CHD) risk (although confounded by smoking and markers of general inflammation), while serum sCD40L may be moderately inversely related to CHD risk. More data is required for these markers. IL-18 and TNF alpha displayed similar degrees of short-term biological variability and regression dilution as CRP. Population distributions of both cytokines were consistent with limited previous reports. Both displayed associations with conventional vascular risk factors (such as age, gender, HDL cholesterol, and smoking), although interestingly, associations with epidemiological measures of obesity were poor. Both cytokines demonstrated moderate associations with vascular disease in a retrospective CHD study. In 3 prospective CHD or CVD studies, IL-18 demonstrated consistent but moderate associations with risk of vascular events in age- and sex-adjusted models (Odds ratio [OR]~1.6 in the top versus bottom third of the population). The association became borderline significant after adjustment for conventional risk markers. Associations of TNF alpha with risk of CHD in these studies were inconsistent, and more data are needed. In 3 prospective stroke studies, TNF alpha demonstrated some moderate associations with acute stroke outcome and recurrent stroke risk, but not with incident stroke in the elderly with vascular disease. IL-18 demonstrated no association with risk or outcome in any stroke study. Meta-analysis in 4 suitable prospective studies showed (in full adjustment models) that IL-18 (OR 1.18 [95% CI 0.95-1.48] comparing extreme thirds) and TNF alpha OR 1.05 [0.67-1.64]) have at best weak independent associations with CVD risk. Therefore these markers are unlikely to add significantly to clinical vascular risk prediction models, although these cytokines may still be of biological significance and potential therapeutic targets. More data is required for these markers. In conclusion IL-18, TNF alpha, MMP-9 and sCD40L may show weak associations with CVD. However, despite animal and tissue models indicating that they may play pivotal roles in atherogenesis, circulating concentrations of these inflammatory markers have limited independent vascular risk associations. Elevated circulating levels of APR-associated markers may sensitively reflect exposure to a wide range of adverse pro-inflammatory stimuli including lifestyle exposures, whereas some other inflammatory markers may not

A Novel Method of Anatomical Data Acquisition Using the Perceptron ScanWorks V5 Scanner

A drastic reduction in the time available for cadaveric dissection and anatomy teaching in medical and surgical education has increased the requirement to supplement learning with the use of virtual gross anatomy training tools. In light of this, a number of known studies have approached the task of sourcing anatomical data from cadaveric material for end us in creating 3D reconstructions of the human body by producing vast image libraries of anatomical cross sections. However, the processing involved in the conversion of cross sectional images to reconstructions in 3D elicits a number of problems in creating an accurate and adequately detailed end product, suitable for educational. In this paperwe have employed a unique approach in a pilot study acquire anatomical data for end-use in 3D anatomical reconstruction by using topographical 3D laser scanning and high-resolution digital photography of all clinically relevant structures from the lower limb of a male cadaveric specimen. As a result a comprehensive high-resolution dataset, comprising 3D laser scanned data and corresponding colour photography was obtained from all clinically relevant gross anatomical structures associated with the male lower limb. This unique dataset allows a very unique and novel way to capture anatomical data and saves on the laborious processing of image segmentation common to conventional image acquisition used clinically, like CT and MRI scans. From this, it provides a dataset which can then be used across a number of commercial products dependent on the end-users requirements for development of computer training packages in medical and surgical rehearsal

A Novel Method of Anatomical Data Acquisition Using the Perceptron ScanWorks V5 Scanner

A drastic reduction in the time available for cadaveric dissection and anatomy teaching in medical and surgical education has increased the requirement to supplement learning with the use of virtual gross anatomy training tools. In light of this, a number of known studies have approached the task of sourcing anatomical data from cadaveric material for end us in creating 3D reconstructions of the human body by producing vast image libraries of anatomical cross sections. However, the processing involved in the conversion of cross sectional images to reconstructions in 3D elicits a number of problems in creating an accurate and adequately detailed end product, suitable for educational. In this paperwe have employed a unique approach in a pilot study acquire anatomical data for end-use in 3D anatomical reconstruction by using topographical 3D laser scanning and high-resolution digital photography of all clinically relevant structures from the lower limb of a male cadaveric specimen. As a result a comprehensive high-resolution dataset, comprising 3D laser scanned data and corresponding colour photography was obtained from all clinically relevant gross anatomical structures associated with the male lower limb. This unique dataset allows a very unique and novel way to capture anatomical data and saves on the laborious processing of image segmentation common to conventional image acquisition used clinically, like CT and MRI scans. From this, it provides a dataset which can then be used across a number of commercial products dependent on the end-users requirements for development of computer training packages in medical and surgical rehearsal

Thyroid stimulating hormone (TSH) ≥2.5mU/l in early pregnancy: prevalence and subsequent outcomes

Objective: There remains controversy over how women with abnormal thyroid function tests in pregnancy should be classified. In this study we assessed the proportion of women with thyroid stimulating hormone (TSH) ≥ 2.5 mU/l in a large obstetric cohort, and examined how many have gone on to develop thyroid disease in the years since their pregnancy. Study design: 4643 women were recruited and samples taken in early pregnancy between 2007 and 2010. Thyroid function tests were analysed in 2014; in women with raised TSH computerised health records and prescription databases were used to identify thyroid disease detected since pregnancy. Results: 58 women (1.5%) had a TSH over 5 mU/l and 396 women (10.3%) had TSH between 2.5 and 5 mU/l. Women with TSH > 5mU/l delivered infants of lower birthweight than those with TSH < 2.5 mU/l; there were no other differences in obstetric outcomes between the groups. Of those who have had thyroid tests since their pregnancy, 78% of those with TSH > 5 mU/l and 19% of those with TSH between 2.5 and 5 mU/l have gone on to be diagnosed with thyroid disease. Conclusions: Using a TSH cut-off of 2.5 mU/l in keeping with European and US guidelines means that over 12% of women in this cohort would be classified as having subclinical hypothyroidism. Treatment and monitoring of these women would have major implications for planning of obstetric services

Response by Welsh et al to letter regarding article “Urinary sodium excretion, blood pressure, and risk of future cardiovascular disease and mortality in subjects without prior cardiovascular disease”

No abstract available

Finite sample properties of the Buckland-Burnham-Augustin confidence interval centered on a model averaged estimator

We consider the confidence interval centered on a frequentist model averaged estimator that was proposed by Buckland, Burnham & Augustin (1997). In the context of a simple testbed situation involving two linear regression models, we derive exact expressions for the confidence interval and then for the coverage and scaled expected length of the confidence interval. We use these measures to explore the exact finite sample performance of the Buckland-Burnham-Augustin confidence interval. We also explore the limiting asymptotic case (as the residual degrees of freedom increases) and compare our results for this case to those obtained for the asymptotic coverage of the confidence interval by Hjort & Claeskens (2003).Comment: Journal of Statistical Planning and Inference (2019

The emergence of proton nuclear magnetic resonance metabolomics in the cardiovascular arena as viewed from a clinical perspective

The ability to phenotype metabolic profiles in serum has increased substantially in recent years with the advent of metabolomics. Metabolomics is the study of the metabolome, defined as those molecules with an atomic mass less than 1.5 kDa. There are two main metabolomics methods: mass spectrometry (MS) and proton nuclear magnetic resonance (1H NMR) spectroscopy, each with its respective benefits and limitations. MS has greater sensitivity and so can detect many more metabolites. However, its cost (especially when heavy labelled internal standards are required for absolute quantitation) and quality control is sub-optimal for large cohorts. 1H NMR is less sensitive but sample preparation is generally faster and analysis times shorter, resulting in markedly lower analysis costs. 1H NMR is robust, reproducible and can provide absolute quantitation of many metabolites. Of particular relevance to cardio-metabolic disease is the ability of 1H NMR to provide detailed quantitative data on amino acids, fatty acids and other metabolites as well as lipoprotein subparticle concentrations and size. Early epidemiological studies suggest promise, however, this is an emerging field and more data is required before we can determine the clinical utility of these measures to improve disease prediction and treatment. This review describes the theoretical basis of 1H NMR; compares MS and 1H NMR and provides a tabular overview of recent 1H NMR-based research findings in the atherosclerosis field, describing the design and scope of studies conducted to date. 1H NMR metabolomics-CVD related research is emerging, however further large, robustly conducted prospective, genetic and intervention studies are needed to advance research on CVD risk prediction and to identify causal pathways amenable to intervention