2,215 research outputs found

    Power calculator for instrumental variable analysis in pharmacoepidemiology

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    Background: Instrumental variable analysis, for example with physicians' prescribing preferences as an instrument for medications issued in primary care, is an increasingly popular method in the field of pharmacoepidemiology. Existing power calculators for studies using instrumental variable analysis, such as Mendelian randomization power calculators, do not allow for the structure of research questions in this field. This is because the analysis in pharmacoepidemiology will typically have stronger instruments and detect larger causal effects than in other fields. Consequently, there is a need for dedicated power calculators for pharmacoepidemiological research. Methods and Results: The formula for calculating the power of a study using instrumental variable analysis in the context of pharmacoepidemiology is derived before being validated by a simulation study. The formula is applicable for studies using a single binary instrument to analyse the causal effect of a binary exposure on a continuous outcome. An online calculator, as well as packages in both R and Stata, are provided for the implementation of the formula by others. Conclusions: The statistical power of instrumental variable analysis in pharmacoepidemiological studies to detect a clinically meaningful treatment effect is an important consideration. Research questions in this field have distinct structures that must be accounted for when calculating power. The formula presented differs from existing instrumental variable power formulae due to its parametrization, which is designed specifically for ease of use by pharmacoepidemiologists.This work was supported by the Perros Trust and the Integrative Epidemiology Unit. The Integrative Epidemiology Unit is supported by the Medical Research Council and the University of Bristol [grant number MC_UU_12013/9]. S.B. is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 204623/Z/16/Z)

    GPs’ strategies in exploring the preschool child’s wellbeing in the paediatric consultation

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    Background: Although General Practitioners (GPs) are uniquely placed to identify children with emotional, social, and behavioural problems, they succeed in identifying only a small number of them. The aim of this article is to explore the strategies, methods, and tools employed by GPs in the assessment of the preschool child’s emotional, mental, social, and behavioural health. We look at how GPs address parental care of the child in general and in situations where GPs have a particular awareness of the child. Method: Twenty-eight Danish GPs were purposively selected to take part in a qualitative study which combined focus-group discussions, observation of child consultations, and individual interviews with GPs. Results: Analysis of the data suggests that GPs have developed a set of methods, and strategies to assess the preschool child and parental care of the child. They look beyond paying narrow attention to the physical health of the child and they have expanded their practice to include the relations and interactions in the consultation room. The physical examination of the child continues to play a central role in doctor-child communication. Conclusion: The participating GPs’ strategies helped them to assess the wellbeing of the preschool child but they often find it difficult to share their impressions with parents

    Low temperature scattering with the R-matrix method: the Morse potential

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    Experiments are starting to probe collisions and chemical reactions between atoms and molecules at ultra-low temperatures. We have developed a new theoretical procedure for studying these collisions using the R-matrix method. Here this method is tested for the atom -- atom collisions described by a Morse potential. Analytic solutions for continuum states of the Morse potential are derived and compared with numerical results computed using an R-matrix method where the inner region wavefunctions are obtained using a standard nuclear motion algorithm. Results are given for eigenphases and scattering lengths. Excellent agreement is obtained in all cases. Progress in developing a general procedure for treating ultra-low energy reactive and non-reactive collisions is discussed.Comment: 18 pages, 6 figures, 3 tables, conferenc

    Globular Cluster Distance Determinations

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    The present status of the distance scale to Galactic globular clusters is reviewed. Six distance determination techniques which are deemed to be most reliable are discussed in depth. These different techniques are used to calibrate the absolute magnitude of the RR Lyrae stars. The various calibrations fall into three groups. Main sequence fitting using Hipparcos parallaxes, theoretical HB models and the RR Lyrae in the LMC all favor a bright calibration, implying a `long' globular cluster distance scale. White dwarf fitting and the astrometric distances yield a somewhat fainter RR Lyrae calibration, while the statistical parallax solution yields faint RR Lyrae stars implying a `short' distance scale to globular clusters. Various secondary distance indicators discussed all favor the long distance scale. The `long' and `short' distance scales differ by (0.31+/-0.16) mag. Averaging together all of the different distance determinations yields Mv(RR) = (0.23+/-0.04)([Fe/H] + 1.6) + (0.56+/-0.12) mag.Comment: Invited review article to appear in: `Post-Hipparcos Cosmic Candles', A. Heck & F. Caputo (Eds), Kluwer Academic Publ., Dordrecht, in pres

    Genetically Predicted Blood Pressure and Risk of Atrial Fibrillation.

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    Observational studies have shown an association between hypertension and atrial fibrillation (AF). Aggressive blood pressure management in patients with known AF reduces overall arrhythmia burden, but it remains unclear whether hypertension is causative for AF. To address this question, this study explored the relationship between genetic predictors of blood pressure and risk of AF. We secondarily explored the relationship between genetically proxied use of antihypertensive drugs and risk of AF. Two-sample Mendelian randomization was performed using an inverse-variance weighted meta-analysis with weighted median Mendelian randomization and Egger intercept tests performed as sensitivity analyses. Summary statistics for systolic blood pressure, diastolic blood pressure, and pulse pressure were obtained from the International Consortium of Blood Pressure and the UK Biobank discovery analysis and AF from the 2018 Atrial Fibrillation Genetics Consortium multiethnic genome-wide association studies. Increases in genetically proxied systolic blood pressure, diastolic blood pressure, or pulse pressure by 10 mm Hg were associated with increased odds of AF (systolic blood pressure: odds ratio [OR], 1.17 [95% CI, 1.11-1.22]; P=1×10-11; diastolic blood pressure: OR, 1.25 [95% CI, 1.16-1.35]; P=3×10-8; pulse pressure: OR, 1.1 [95% CI, 1.0-1.2]; P=0.05). Decreases in systolic blood pressure by 10 mm Hg estimated by genetic proxies of antihypertensive medications showed calcium channel blockers (OR, 0.66 [95% CI, 0.57-0.76]; P=8×10-9) and β-blockers (OR, 0.61 [95% CI, 0.46-0.81]; P=6×10-4) decreased the risk of AF. Blood pressure-increasing genetic variants were associated with increased risk of AF, consistent with a causal relationship between blood pressure and AF. These data support the concept that blood pressure reduction with calcium channel blockade or β-blockade could reduce the risk of AF

    Mendelian randomization for studying the effects of perturbing drug targets [version 1; peer review: awaiting peer review]

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    Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline

    Hsp90 orchestrates transcriptional regulation by Hsf1 and cell wall remodelling by MAPK signalling during thermal adaptation in a pathogenic yeast

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    Acknowledgments We thank Rebecca Shapiro for creating CaLC1819, CaLC1855 and CaLC1875, Gillian Milne for help with EM, Aaron Mitchell for generously providing the transposon insertion mutant library, Jesus Pla for generously providing the hog1 hst7 mutant, and Cathy Collins for technical assistance.Peer reviewedPublisher PD

    Mendelian randomization for studying the effects of perturbing drug targets [version 2; peer review: 3 approved, 1 approved with reservations]

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    Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline
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