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- Niemi, MEK, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 2. |
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| 3. |
- Adare, A., et al.
(författare)
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Measurement of High-p(T) single electrons from heavy-flavor decays in p+p collisions at root s=200 GeV
- 2006
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Ingår i: Physical Review Letters. - 1079-7114. ; 97:25
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Tidskriftsartikel (refereegranskat)abstract
- The momentum distribution of electrons from decays of heavy flavor (charm and bottom) for midrapidity |y|< 0.35 in p+p collisions at s=200 GeV has been measured by the PHENIX experiment at the BNL Relativistic Heavy Ion Collider over the transverse momentum range 0.3 < p(T)< 9 GeV/c. Two independent methods have been used to determine the heavy-flavor yields, and the results are in good agreement with each other. A fixed-order-plus-next-to-leading-log perturbative QCD calculation agrees with the data within the theoretical and experimental uncertainties, with the data/theory ratio of 1.71 +/- 0.02(stat)+/- 0.18(sys) for 0.3 < p(T)< 9 GeV/c. The total charm production cross section at this energy has also been deduced to be sigma(cc)=567 +/- 57(stat)+/- 193(sys) mu b.
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| 4. |
- Burgess, S., et al.
(författare)
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Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables
- 2010
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Ingår i: Statistics in medicine. - : Wiley. - 1097-0258 .- 0277-6715. ; 29:12, s. 1298-311
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Tidskriftsartikel (refereegranskat)abstract
- Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context of multiple genetic markers measured in multiple studies, based on the analysis of individual participant data. First, for a single genetic marker in one study, we show that the usual ratio of coefficients approach can be reformulated as a regression with heterogeneous error in the explanatory variable. This can be implemented using a Bayesian approach, which is next extended to include multiple genetic markers. We then propose a hierarchical model for undertaking a meta-analysis of multiple studies, in which it is not necessary that the same genetic markers are measured in each study. This provides an overall estimate of the causal relationship between the phenotype and the outcome, and an assessment of its heterogeneity across studies. As an example, we estimate the causal relationship of blood concentrations of C-reactive protein on fibrinogen levels using data from 11 studies. These methods provide a flexible framework for efficient estimation of causal relationships derived from multiple studies. Issues discussed include weak instrument bias, analysis of binary outcome data such as disease risk, missing genetic data, and the use of haplotypes.
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| 8. |
- Beumer, B. R., et al.
(författare)
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Impact of muscle mass on survival of patients with hepatocellular carcinoma after liver transplantation beyond the Milan criteria
- 2022
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Ingår i: Journal of Cachexia, Sarcopenia and Muscle. - : Wiley. - 2190-5991 .- 2190-6009. ; 13:5, s. 2373-2382
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Tidskriftsartikel (refereegranskat)abstract
- Background: Access to the liver transplant waitlist for patients with hepatocellular carcinoma (HCC) depends on tumour presentation, biology, and response to treatments. The Milan Criteria (MC) represent the benchmark for expanded criteria that incorporate additional prognostic factors. The purpose of this study was to determine the added value of skeletal muscle index (SMI) in HCC patients beyond the MC. Method: Patients with HCC that were transplanted beyond the MC were included in this retrospective multicentre study. SMI was quantified using the Computed Tomography (CT) within 3months prior to transplantation. Cox regression models were used to identify predictors of overall survival (OS). The discriminative performance of SMI extended Metroticket 2.0 and AFP models was also assessed. Results: Out of 889 patients transplanted outside the MC, 528 had a CT scan within 3months prior to liver transplantation (LT), of whom 176 (33%) were classified as sarcopenic. The median time between assessment of the SMI and LT was 1.8months (IQR: 0.77–2.67). The median follow-up period was 5.1 95% CI [4.7–5.5] years, with a total of 177 recorded deaths from any cause. In a linear regression model with SMI as the dependent variable, only male gender (8.55 95% CI [6.51–10.59], P<0.001) and body mass index (0.74 95% CI [0.59–0.89], P<0.001) were significant. Univariable survival analysis of patients with sarcopenia versus patients without sarcopenia showed a significant difference in OS (HR 1.44 95% CI [1.07−1.94], P=0.018). Also the SMI was significant (HR 0.98 95% CI [0.96–0.99], P=0.014). The survival difference between the lowest SMI quartile versus the highest SMI quartile was significant (log-rank: P=0.005) with 5year OS of 57% and 71%, respectively. Data from 423 patients, describing 139 deaths, was used for multivariate analysis. Both sarcopenia (HR 1.45 95% CI [1.02−2.05], P=0.036) and SMI were (HR 0.98 95% CI [0.95–0.99], P=0.035) significant. On the survival scale this translates to a 5year OS difference of 11% between sarcopenia and no sarcopenia. Whereas for SMI, this translates to a survival difference of 8% between first and third quartiles for both genders. Conclusions: Overall, we can conclude that higher muscle mass contributes to a better long-term survival. However, for individual patients, low muscle mass should not be considered an absolute contra-indication for LT as its discriminatory performance was limited.
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