40 results found
Schweinsberg M, Feldman M, Staub N, et al., 2021, Same data, different conclusions: Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis, ORGANIZATIONAL BEHAVIOR AND HUMAN DECISION PROCESSES, Vol: 165, Pages: 228-249, ISSN: 0749-5978
Gramatica M, Congdon P, Liverani S, 2021, Bayesian modelling for spatially misaligned health areal data: A multiple membership approach, JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, Vol: 70, Pages: 645-666, ISSN: 0035-9254
Ali R, Qureshi N, Liverani S, et al., 2020, Left atrial enhancement correlates with myocardial conduction velocity in patients with persistent atrial fibrillation, Frontiers in Physiology, Vol: 11, ISSN: 1664-042X
Background: Conduction velocity (CV) heterogeneity and myocardial fibrosis both promote re-entry, but the relationship between fibrosis as determined by left atrial (LA) late-gadolinium enhanced cardiac magnetic resonance imaging (LGE-CMRI) and CV remains uncertain.Objective: Although average CV has been shown to correlate with regional LGE-CMRI in patients with persistent AF, we test the hypothesis that a localized relationship exists to underpin LGE-CMRI as a minimally invasive tool to map myocardial conduction properties for risk stratification and treatment guidance.Method: 3D LA electroanatomic maps during LA pacing were acquired from eight patients with persistent AF following electrical cardioversion. Local CVs were computed using triads of concurrently acquired electrograms and were co-registered to allow correlation with LA wall intensities obtained from LGE-CMRI, quantified using normalized intensity (NI) and image intensity ratio (IIR). Association was evaluated using multilevel linear regression.Results: An association between CV and LGE-CMRI intensity was observed at scales comparable to the size of a mapping electrode: −0.11 m/s per unit increase in NI (P < 0.001) and −0.96 m/s per unit increase in IIR (P < 0.001). The magnitude of this change decreased with larger measurement area. Reproducibility of the association was observed with NI, but not with IIR.Conclusion: At clinically relevant spatial scales, comparable to area of a mapping catheter electrode, LGE-CMRI correlates with CV. Measurement scale is important in accurately quantifying the association of CV and LGE-CMRI intensity. Importantly, NI, but not IIR, accounts for changes in the dynamic range of CMRI and enables quantitative reproducibility of the association.
Lavigne A, Freni Sterrantino A, Fecht D, et al., 2020, A spatial joint analysis of metal constituents of ambient particulate matter and mortality in England, Environmental Epidemiology, Vol: 4, Pages: e098-e098, ISSN: 2474-7882
Background Few studies have investigated associations between metal components of particulate matter on mortality due to well-known issues of multicollinearity. Here, we analyze these exposures jointly to evaluate their associations with mortality on small area data.Methods We fit a Bayesian Profile Regression (BPR) to account for the multicollinearity in the elemental components (iron, copper and zinc) of PM10 and PM2.5. The models are developed in relation to mortality from cardiovascular and respiratory disease and lung cancer incidence in 2008-11 at small area level, for a population of 13.6 million in the London-Oxford area of England.Results From the BPR, we identified higher risks in the PM10 fraction cluster likely to represent the study area, excluding London, for cardiovascular mortality RR 1.07 (95%CI 1.02, 1.12) and for respiratory mortality RR 1.06 (95%CI 0.99, 1.31), compared to the study mean. For PM2.5 fraction, higher risks were seen for cardiovascular mortality RR 1.55 (CI 95% 1.38, 1.71) and respiratory mortality RR 1.51 (CI 95% 1.33, 1.72), likely to represent the 'highways' cluster. We did not find relevant associations for lung cancer incidence.Conclusion Our analysis showed small but not fully consistent adverse associations between health outcomes and particulate metal exposures. The BPR approach identified subpopulations with unique exposure profiles and provided information about the geographical location of these to help interpret findings.
Liverani S, Leigh L, Hudson IL, et al., 2020, Clustering method for censored and collinear survival data, COMPUTATIONAL STATISTICS, Vol: 36, Pages: 35-60, ISSN: 0943-4062
Ryan JM, Cameron MH, Liverani S, et al., 2020, Incidence of falls among adults with cerebral palsy: a cohort study using primary care data, DEVELOPMENTAL MEDICINE AND CHILD NEUROLOGY, Vol: 62, Pages: 477-482, ISSN: 0012-1622
Liu X, Liverani S, Smith KJ, et al., 2020, Modeling tails for collinear data with outliers in the English Longitudinal Study of Ageing: Quantile profile regression, BIOMETRICAL JOURNAL, Vol: 62, Pages: 916-931, ISSN: 0323-3847
Bonaventura M, Ciotti V, Panzarasa P, et al., 2020, Predicting success in the worldwide start-up network, SCIENTIFIC REPORTS, Vol: 10, ISSN: 2045-2322
Lavigne A, Freni Sterrantino A, Liverani S, et al., 2019, Associations between metal constituents of ambient particulate matter and mortality in England; an ecological study, BMJ Open, Vol: 9, ISSN: 2044-6055
Objectives To investigate long-term associations between metal components of particulate matter and mortality and lung cancer incidenceDesign Small area (ecological) study Setting Population living in all wards (~9000 individuals per ward) in the London and Oxford area of England, comprising 13.6 million individuals Exposure and Outcome measures We used land use regression (LUR) models originally used in the Transport related Air Pollution and Health impacts – Integrated Methodologies for Assessing Particulate Matter (TRANSPHORM) study to estimate exposure to copper, iron and zinc in ambient air particulate matter. We examined associations of metal exposure with Office for National Statistics mortality data from cardiovascular (CVD) and respiratory causes and with lung cancer incidence in 2008-11.Results There were 108,478 CVD deaths, 48,483 respiratory deaths and 24,849 incident cases of lung cancer in the study period and area. Using Poisson regression models adjusted for area-level deprivation, tobacco sales and ethnicity, we found associations between cardiovascular mortality and PM2.5 copper with interdecile range (IDR-2.6-5.7 ng/m3) and IDR Relative risk (RR) 1.005 (95%CI 1.001, 1.009) and between respiratory mortality and PM10 zinc (IDR 1135-153 ng/m3) and IDR RR 1.136 (95%CI 1.010, 1.277). We did not find relevant associations for lung cancer incidence. Metal elements were highly correlated.Conclusion Our analysis showed small but not fully consistent adverse associations between mortality and particulate metal exposures likely derived from non-tailpipe road traffic emissions (brake and tyre-wear), which have previously been associated with increases in inflammatory markers in the blood.
Ryan JM, Peterson MD, Matthews A, et al., 2019, Noncommunicable disease among adults with cerebral palsy A matched cohort study, NEUROLOGY, Vol: 93, Pages: E1385-E1396, ISSN: 0028-3878
O'Connell NE, Smith KJ, Peterson MD, et al., 2019, Incidence of osteoarthritis, osteoporosis and inflammatory musculoskeletal diseases in adults with cerebral palsy: A population-based cohort study, BONE, Vol: 125, Pages: 30-35, ISSN: 8756-3282
Ryan JM, Peterson MD, Ryan N, et al., 2019, Mortality due to cardiovascular disease, respiratory disease, and cancer in adults with cerebral palsy, DEVELOPMENTAL MEDICINE AND CHILD NEUROLOGY, Vol: 61, Pages: 924-+, ISSN: 0012-1622
Smith KJ, Peterson MD, O'Connell NE, et al., 2019, Risk of Depression and Anxiety in Adults With Cerebral Palsy, JAMA NEUROLOGY, Vol: 76, Pages: 294-300, ISSN: 2168-6149
Huang R, Xu W, Wang Y, et al., 2019, Performance Comparison of Julia Distributed Implementations of Dirichlet Process Mixture Models, IEEE International Conference on Big Data (Big Data), Publisher: IEEE, Pages: 3350-3354, ISSN: 2639-1589
Edwards KD, Takata N, Johansson M, et al., 2018, Circadian clock components control daily growth activities by modulating cytokinin levels and cell division-associated gene expression in Populus trees, PLANT CELL AND ENVIRONMENT, Vol: 41, Pages: 1468-1482, ISSN: 0140-7791
Coker E, Liverani S, Su JG, et al., 2018, Multi-pollutant Modeling Through Examination of Susceptible Subpopulations Using Profile Regression, CURRENT ENVIRONMENTAL HEALTH REPORTS, Vol: 5, Pages: 59-69
Hirth M, Liverani S, Mahlow S, et al., 2017, Metabolic profiling identifies trehalose as an abundant and diurnally fluctuating metabolite in the microalga Ostreococcus tauri, METABOLOMICS, Vol: 13, ISSN: 1573-3882
Ryan JM, Mahmoudi E, Hurvitz EA, et al., 2016, THE PREVALENCE OF AGE-RELATED CHRONIC CONDITIONS IN ADULTS WITH CEREBRAL PALSY., Publisher: OXFORD UNIV PRESS INC, Pages: 420-421, ISSN: 0016-9013
Liverani S, Smith JQ, 2016, Bayesian selection of graphical regulatory models, INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, Vol: 77, Pages: 87-104, ISSN: 0888-613X
Liverani S, Lavigne A, Blangiardo M, 2016, Modelling collinear and spatially correlated data.
In this work we present a statistical approach to distinguish and interpret the complex relationship between several predictors and a response variable at the small area level, in the presence of (i) high correlation between the predictors and (ii) spatial correlation for the response. Covariates which are highly correlated create collinearity problems when used in a standard multiple regression model. Many methods have been proposed in the literature to address this issue. A very common approach is to create an index which aggregates all the highly correlated variables of interest. For example, it is well known that there is a relationship between social deprivation measured through the Multiple Deprivation Index (IMD) and air pollution; this index is then used as a confounder in assessing the effect of air pollution on health outcomes (e.g. respiratory hospital admissions or mortality). However it would be more informative to look specifically at each domain of the IMD and at its relationship with air pollution to better understand its role as a confounder in the epidemiological analyses. In this paper we illustrate how the complex relationships between the domains of IMD and air pollution can be deconstructed and analysed using profile regression, a Bayesian non-parametric model for clustering responses and covariates simultaneously. Moreover, we include an intrinsic spatial conditional autoregressive (ICAR) term to account for the spatial correlation of the response variable.
Liverani S, Lavigne A, Blangiardo MAG, 2016, Modelling collinear and spatially correlated data, Spatial and Spatio-temporal Epidemiology, ISSN: 1877-5853
In this work we present a statistical approach to distinguish and interpret the complexrelationship between several predictors and a response variable at the small area level, in thepresence of i) high correlation between the predictors and ii) spatial correlation for the response.Covariates which are highly correlated create collinearity problems when used in a standardmultiple regression model. Many methods have been proposed in the literature to address thisissue. A very common approach is to create an index which aggregates all the highly correlatedvariables of interest. For example, it is well known that there is a relationship between socialdeprivation measured through the Multiple Deprivation Index (IMD) and air pollution; thisindex is then used as a confounder in assessing the effect of air pollution on health outcomes(e.g. respiratory hospital admissions or mortality). However it would be more informative tolook specifically at each domain of the IMD and at its relationship with air pollution to betterunderstand its role as a confounder in the epidemiological analyses.In this paper we illustrate how the complex relationships between the domains of IMD and airpollution can be deconstructed and analysed using profile regression, a Bayesian non-parametricmodel for clustering responses and covariates simultaneously. Moreover, we include an intrinsicspatial conditional autoregressive (ICAR) term to account for the spatial correlation of theresponse variable.
Li A, Smith KJ, Liverani S, 2016, The association of cardiometabolic functioning clusters with depressive symptoms in older adults: results from the English Longitudinal Study of Ageing, Publisher: WILEY-BLACKWELL, Pages: 172-172, ISSN: 0742-3071
Mattei F, Liverani S, Guida F, et al., 2016, Multidimensional analysis of the effect of occupational exposure to organic solvents on lung cancer risk: the ICARE study, Occupational and Environmental Medicine, Vol: 73, Pages: 368-377, ISSN: 1470-7926
Background The association between lung cancer and occupational exposure to organic solvents is discussed. Since different solvents are often used simultaneously, it is difficult to assess the role of individual substances.Objectives The present study is focused on an in-depth investigation of the potential association between lung cancer risk and occupational exposure to a large group of organic solvents, taking into account the well-known risk factors for lung cancer, tobacco smoking and occupational exposure to asbestos.Methods We analysed data from the Investigation of occupational and environmental causes of respiratory cancers (ICARE) study, a large French population-based case–control study, set up between 2001 and 2007. A total of 2276 male cases and 2780 male controls were interviewed, and long-life occupational history was collected. In order to overcome the analytical difficulties created by multiple correlated exposures, we carried out a novel type of analysis based on Bayesian profile regression.Results After analysis with conventional logistic regression methods, none of the 11 solvents examined were associated with lung cancer risk. Through a profile regression approach, we did not observe any significant association between solvent exposure and lung cancer. However, we identified clusters at high risk that are related to occupations known to be at risk of developing lung cancer, such as painters.Conclusions Organic solvents do not appear to be substantial contributors to the occupational risk of lung cancer for the occupations known to be at risk.
Coker E, Liverani S, Ghosh JK, et al., 2016, Multi-pollutant exposure profiles associated with term low birth weight in Los Angeles County, Environment International, Vol: 91, Pages: 1-13, ISSN: 1873-6750
Research indicates that multiple outdoor air pollutants and adverse neighborhood conditions are spatially correlated. Yet health risks associated with concurrent exposure to air pollution mixtures and clustered neighborhood factors remain underexplored. Statistical models to assess the health effects from pollutant mixtures remain limited, due to problems of collinearity between pollutants and area-level covariates, and increases in covariate dimensionality. Here we identify pollutant exposure profiles and neighborhood contextual profiles within Los Angeles (LA) County. We then relate these profiles with term low birth weight (TLBW). We used land use regression to estimate NO2, NO, and PM2.5 concentrations averaged over census block groups to generate pollutant exposure profile clusters and census block group-level contextual profile clusters, using a Bayesian profile regression method. Pollutant profile cluster risk estimation was implemented using a multilevel hierarchical model, adjusting for individual-level covariates, contextual profile cluster random effects, and modeling of spatially structured and unstructured residual error. Our analysis found 13 clusters of pollutant exposure profiles. Correlations between study pollutants varied widely across the 13 pollutant clusters. Pollutant clusters with elevated NO2, NO, and PM2.5 concentrations exhibited increased log odds of TLBW, and those with low PM2.5, NO2, and NO concentrations showed lower log odds of TLBW. The spatial patterning of pollutant cluster effects on TLBW, combined with between-pollutant correlations within pollutant clusters, imply that traffic-related primary pollutants influence pollutant cluster TLBW risks. Furthermore, contextual clusters with the greatest log odds of TLBW had more adverse neighborhood socioeconomic, demographic, and housing conditions. Our data indicate that, while the spatial patterning of high-risk multiple pollutant clusters largely overlaps with adverse contextual neigh
Boulieri A, Liverani S, de Hoogh K, et al., 2016, A space-time multivariate Bayesian model to analyse road traffic accidents by severity, Journal of the Royal Statistical Society. Series A. Statistics in Society, Vol: 180, Pages: 119-139, ISSN: 0964-1998
his paper investigates the dependencies between severity levels ofroad traffic accidents, accounting at the same time for spatial and temporal cor-relations. The study analyses road traffic accidents data at ward level in Englandover the period 2005-2013. We include in our model multivariate spatially struc-tured and unstructured effects to capture the respective dependencies betweenseverities, within a Bayesian hierarchical formulation. We also include a tempo-ral component to capture the time effects and we carry out an extensive modelcomparison. The results show important associations in both spatially structuredand unstructured effects between severities, while a downward temporal trend isobserved for low and high severity levels. Maps of posterior accident rates indi-cate elevated risk within big cities for accidents of low severity and in suburbanareas in the north and on the southern coast of England for accidents of high2Boulieriet al.severity. Posterior probability of extreme rates is used to suggest the presenceof hot spots in a public health perspective.
Coker E, Ghosh J, Jerrett M, et al., 2015, Modeling spatial effects of PM2.5 on term low birth weight in Los Angeles County, ENVIRONMENTAL RESEARCH, Vol: 142, Pages: 354-364, ISSN: 0013-9351
Hastie DI, Liverani S, Richardson S, 2015, Sampling from Dirichlet process mixture models with unknown concentration parameter: mixing issues in large data implementations, STATISTICS AND COMPUTING, Vol: 25, Pages: 1023-1037, ISSN: 0960-3174
Liverani S, Hastie DI, Azizi L, et al., 2015, PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes, JOURNAL OF STATISTICAL SOFTWARE, Vol: 64, Pages: 1-30, ISSN: 1548-7660
Pirani M, Best N, Blangiardo M, et al., 2015, Analysing the health effects of simultaneous exposure to physical and chemical properties of airborne particles, Environment International, Vol: 79, Pages: 56-64, ISSN: 0160-4120
Background:Airborne particles are a complex mix of organic and inorganic compounds, with a range of physical and chemical properties. Estimation of how simultaneous exposure to air particles affects the risk of adverse health response represents a challenge for scientific research and air quality management. In this paper, we present a Bayesian approach that can tackle this problem within the framework of time series analysis.Methods:We used Dirichlet process mixture models to cluster time points with similar multipollutant and response profiles, while adjusting for seasonal cycles, trends and temporal components. Inference was carried out via Markov Chain Monte Carlo methods. We illustrated our approach using daily data of a range of particle metrics and respiratory mortality for London (UK) 2002–2005. To better quantify the average health impact of these particles, we measured the same set of metrics in 2012, and we computed and compared the posterior predictive distributions of mortality under the exposure scenario in 2012 vs 2005.Results:The model resulted in a partition of the days into three clusters. We found a relative risk of 1.02 (95% credible intervals (CI): 1.00, 1.04) for respiratory mortality associated with days characterised by high posterior estimates of non-primary particles, especially nitrate and sulphate. We found a consistent reduction in the airborne particles in 2012 vs 2005 and the analysis of the posterior predictive distributions of respiratory mortality suggested an average annual decrease of − 3.5% (95% CI: − 0.12%, − 5.74%).Conclusions:We proposed an effective approach that enabled the better understanding of hidden structures in multipollutant health effects within time series analysis. It allowed the identification of exposure metrics associated with respiratory mortality and provided a tool to assess the changes in health effects from various policies to control the ambient particle matter mixtures.
Molitor J, Brown IJ, Chan Q, et al., 2014, Blood Pressure Differences Associated With Optimal Macronutrient Intake Trial for Heart Health (OMNIHEART)-Like Diet Compared With a Typical American Diet, HYPERTENSION, Vol: 64, Pages: 1198-U86, ISSN: 0194-911X
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