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1دورية أكاديمية
المساهمون: Fundación La Caixa, NIH - National Institute of Environmental Health Sciences (NIEHS) (Estados Unidos), Agencia Estatal de Investigación (España), Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación (España), Unión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF)
مصطلحات موضوعية: Mediation analysis, Survival analysis, Correlated mediators, Additive models
العلاقة: https://doi.org/10.1101/2024.02.16.24302923Test; info:eu-repo/grantAgreement/MINECO//CP12%2F03080/ES/CP12%2F03080/; info:eu-repo/grantAgreement/MINECO//PI15%2F00071/ES/Metales y arteriosclerosis subclínica: papel de la variación genética y epigenética en genes candidatos/; info:eu-repo/grantAgreement/ES/PID2019-108973RB-C21; medRxiv. 2024 Feb 18:2024.02.16.24302923.; http://hdl.handle.net/20.500.12105/19002Test; medRxiv : the preprint server for health sciences
الإتاحة: https://doi.org/20.500.12105/19002Test
https://doi.org/10.1101/2024.02.16.24302923Test
https://hdl.handle.net/20.500.12105/19002Test -
2دورية أكاديمية
المساهمون: Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), Institut National des Sciences Mathématiques et de leurs Interactions - CNRS Mathématiques (INSMI-CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions - CNRS Mathématiques (INSMI-CNRS)-Centre National de la Recherche Scientifique (CNRS)
المصدر: ISSN: 2194-573X.
مصطلحات موضوعية: correlated mediators direct and indirect effects independent mediators multiple mediators simulation of counterfactuals, correlated mediators, direct and indirect effects, independent mediators, multiple mediators, simulation of counterfactuals, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]
العلاقة: hal-03923960; https://hal.science/hal-03923960Test; https://hal.science/hal-03923960/documentTest; https://hal.science/hal-03923960/file/10.1515_ijb-2019-0088.pdfTest
الإتاحة: https://doi.org/10.1515/ijb-2019-0088Test
https://hal.science/hal-03923960Test
https://hal.science/hal-03923960/documentTest -
3دورية أكاديمية
المؤلفون: Song, Yanyi, Zhou, Xiang, Kang, Jian, Aung, Max T., Zhang, Min, Zhao, Wei, Needham, Belinda L., Kardia, Sharon L. R., Liu, Yongmei, Meeker, John D., Smith, Jennifer A., Mukherjee, Bhramar
مصطلحات موضوعية: environmental exposure, Gaussian mixture model, Potts model, Bayesian hierarchical mediation analysis, correlated mediators, epigenetics, Medicine (General), Statistics and Numeric Data, Public Health, Science, Social Sciences, Health Sciences
وصف الملف: application/pdf
العلاقة: Song, Yanyi; Zhou, Xiang; Kang, Jian; Aung, Max T.; Zhang, Min; Zhao, Wei; Needham, Belinda L.; Kardia, Sharon L. R.; Liu, Yongmei; Meeker, John D.; Smith, Jennifer A.; Mukherjee, Bhramar (2021). "Bayesian hierarchical models for high‐dimensional mediation analysis with coordinated selection of correlated mediators." Statistics in Medicine 40(27): 6038-6056.; https://hdl.handle.net/2027.42/170955Test; Statistics in Medicine; Ferguson KK, Chin HB. Environmental chemicals and preterm birth: biological mechanisms and the state of the science. Current Epidemiol Rep. 2017; 4 ( 1 ): 56 ‐ 71.; Ročková V, George EI. The spike‐and‐slab lasso. J Am Stat Assoc. 2018; 113 ( 521 ): 431 ‐ 444.; Feng D, Tierney L, Magnotta V. MRI tissue classification using high‐resolution Bayesian hidden Markov normal mixture models. J Am Stat Assoc. 2012; 107 ( 497 ): 102 ‐ 119.; Li Q, Wang X, Liang F, et al. A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images. 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Vol 8; Hoboken, New Jersey: John Wiley & Sons, Inc; 1988.; Efron B. Size, power and false discovery rates. Ann Stat. 2007; 35 ( 4 ): 1351 ‐ 1377.; Alegbeleye OO, Opeolu BO, Jackson VA. Polycyclic aromatic hydrocarbons: a critical review of environmental occurrence and bioremediation. Environ Manag. 2017; 60 ( 4 ): 758 ‐ 783.; Padula AM, Noth EM, Hammond SK, et al. Exposure to airborne polycyclic aromatic hydrocarbons during pregnancy and risk of preterm birth. Environ Res. 2014; 135: 221 ‐ 226.; Sadler NC, Nandhikonda P, Webb‐Robertson B‐J, et al. Hepatic cytochrome P450 activity, abundance, and expression throughout human development. Drug Metab Dispos. 2016; 44 ( 7 ): 984 ‐ 991.; Banerjee BD, Mustafa MD, Sharma T, et al. Assessment of toxicogenomic risk factors in etiology of preterm delivery. Reprod Syst Sex Disord. 2014; 3 ( 2 ): 1 ‐ 10.; Aung MT, Yu Y, Ferguson KK, et al. Prediction and associations of preterm birth and its subtypes with eicosanoid enzymatic pathways and inflammatory markers. Sci Rep. 2019; 9 ( 1 ): 1 ‐ 17.; Neelon BH, O’Malley AJ, Normand SLT. A Bayesian model for repeated measures zero‐inflated count data with application to outpatient psychiatric service use. Stat Model. 2010; 10 ( 4 ): 421 ‐ 439.; Needham BL, Smith JA, Zhao W, et al. Life course socioeconomic status and DNA methylation in genes related to stress reactivity and inflammation: the multi‐ethnic study of atherosclerosis. Epigenetics. 2015; 10 ( 10 ): 958 ‐ 969.; Smith JA, Zhao W, Wang X, et al. Neighborhood characteristics influence DNA methylation of genes involved in stress response and inflammation: the multi‐ethnic study of atherosclerosis. Epigenetics. 2017; 12 ( 8 ): 662 ‐ 673.; Ross CE, Mirowsky J. Neighborhood disorder, subjective alienation, and distress. J Health Soc Behav. 2009; 50 ( 1 ): 49 ‐ 64.; Kaplan GA, Keil JE. 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A common MYBPC3 (cardiac myosin binding protein C) variant associated with cardiomyopathies in South Asia. Nature Genet. 2009; 41 ( 2 ): 187 ‐ 191.; Tran DH, Wang ZV. Glucose metabolism in cardiac hypertrophy and heart failure. J Amer Heart Assoc. 2019; 8 ( 12 ): e012673.; Xue A, Wu Y, Zhu Z, et al. Genome‐wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nature Commun. 2018; 9 ( 1 ): 1 ‐ 14.; Djordjilović V, Page CM, Gran JM, et al. Global test for high‐dimensional mediation: testing groups of potential mediators. Stat Med. 2019; 38 ( 18 ): 3346 ‐ 3360.; Bobb JF, Valeri L, Claus HB, et al. Bayesian kernel machine regression for estimating the health effects of multi‐pollutant mixtures. Biostatistics. 2015; 16 ( 3 ): 493 ‐ 508.; MacKinnon DP. Introduction to Statistical Mediation Analysis. London, UK: Routledge; 2008.; Imai K, Keele L, Tingley D. A general approach to causal mediation analysis. Psychol Methods. 2010; 15 ( 4 ): 309.; Pearl J. The causal mediation formula: a guide to the assessment of pathways and mechanisms. Prev Sci. 2012; 13 ( 4 ): 426 ‐ 436.; Valeri L, VanderWeele TJ. Mediation analysis allowing for exposure–mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychol Methods. 2013; 18 ( 2 ): 137.; VanderWeele TJ. Causal mediation analysis with survival data. Epidemiology. 2011; 22 ( 4 ): 582.; McElrath TF, Lim K‐H, Pare E, et al. Longitudinal evaluation of predictive value for preeclampsia of circulating angiogenic factors through pregnancy. Am J Obstet Gynecol. 2012; 207 ( 5 ): 407 ‐ e1.; Aung MT, Song Y, Ferguson KK, et al. Application of a novel analytical pipeline for high‐dimensional multivariate mediation analysis of environmental data. medRxiv. 2020. https://doi.org/10.1101/2020.05.30.20117655Test.; Bild DE, Bluemke DA, Burke GL, et al. 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Bayesian variable selection in linear regression. J Am Stat Assoc. 1988; 83 ( 404 ): 1023 ‐ 1032.; Li F, Zhang NR. Bayesian variable selection in structured high‐dimensional covariate spaces with applications in genomics. J Am Stat Assoc. 2010; 105 ( 491 ): 1202 ‐ 1214.; Chekouo T, Stingo FC, Guindani M, Do K‐A. A Bayesian predictive model for imaging genetics with application to schizophrenia. Ann Appl Stat. 2016; 10 ( 3 ): 1547 ‐ 1571.; Li F, Zhang T, Wang Q, et al. Spatial Bayesian variable selection and grouping for high‐dimensional scalar‐on‐image regression. Ann Appl Stat. 2015; 9 ( 2 ): 687 ‐ 713.; Zhang L, Baladandayuthapani V, Mallick BK, et al. Bayesian hierarchical structured variable selection methods with application to molecular inversion probe studies in breast cancer. J Royal Stat Soc Ser C (Appl Stat). 2014; 63 ( 4 ): 595 ‐ 620.; VanderWeele TJ. Mediation analysis: a practitioner’s guide. Annu Rev Public Health. 2016; 37: 17 ‐ 32.; Huang Y‐T. 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الإتاحة: https://doi.org/10.1002/sim.9168Test
https://doi.org/10.1101/2020.05.30.20117655Test
https://hdl.handle.net/2027.42/170955Test -
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المساهمون: Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS)
مصطلحات موضوعية: FOS: Computer and information sciences, multiple mediators, Models, Statistical, Mediation Analysis, direct and indirect effects, simulation of counterfactuals, Bayes Theorem, Statistical, Methodology (stat.ME), Causality, Cohort Studies, Models, correlated mediators, independent mediators, Humans, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Statistics - Methodology
الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ef13fb27d338ffa571381948bcea98ffTest
https://hal.archives-ouvertes.fr/hal-01879552Test
