Publications

New or Expanded Methods

The following publications represent novel statistical methods or expansions of existing methods.

Methods were recently reviewed in Joubert BR, Kioumourtzoglou MA, Chamberlain T, Chen HY, Gennings C, Turyk ME, Miranda ML, Webster TF, Ensor KB, Dunson DB, Coull BA. 2022. Powering research through innovative methods for mixtures in epidemiology (PRIME) program: novel and expanded statistical methods. Int J Environ Res Public Health 19(3):1378. [ Full TextJoubert BR, Kioumourtzoglou MA, Chamberlain T, Chen HY, Gennings C, Turyk ME, Miranda ML, Webster TF, Ensor KB, Dunson DB, Coull BA. 2022. Powering research through innovative methods for mixtures in epidemiology (PRIME) program: novel and expanded statistical methods. Int J Environ Res Public Health 19(3):1378. ]

• ACR: Gennings C, Shu H, Rudén C, Öberg M, Lindh C, Kiviranta H, Bornehag CG. 2018. Incorporating regulatory guideline values in analysis of epidemiology data.Environ Int. 120:535-543. [ Full TextGennings C, Shu H, Rudén C, Öberg M, Lindh C, Kiviranta H, Bornehag CG. 2018. Incorporating regulatory guideline values in analysis of epidemiology data. Environ Int. 120:535-543. ]
• BAG: Jin B, Peruzzi M, Dunson DB. Bag of DAGs: Flexible nonstationary modeling of spatiotemporal dependence. arXiv 2021, arXiv:2112.11870. [ Full TextJin B, Peruzzi M, Dunson DB. Bag of DAGs: Flexible nonstationary modeling of spatiotemporal dependence. arXiv 2021, arXiv:2112.11870. ]
• Bayes Tree Pairs: Mork D, Wilson A. Estimating perinatal critical windows of susceptibility to environmental mixtures via structured Bayesian regression tree pairs. arXiv 2021, arXiv:2102.09071. [ Full TextMork D, Wilson A. Estimating perinatal critical windows of susceptibility to environmental mixtures via structured Bayesian regression tree pairs. arXiv 2021, arXiv:2102.09071. ] [ SoftwareMork D, Wilson A. Estimating perinatal critical windows of susceptibility to environmental mixtures via structured Bayesian regression tree pairs. arXiv 2021, arXiv:2102.09071. ]
• BDS: Feldman J, Kowal D. Bayesian data synthesis and the utility-risk trade-off for mixed epidemiological data. arXiv 2021, arXiv:2102.08255. [ Full TextFeldman J, Kowal D. Bayesian data synthesis and the utility-risk trade-off for mixed epidemiological data. arXiv 2021, arXiv:2102.08255. ] [ SoftwareFeldman J, Kowal D. Bayesian data synthesis and the utility-risk trade-off for mixed epidemiological data. arXiv 2021, arXiv:2102.08255. ]
• BKMR-CMA: Devick KL, Bobb JF, Mazumdar M, Henn BC, Bellinger DC, Christiani DC, Wright RO, Williams PL, Coull BA, Valeria L. 2018. Bayesian kernel machine regression-causal mediation analysis. [ Full TextDevick KL, Bobb JF, Mazumdar M, Henn BC, Bellinger DC, Christiani DC, Wright RO, Williams PL, Coull BA, Valeria L. 2018. Bayesian kernel machine regression-causal mediation analysis. ] [ SoftwareDevick KL, Bobb JF, Mazumdar M, Henn BC, Bellinger DC, Christiani DC, Wright RO, Williams PL, Coull BA, Valeria L. 2018. Bayesian kernel machine regression-causal mediation analysis. ]
• BKMR-DLM: Wilson A Hsu HH, Chiu, Y-H, Wright RO, Wright RJ, Coull BA. 2020. Kernel machine and distributed lag models for assessing windows of susceptibility to environmental mixtures in children's health studies. Ann Appl Stat 2021, in press. [ Full TextWilson A Hsu HH, Chiu, Y-H, Wright RO, Wright RJ, Coull BA. 2020. Kernel machine and distributed lag models for assessing windows of susceptibility to environmental mixtures in children's health studies. Ann Appl Stat 2021, in press. ] [ SoftwareWilson A Hsu HH, Chiu, Y-H, Wright RO, Wright RJ, Coull BA. 2020. Kernel machine and distributed lag models for assessing windows of susceptibility to environmental mixtures in children's health studies. Ann Appl Stat 2021, in press. ]
• BMC: Jin B, Dunson DB, Rager JE, Reif D, Engel SM, Herring AH. Bayesian matrix completion for hypothesis testing. arXiv 2020, arXiv:2009.08405. [ Full TextJin B, Dunson DB, Rager JE, Reif D, Engel SM, Herring AH. Bayesian matrix completion for hypothesis testing. arXiv 2020, arXiv:2009.08405. ] [ SoftwareJin B, Dunson DB, Rager JE, Reif D, Engel SM, Herring AH. Bayesian matrix completion for hypothesis testing. arXiv 2020, arXiv:2009.08405. ]
• BMIM: McGee G, Wilson A. Webster TF. Coull BA. 2021 Bayesian multiple index models for environmental mixtures. arXiv 2021, arXiv:2101.05352. [ Full TextMcGee G, Wilson A. Webster TF. Coull BA. 2021 Bayesian multiple index models for environmental mixtures. arXiv 2021, arXiv:2101.05352. ]
• BN2MF: Gibson EA, Rowland ST, Goldsmith J, Paisley J, Herbstman JB, Kiourmourtzoglou MA. Bayesian non-parametric non-negative matrix factorization for pattern identification in environmental mixtures. arXiv 2021, arXiv:2109.12164. [ Full TextGibson EA, Rowland ST, Goldsmith J, Paisley J, Herbstman JB, Kiourmourtzoglou MA. Bayesian non-parametric non-negative matrix factorization for pattern identification in environmental mixtures. arXiv 2021, arXiv:2109.12164. ] [ SoftwareGibson EA, Rowland ST, Goldsmith J, Paisley J, Herbstman JB, Kiourmourtzoglou MA. Bayesian non-parametric non-negative matrix factorization for pattern identification in environmental mixtures. arXiv 2021, arXiv:2109.12164. ]
• BS3FA: Moran KR, Dunson D, Wheeler MW, Herring AH. 2020. Bayesian joint modeling of chemical structure and dose response curves. [ Full TextMoran KR, Dunson D, Wheeler MW, Herring AH. 2020. Bayesian joint modeling of chemical structure and dose response curves. ]
• BVSM: Kowal DR, Bravo M, Leong H, Bui A, Griffin RJ, Ensor KB, Miranda ML. Bayesian variable selection for understanding mixtures in environmental exposures. Stat Med. 2021;40(22):4850-4871. doi:10.1002/sim.9099 [ Full TextKowal DR, Bravo M, Leong H, Bui A, Griffin RJ, Ensor KB, Miranda ML. Bayesian variable selection for understanding mixtures in environmental exposures. Stat Med. 2021;40(22):4850-4871. doi:10.1002/sim.9099 ] [ SoftwareKowal DR, Bravo M, Leong H, Bui A, Griffin RJ, Ensor KB, Miranda ML. Bayesian variable selection for understanding mixtures in environmental exposures. Stat Med. 2021;40(22):4850-4871. doi:10.1002/sim.9099 ]
• CVEK: Liu JZ, Lee J, Lin P-I, Valeri L, Christiani DC, Bellinger DC, Wright RO, Mazumdar MM, Coull BA. 2021. A cross-validated ensemble approach to robust hypothesis testing of continuous nonlinear interactions: application to nutrition-environment studies. arXiv 2019, arXiv:1904.10918. [ Full TextLiu JZ, Lee J, Lin P-I, Valeri L, Christiani DC, Bellinger DC, Wright RO, Mazumdar MM, Coull BA. 2021. A cross-validated ensemble approach to robust hypothesis testing of continuous nonlinear interactions: application to nutrition-environment studies. arXiv 2019, arXiv:1904.10918. ]
• DAG Analysis: Weisskopf MG, Seals RM, Webster TF. 2018. Bias Amplification in Epidemiologic Analysis of Exposure to Mixtures.Environmental Health Perspectives. 126(4): 047003. [ Full TextWeisskopf MG, Seals RM, Webster TF. 2018. Bias Amplification in Epidemiologic Analysis of Exposure to Mixtures. Environmental Health Perspectives. 126(4): 047003. ]
• DLMtree: Wilson A and Mork D. 2020. Treed distributed lag nonlinear models. Revision invited for Biostatistics. [ Full TextWilson A and Mork D. 2020. Treed distributed lag nonlinear models. Revision invited for Biostatistics. ] [ SoftwareWilson A and Mork D. 2020. Treed distributed lag nonlinear models. Revision invited for Biostatistics. ]
• FIN: Ferrari F, Dunson DB. 2020. Bayesian Factor Analysis for Inference on Interactions. [ Full TextFerrari F, Dunson DB. 2020. Bayesian Factor Analysis for Inference on Interactions. ]
• FOTP: Kowal DR. Fast, optimal, and targeted predictions using parameterized decision analysis. J Am Stat Assoc. 2021:1-12. doi:10.1080/01621459.2021.1891926 [ Full TextKowal DR. Fast, optimal, and targeted predictions using parameterized decision analysis. J Am Stat Assoc. 2021:1-12. doi:10.1080/01621459.2021.1891926 ] [ SoftwareKowal DR. Fast, optimal, and targeted predictions using parameterized decision analysis. J Am Stat Assoc. 2021:1-12. doi:10.1080/01621459.2021.1891926 ]
• GIF-SIS: Schiavon L, Canale A, Dunson DB. Generalized infinite factorization models. arXiv 2021, arXiv:2103.10333. [ Full TextSchiavon L, Canale A, Dunson DB. Generalized infinite factorization models. arXiv 2021, arXiv:2103.10333. ] [ SoftwareSchiavon L, Canale A, Dunson DB. Generalized infinite factorization models. arXiv 2021, arXiv:2103.10333. ]
• GL-GPs: Dunson DB, Wu HT, Wu N. Graph based gaussian processes on restricted domains. arXiv 2020, arXiv:2010.07242. [ Full TextDunson DB, Wu HT, Wu N. Graph based gaussian processes on restricted domains. arXiv 2020, arXiv:2010.07242. ] [ SoftwareDunson DB, Wu HT, Wu N. Graph based gaussian processes on restricted domains. arXiv 2020, arXiv:2010.07242. ]
• GriPS: Peruzzi M, Banerjee S, Dunson DB, Finley AO. Grid-Parametrize-Split (GriPS) for improved scalable inference in spatial big data analysis. arXiv 2021, arXiv:2101.03579. [ Full TextPeruzzi M, Banerjee S, Dunson DB, Finley AO. Grid-Parametrize-Split (GriPS) for improved scalable inference in spatial big data analysis. arXiv 2021, arXiv:2101.03579. ] [ SoftwarePeruzzi M, Banerjee S, Dunson DB, Finley AO. Grid-Parametrize-Split (GriPS) for improved scalable inference in spatial big data analysis. arXiv 2021, arXiv:2101.03579. ]
• Het-DLM: Mork D, Kioumourtzoglou M A, Weisskopf M, Coull BA, Wilson A. Heterogeneous distributed lag models to estimate personalized effects of maternal exposures to air pollution. arXiv 2021, arXiv:2109.13763. [ Full TextMork D, Kioumourtzoglou M A, Weisskopf M, Coull BA, Wilson A. Heterogeneous distributed lag models to estimate personalized effects of maternal exposures to air pollution. arXiv 2021, arXiv:2109.13763. ] [ SoftwareMork D, Kioumourtzoglou M A, Weisskopf M, Coull BA, Wilson A. Heterogeneous distributed lag models to estimate personalized effects of maternal exposures to air pollution. arXiv 2021, arXiv:2109.13763. ]
• LWQS: Gennings C, Curtin P, Bello G, Wright R, Arora M, Austin C. 2020. Lagged WQS regression for mixtures with many components. Environ Res. Jul;186:109529. [ AbstractGennings C, Curtin P, Bello G, Wright R, Arora M, Austin C. 2020. Lagged WQS regression for mixtures with many components. Environ Res. Jul;186:109529. ] [ Full TextGennings C, Curtin P, Bello G, Wright R, Arora M, Austin C. 2020. Lagged WQS regression for mixtures with many components. Environ Res. Jul;186:109529. ]
• MatchAlign: Poworoznek E, Ferrari F, Dunson D. Efficiently resolving rotational ambiguity in Bayesian matrix sampling with matching. arXiv 2021, arXiv:2107.13783. [ Full TextPoworoznek E, Ferrari F, Dunson D. Efficiently resolving rotational ambiguity in Bayesian matrix sampling with matching. arXiv 2021, arXiv:2107.13783. ] [ SoftwarePoworoznek E, Ferrari F, Dunson D. Efficiently resolving rotational ambiguity in Bayesian matrix sampling with matching. arXiv 2021, arXiv:2107.13783. ]
• Mix Select: Ferrari F, Dunson DB. 2020. Identifying main effects and interactions among exposures using Gaussian processes. [ Full TextFerrari F, Dunson DB. 2020. Identifying main effects and interactions among exposures using Gaussian processes. ]
• MrGap: Dunson DB, Wu N. Inferring manifolds from noisy data using gaussian processes. arXiv 2021, arXiv:2110.07478. [ Full TextDunson DB, Wu N. Inferring manifolds from noisy data using gaussian processes. arXiv 2021, arXiv:2110.07478. ] [ SoftwareDunson DB, Wu N. Inferring manifolds from noisy data using gaussian processes. arXiv 2021, arXiv:2110.07478. ]
• Mult DLAG: Antonelli J, Wilson A, Coull B. Multiple exposure distributed lag models with variable selection. arXiv 2021, arXiv:2107.14567. [ Full TextAntonelli J, Wilson A, Coull B. Multiple exposure distributed lag models with variable selection. arXiv 2021, arXiv:2107.14567. ] [ SoftwareAntonelli J, Wilson A, Coull B. Multiple exposure distributed lag models with variable selection. arXiv 2021, arXiv:2107.14567. ]
• MVNimpute: Li H. Mvnimpute. [ SoftwareLi H. Mvnimpute. ]
• NLinteraction: Antonelli J, Mazumdar M, Bellinger DC, Christiani DC, Wright RO, Coull BA. 2020. Estimating the health effects of environmental mixtures using Bayesian semiparametric and sparsity inducing priors.Annals of Applied Statistics. 14(1): 257-275. [ Full TextAntonelli J, Mazumdar M, Bellinger DC, Christiani DC, Wright RO, Coull BA. 2020. Estimating the health effects of environmental mixtures using Bayesian semiparametric and sparsity inducing priors. Annals of Applied Statistics. 14(1): 257-275. ] [ SoftwareAntonelli J, Mazumdar M, Bellinger DC, Christiani DC, Wright RO, Coull BA. 2020. Estimating the health effects of environmental mixtures using Bayesian semiparametric and sparsity inducing priors. Annals of Applied Statistics. 14(1): 257-275. ]
• PCP: Gibson EA, Zhang J, Yan J, Chillrud L, Benavides J, Nunez Y, Herbstman JB, Goldsmith J, Wright J, Kioumourtzoglou MA. Principal component pursuit for pattern identification in environmental mixtures. arXiv 2021, arXiv:2111.00104. [ Full TextGibson EA, Zhang J, Yan J, Chillrud L, Benavides J, Nunez Y, Herbstman JB, Goldsmith J, Wright J, Kioumourtzoglou MA. Principal component pursuit for pattern identification in environmental mixtures. arXiv 2021, arXiv:2111.00104. ] [ SoftwareGibson EA, Zhang J, Yan J, Chillrud L, Benavides J, Nunez Y, Herbstman JB, Goldsmith J, Wright J, Kioumourtzoglou MA. Principal component pursuit for pattern identification in environmental mixtures. arXiv 2021, arXiv:2111.00104. ]
• PFA: Roy A, Lavine I, Herring A, Dunson D. 2020. Perturbed factor analysis: Accounting for group differences in exposure profiles. [ Full TextRoy A, Lavine I, Herring A, Dunson D. 2020. Perturbed factor analysis: Accounting for group differences in exposure profiles. ]
• RH-WQS: Tanner EM, Bornehag CG, Gennings C. 2019. Repeated holdout validation for weighted quantile sum regression.MethodsX. 6:2855-2860. [ Full TextTanner EM, Bornehag CG, Gennings C. 2019. Repeated holdout validation for weighted quantile sum regression. MethodsX. 6:2855-2860. ]
• SCC: Schedler JC, Ensor KB. A spatiotemporal case-crossover model of asthma exacerbation in the city of Houston. Stat 2021, 10, e357. [ Full TextSchedler JC, Ensor KB. A spatiotemporal case-crossover model of asthma exacerbation in the city of Houston. Stat 2021, 10, e357. ] [ SoftwareSchedler JC, Ensor KB. A spatiotemporal case-crossover model of asthma exacerbation in the city of Houston. Stat 2021, 10, e357. ]
• SGP-MPI: Sonabend A, Zhang J, Schwartz J, Coull BA, Lu J. Scalable gaussian process regression via median posterior inference for estimating multi-pollutant mixture health effects. 2020. [ Full TextSonabend A, Zhang J, Schwartz J, Coull BA, Lu J. Scalable gaussian process regression via median posterior inference for estimating multi-pollutant mixture health effects. 2020. ]
• SiBAR: Actkinson B, Ensor K, Griffin RJ. SIBaR: A new method for background quantification and removal from mobile air pollution measurements. Atmos Meas Tech. 2021, 14, 5809–5821. [ Full TextActkinson B, Ensor K, Griffin RJ. SIBaR: A new method for background quantification and removal from mobile air pollution measurements. Atmos Meas Tech. 2021, 14, 5809–5821. ] [ SoftwareActkinson B, Ensor K, Griffin RJ. SIBaR: A new method for background quantification and removal from mobile air pollution measurements. Atmos Meas Tech. 2021, 14, 5809–5821. ]
• SPAMTREE: Peruzzi M, Dunson DB. Spatial multivariate trees for big data Bayesian regression. arXiv 2020, arXiv:2012.00943. [ Full Text Peruzzi M, Dunson DB. Spatial multivariate trees for big data Bayesian regression. arXiv 2020, arXiv:2012.00943. ] [ SoftwarePeruzzi M, Dunson DB. Spatial multivariate trees for big data Bayesian regression. arXiv 2020, arXiv:2012.00943. ]
• SPORM: Chen HY. Statistical inference on explained variation in high-dimensional linear model with dense effects. arXiv 2022, arXiv:2201.08723. [ Full TextChen HY. Statistical inference on explained variation in high-dimensional linear model with dense effects. arXiv 2022, arXiv:2201.08723. ] [ SoftwareChen HY. Statistical inference on explained variation in high-dimensional linear model with dense effects. arXiv 2022, arXiv:2201.08723. ]
• TEV: Chen HY. 2021. Semiparametric odds ratio model and its applications. Boca Raton, FL: Chapman and Hall/CRC. [ Full TextChen HY. 2021. Semiparametric odds ratio model and its applications. Boca Raton, FL: Chapman and Hall/CRC. ] [ SoftwareChen HY. 2021. Semiparametric odds ratio model and its applications. Boca Raton, FL: Chapman and Hall/CRC. ]
• TEV: Chen HY, Li H, Argos M, Persky V, Turyk M. Statistical methods for assessing explained variations of a health outcome by mixtures of exposures. Prep Spec Issue Int J Environ Res Public Health. 2022. [ SoftwareChen HY, Li H, Argos M, Persky V, Turyk M. Statistical methods for assessing explained variations of a health outcome by mixtures of exposures. Prep Spec Issue Int J Environ Res Public Health. 2022. ]

Other Highlighted Publications From PRIME Projects

All publications linked to PRIME funding support can be found in the NIH RePORTER search results . Some highlighted applications using new methods are noted below.

• Bravo MA, Miranda ML. 2022. A longitudinal study of exposure to fine particulate matter during pregnancy, small-for-gestational age births, and birthweight percentile for gestational age in a statewide birth cohort. Environmental Health 21(1):1-11. [ Full TextBravo MA, Miranda ML. 2022. A longitudinal study of exposure to fine particulate matter during pregnancy, small-for-gestational age births, and birthweight percentile for gestational age in a statewide birth cohort. Environmental Health 21(1):1-11. ]
• Bravo MA, Miranda ML. 2021. Effects of accumulated environmental, social and host exposures on early childhood educational outcomes. Environmental Research 198:111241. [ Full TextBravo MA, Miranda ML. 2021. Effects of accumulated environmental, social and host exposures on early childhood educational outcomes. Environmental Research 198:111241. ]
• Bravo MA, Leong MC, Gelfand AE, Miranda ML. 2021. Assessing disparity using measures of racial and educational isolation. Int J Environ Res Public Health 18(17):9384. [ Full TextBravo MA, Leong MC, Gelfand AE, Miranda ML. 2021. Assessing disparity using measures of racial and educational isolation. Int J Environ Res Public Health 18(17):9384. ]
• Marayata L, Lerner D, Quimby A, Twogood S, Richard MJ, Meeker JD, Bastain TM, Breton C. 2020. Prenatal metal mixtures and birth weight for gestational age in a predominately lower-income hispanic pregnancy cohort in Los Angeles. Environmental Health Perspectives 128(11):117001. [ Full TextMarayata L, Lerner D, Quimby A, Twogood S, Richard MJ, Meeker JD, Bastain TM, Breton C. 2020. Prenatal metal mixtures and birth weight for gestational age in a predominately lower-income hispanic pregnancy cohort in Los Angeles. Environmental Health Perspectives 128(11):117001. ]
• Webster TF, Weisskopf MG. 2020. Epidemiology of exposure to mixtures: we can't be casual about causality when using or testing methods. [ Full TextWebster TF, Weisskopf MG. 2020. Epidemiology of exposure to mixtures: we can't be casual about causality when using or testing methods. ]
• Levin-Schwartz Y, Gennings C, Schnaas L, Del Carmen Hernandez Chavez M, Bellinger DC, Téllez-Rojo MM, Baccarelli AA, Wright RO. 2019. Time-varying associations between prenatal metal mixtures and rapid visual processing in children.Environ Health. 18(1):92. [ Full TextLevin-Schwartz Y, Gennings C, Schnaas L, Del Carmen Hernandez Chavez M, Bellinger DC, Téllez-Rojo MM, Baccarelli AA, Wright RO. 2019. Time-varying associations between prenatal metal mixtures and rapid visual processing in children. Environ Health. 18(1):92. ]

Epidemiology Applications

The following publication(s) represent notable applications of PRIME methods in epidemiology studies. Other applications using existing methods for mixtures can be found in the RePORTER search results for all PRIME-funded publications.