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 Text 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.]

• 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 Text 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.]
• BAG: Jin B, Peruzzi M, Dunson DB. Bag of DAGs: Flexible nonstationary modeling of spatiotemporal dependence. arXiv 2021, arXiv:2112.11870. [Full Text Jin 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 Text Mork D, Wilson A. Estimating perinatal critical windows of susceptibility to environmental mixtures via structured Bayesian regression tree pairs. arXiv 2021, arXiv:2102.09071.] [Software Mork 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 Text Feldman J, Kowal D. Bayesian data synthesis and the utility-risk trade-off for mixed epidemiological data. arXiv 2021, arXiv:2102.08255.] [Software Feldman 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 Text 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.] [Software 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.]
• 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 Text 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.] [Software 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.]
• 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 Text Jin B, Dunson DB, Rager JE, Reif D, Engel SM, Herring AH. Bayesian matrix completion for hypothesis testing. arXiv 2020, arXiv:2009.08405.] [Software Jin 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 Text McGee 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 Text 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.] [Software 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.]
• BS3FA: Moran KR, Dunson D, Wheeler MW, Herring AH. 2020. Bayesian joint modeling of chemical structure and dose response curves. [Full Text Moran 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 Text 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] [Software 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]
• 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 Text 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.]
• 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 Text Weisskopf 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 Text Wilson A and Mork D. 2020. Treed distributed lag nonlinear models. Revision invited for Biostatistics.] [Software Wilson 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 Text Ferrari 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 Text Kowal DR. Fast, optimal, and targeted predictions using parameterized decision analysis. J Am Stat Assoc. 2021:1-12. doi:10.1080/01621459.2021.1891926] [Software Kowal 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 Text Schiavon L, Canale A, Dunson DB. Generalized infinite factorization models. arXiv 2021, arXiv:2103.10333.] [Software Schiavon 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 Text Dunson DB, Wu HT, Wu N. Graph based gaussian processes on restricted domains. arXiv 2020, arXiv:2010.07242.] [Software Dunson 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 Text 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.] [Software 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.]
• 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 Text 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.] [Software 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.]
• 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. [Abstract 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.] [Full Text 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.]
• MatchAlign: Poworoznek E, Ferrari F, Dunson D. Efficiently resolving rotational ambiguity in Bayesian matrix sampling with matching. arXiv 2021, arXiv:2107.13783. [Full Text Poworoznek E, Ferrari F, Dunson D. Efficiently resolving rotational ambiguity in Bayesian matrix sampling with matching. arXiv 2021, arXiv:2107.13783.] [Software Poworoznek 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 Text Ferrari 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 Text Dunson DB, Wu N. Inferring manifolds from noisy data using gaussian processes. arXiv 2021, arXiv:2110.07478.] [Software Dunson 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 Text Antonelli J, Wilson A, Coull B. Multiple exposure distributed lag models with variable selection. arXiv 2021, arXiv:2107.14567.] [Software Antonelli J, Wilson A, Coull B. Multiple exposure distributed lag models with variable selection. arXiv 2021, arXiv:2107.14567.]
• MVNimpute: Li H. Mvnimpute. [Software Li 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 Text 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.] [Software 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.]
• 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 Text 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.] [Software 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.] [Software Helper 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.]
• PFA: Roy A, Lavine I, Herring A, Dunson D. 2020. Perturbed factor analysis: Accounting for group differences in exposure profiles. [Full Text Roy 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 Text Tanner 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 Text Schedler JC, Ensor KB. A spatiotemporal case-crossover model of asthma exacerbation in the city of Houston. Stat 2021, 10, e357.] [Software Schedler 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 Text 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.]
• 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 Text 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.] [Software 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.]
• 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.] [Software Peruzzi 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 Text Chen HY. Statistical inference on explained variation in high-dimensional linear model with dense effects. arXiv 2022, arXiv:2201.08723.] [Software Chen 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 Text Chen HY. 2021. Semiparametric odds ratio model and its applications. Boca Raton, FL: Chapman and Hall/CRC.] [Software Chen 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. [Software 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.]

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 Text 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.]
• Bravo MA, Miranda ML. 2021. Effects of accumulated environmental, social and host exposures on early childhood educational outcomes. Environmental Research 198:111241. [Full Text Bravo 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 Text 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.]
• 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 Text 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.]
• Webster TF, Weisskopf MG. 2020. Epidemiology of exposure to mixtures: we can't be casual about causality when using or testing methods. [Full Text Webster 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 Text 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.]

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.

2022

• Gennings C, Svensson K, Wolk A, Lindh C, Kiviranta H, Bornehag CG. 2022. Using Metrics of a Mixture Effect and Nutrition from an Observational Study for Consideration towards Causal Inference. International journal of environmental research and public health; 19(4), 2273. [Full Text Gennings C, Svensson K, Wolk A, Lindh C, Kiviranta H, Bornehag CG. 2022. Using Metrics of a Mixture Effect and Nutrition from an Observational Study for Consideration towards Causal Inference. International journal of environmental research and public health; 19(4), 2273.]

2020

• Howe CG, Claus Henn B, Eckel SP, Farzan SF, Grubbs BH, Chavez TA, Hodes TL, Faham D, Al-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 Text Howe CG, Claus Henn B, Eckel SP, Farzan SF, Grubbs BH, Chavez TA, Hodes TL, Faham D, Al-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.]