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\n  \n 2025\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n \n Estimation and Hypothesis Testing of Strain-Specific Vaccine Efficacy With Missing Strain Types With Application to a COVID-19 Vaccine Trial.\n \n \n \n \n\n\n \n Heng, F.; Sun, Y.; Li, L.; and B. Gilbert, P.\n\n\n \n\n\n\n Statistics in Medicine, 44(6): e10345. 2025.\n \n\n\n\n
\n\n\n\n \n \n \"EstimationPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{Heng2025,\nauthor = {Heng, Fei and Sun, Yanqing and Li, Li and B. Gilbert, Peter},\ntitle = {Estimation and Hypothesis Testing of Strain-Specific Vaccine Efficacy With Missing Strain Types With Application to a COVID-19 Vaccine Trial},\njournal = {Statistics in Medicine},\nvolume = {44},\nnumber = {6},\npages = {e10345},\nkeywords = {augmented inverse probability weighted complete-case estimation, competing risks model, COVID-19 vaccine efficacy trial, missing failure cause, stratified Cox proportional hazards model},\ndoi = {https://doi.org/10.1002/sim.10345},\nurl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.10345},\neprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.10345},\nabstract = {ABSTRACT Based on data from a randomized, controlled vaccine efficacy trial, this article develops statistical methods for assessing vaccine efficacy (VE) to prevent COVID-19 infections by a discrete set of genetic strains of SARS-CoV-2. Strain-specific VE adjusting for possibly time-varying covariates is estimated using augmented inverse probability weighting to address missing viral genotypes under a competing risks model that allows separate baseline hazards for different risk groups. Hypothesis tests are developed to assess whether the vaccine provides at least a specified level of VE against some viral genotypes and whether VE varies across genotypes. Asymptotic properties providing analytic inferences are derived and finite-sample properties of the estimators and hypothesis tests are studied through simulations. This research is motivated by the fact that previous analyses of COVID-19 vaccine efficacy did not account for missing genotypes, which can cause severe bias and efficiency loss. The theoretical properties and simulations demonstrate superior performance of the new methods. Application to the Moderna COVE trial identifies several SARS-CoV-2 genotype features with differential vaccine efficacy across genotypes, including lineage (Reference, Epsilon, Gamma, Zeta), indicators of residue match vs. mismatch to the vaccine-strain residue at Spike amino acid positions (identifying signatures of differential VE), and a weighted Hamming distance to the vaccine strain. The results show VE decreases against genotypes more distant from the vaccine strain, highlighting the need to update COVID-19 vaccine strains.},\nyear = {2025}\n}\n\n
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\n ABSTRACT Based on data from a randomized, controlled vaccine efficacy trial, this article develops statistical methods for assessing vaccine efficacy (VE) to prevent COVID-19 infections by a discrete set of genetic strains of SARS-CoV-2. Strain-specific VE adjusting for possibly time-varying covariates is estimated using augmented inverse probability weighting to address missing viral genotypes under a competing risks model that allows separate baseline hazards for different risk groups. Hypothesis tests are developed to assess whether the vaccine provides at least a specified level of VE against some viral genotypes and whether VE varies across genotypes. Asymptotic properties providing analytic inferences are derived and finite-sample properties of the estimators and hypothesis tests are studied through simulations. This research is motivated by the fact that previous analyses of COVID-19 vaccine efficacy did not account for missing genotypes, which can cause severe bias and efficiency loss. The theoretical properties and simulations demonstrate superior performance of the new methods. Application to the Moderna COVE trial identifies several SARS-CoV-2 genotype features with differential vaccine efficacy across genotypes, including lineage (Reference, Epsilon, Gamma, Zeta), indicators of residue match vs. mismatch to the vaccine-strain residue at Spike amino acid positions (identifying signatures of differential VE), and a weighted Hamming distance to the vaccine strain. The results show VE decreases against genotypes more distant from the vaccine strain, highlighting the need to update COVID-19 vaccine strains.\n
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\n \n\n \n \n \n \n \n Immune correlates analysis of antibody responses against SARS-CoV-2 variants in the ENSEMBLE vaccine efficacy trial.\n \n \n \n\n\n \n Luedtke, A.; Fong, Y.; van der Laan, L.; Heng, F.; Huang, Y.; Lu, Y.; Yu, C.; Carpp, L. N.; Roels, S.; Le Gars, M.; Van Roey, G. A.; Stieh, D. J.; Van Dromme, I.; Kenny, A.; Carone, M.; Hyrien, O.; Ayala, V.; Jayashankar, L.; Castellino, F.; Amoa-Awua, O.; Basappa, M.; Flach, B.; Lin, B. C.; Moore, C.; Naisan, M.; Naqvi, M.; Narpala, S.; O'Connell, S.; Mueller, A.; Serebryannyy, L.; Castro, M.; Wang, J.; Dziubla, G.; Randhawa, A. K.; Andrasik, M. P.; Hendriks, J.; Truyers, C.; Struyf, F.; Schuitemaker, H.; Douoguih, M.; Kublin, J. G.; Corey, L.; Neuzil, K. M.; Bekker, L.; Garrett, N.; Cardoso, S. W.; DelaFontaine, P.; Magaret, C. A.; Vingerhoets, J.; Casapia, M.; Losso, M. H.; Little, S. J.; Gaur, A.; Swann, E.; Petropoulos, C. J.; McDermott, A. B.; Sadoff, J.; Gray, G. E.; Grinsztejn, B.; Goepfert, P. A.; Follmann, D.; Roychoudhury, P.; Greninger, A. L.; Koup, R. A.; Donis, R. O.; and Gilbert, P. B.\n\n\n \n\n\n\n iScience, 28(11): 113660. 2025.\n \n\n\n\n
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@article{Luedtke2025ENSEMBLE,\n  author = {Luedtke, Alex and \n            Fong, Youyi and \n            van der Laan, Lars and \n            Heng, Fei and \n            Huang, Ying and \n            Lu, Yiwen and \n            Yu, Chenchen and \n            Carpp, Lindsay N. and \n            Roels, Sanne and \n            Le Gars, Mathieu and \n            Van Roey, Griet A. and \n            Stieh, Daniel J. and \n            Van Dromme, Ilse and \n            Kenny, Avi and \n            Carone, Marco and \n            Hyrien, Ollivier and \n            Ayala, Victor and \n            Jayashankar, Lakshmi and \n            Castellino, Flora and \n            Amoa-Awua, Obrimpong and \n            Basappa, Manjula and \n            Flach, Britta and \n            Lin, Bob C. and \n            Moore, Christopher and \n            Naisan, Mursal and \n            Naqvi, Muhammed and \n            Narpala, Sandeep and \n            O'Connell, Sarah and \n            Mueller, Allen and \n            Serebryannyy, Leo and \n            Castro, Mike and \n            Wang, Jennifer and \n            Dziubla, Gabrielle and \n            Randhawa, April K. and \n            Andrasik, Michele P. and \n            Hendriks, Jenny and \n            Truyers, Carla and \n            Struyf, Frank and \n            Schuitemaker, Hanneke and \n            Douoguih, Macaya and \n            Kublin, James G. and \n            Corey, Lawrence and \n            Neuzil, Kathleen M. and \n            Bekker, Linda-Gail and \n            Garrett, Nigel and \n            Cardoso, Sandra W. and \n            DelaFontaine, Patrice and \n            Magaret, Craig A. and \n            Vingerhoets, Johan and \n            Casapia, Martin and \n            Losso, Marcelo H. and \n            Little, Susan J. and \n            Gaur, Aditya and \n            Swann, Edith and \n            Petropoulos, Christos J. and \n            McDermott, Adrian B. and \n            Sadoff, Jerald and \n            Gray, Glenda E. and \n            Grinsztejn, Beatriz and \n            Goepfert, Paul A. and \n            Follmann, Dean and \n            Roychoudhury, Pavitra and \n            Greninger, Alexander L. and \n            Koup, Richard A. and \n            Donis, Ruben O. and \n            Gilbert, Peter B.},\n  title = {Immune correlates analysis of antibody responses against SARS-CoV-2 variants in the ENSEMBLE vaccine efficacy trial},\n  journal = {iScience},\n  year = {2025},\n  volume = {28},\n  number = {11},\n  pages = {113660},\n  doi = {10.1016/j.isci.2025.113660}\n}\n\n
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\n \n\n \n \n \n \n \n \n Generalized nonparametric temporal modeling of recurrent events with application to a malaria vaccine trial.\n \n \n \n \n\n\n \n Heng, F.; Sun, Y.; Xu, J.; and Gilbert, P. B\n\n\n \n\n\n\n Biometrics, 81(4): ujaf146. 11 2025.\n \n\n\n\n
\n\n\n\n \n \n \"GeneralizedPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{10.1093/biomtc/ujaf146,\n    author = {Heng, Fei and Sun, Yanqing and Xu, Jing and Gilbert, Peter B},\n    title = {Generalized nonparametric temporal modeling of recurrent events with application to a malaria vaccine trial},\n    journal = {Biometrics},\n    volume = {81},\n    number = {4},\n    pages = {ujaf146},\n    year = {2025},\n    month = {11},\n    abstract = {Motivated by a malaria vaccine efficacy trial, this paper investigates generalized nonparametric temporal models of intensity processes with multiple time scales. Through the choice of link functions, the proposed models encompass a wide range of models such as the multiplicative temporal intensity model and the additive temporal intensity model. A maximum likelihood estimation procedure is developed to estimate the effects of two time-scales via the local linear smoothing with double kernels. Computational algorithms are developed to facilitate applications of the proposed method. An adaptive algorithm is developed to overcome the challenges of overlapping covariates. A cross-validation bandwidth selection procedure based on the logarithm of likelihood criteria is discussed. The asymptotic properties of the proposed estimators are investigated. Our simulation study shows that the proposed methods have satisfactory finite sample performance for both the multiplicative temporal intensity model and additive temporal intensity model. The proposed methods are applied to analyze the MAL-094/MAL-095 malaria vaccine efficacy trial data to investigate how the new malaria infection risk changes over time and how a prior infection or vaccination changes the future infection risk. The proposed method provides new insight into the protective effects of the malaria vaccine against new malaria infections and how the vaccine efficacy is modified by the history of prior malaria infection over time.},\n    issn = {0006-341X},\n    doi = {10.1093/biomtc/ujaf146},\n    url = {https://doi.org/10.1093/biomtc/ujaf146},\n    eprint = {https://academic.oup.com/biometrics/article-pdf/81/4/ujaf146/65446078/ujaf146.pdf},\n}\n\n
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\n Motivated by a malaria vaccine efficacy trial, this paper investigates generalized nonparametric temporal models of intensity processes with multiple time scales. Through the choice of link functions, the proposed models encompass a wide range of models such as the multiplicative temporal intensity model and the additive temporal intensity model. A maximum likelihood estimation procedure is developed to estimate the effects of two time-scales via the local linear smoothing with double kernels. Computational algorithms are developed to facilitate applications of the proposed method. An adaptive algorithm is developed to overcome the challenges of overlapping covariates. A cross-validation bandwidth selection procedure based on the logarithm of likelihood criteria is discussed. The asymptotic properties of the proposed estimators are investigated. Our simulation study shows that the proposed methods have satisfactory finite sample performance for both the multiplicative temporal intensity model and additive temporal intensity model. The proposed methods are applied to analyze the MAL-094/MAL-095 malaria vaccine efficacy trial data to investigate how the new malaria infection risk changes over time and how a prior infection or vaccination changes the future infection risk. The proposed method provides new insight into the protective effects of the malaria vaccine against new malaria infections and how the vaccine efficacy is modified by the history of prior malaria infection over time.\n
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\n \n\n \n \n \n \n \n Role of Age and Competing Risk of Death in the Racial Disparity of Kidney Failure Incidence after Onset of Chronic Kidney Disease.\n \n \n \n\n\n \n Yan, G.; Nee, R.; Scialla, J. J; Greene, T.; Yu, W.; Heng, F.; Cheung, A. K; and Norris, K. C\n\n\n \n\n\n\n Journal of the American Society of Nephrology,10–1681. 2024.\n \n\n\n\n
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@article{yan2024jasn,\n  title={Role of Age and Competing Risk of Death in the Racial Disparity of Kidney Failure Incidence after Onset of Chronic Kidney Disease},\n  author={Yan, Guofen and Nee, Robert and Scialla, Julia J and Greene, Tom and Yu, Wei and Heng, Fei and Cheung, Alfred K and Norris, Keith C},\n  journal={Journal of the American Society of Nephrology},\n  pages={10--1681},\n  year={2024},\n  publisher={LWW}\n}\n\n
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\n \n\n \n \n \n \n \n \n Quantifying how single dose Ad26.COV2.S vaccine efficacy depends on Spike sequence features.\n \n \n \n \n\n\n \n Magaret, C. A.; Li, L.; deCamp , A. C.; Rolland, M.; Juraska, M.; Williamson, B. D.; Ludwig, J.; Molitor, C.; Benkeser, D.; Luedtke, A.; Simpkins, B.; Heng, F.; Sun, Y.; Carpp, L. N.; Bai, H.; Dearlove, B. L.; Giorgi, E. E.; Jongeneelen, M.; Brandenburg, B.; McCallum, M.; Bowen, J. E.; Veesler, D.; Sadoff, J.; Gray, G. E.; Roels, S.; Vandebosch, A.; Stieh, D. J.; Le Gars, M.; Vingerhoets, J.; Grinsztejn, B.; Goepfert, P. A.; de Sousa, L. P.; Silva, M. S. T.; Casapia, M.; Losso, M. H.; Little, S. J.; Gaur, A.; Bekker, L.; Garrett, N.; Truyers, C.; Van Dromme, I.; Swann, E.; Marovich, M. A.; Follmann, D.; Neuzil, K. M.; Corey, L.; Greninger, A. L.; Roychoudhury, P.; Hyrien, O.; and Gilbert, P. B.\n\n\n \n\n\n\n Nature Communications, 15(1): 2175. Mar 2024.\n \n\n\n\n
\n\n\n\n \n \n \"QuantifyingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{Magaret2024,\nauthor={Magaret, Craig A.\nand Li, Li\nand deCamp, Allan C.\nand Rolland, Morgane\nand Juraska, Michal\nand Williamson, Brian D.\nand Ludwig, James\nand Molitor, Cindy\nand Benkeser, David\nand Luedtke, Alex\nand Simpkins, Brian\nand Heng, Fei\nand Sun, Yanqing\nand Carpp, Lindsay N.\nand Bai, Hongjun\nand Dearlove, Bethany L.\nand Giorgi, Elena E.\nand Jongeneelen, Mandy\nand Brandenburg, Boerries\nand McCallum, Matthew\nand Bowen, John E.\nand Veesler, David\nand Sadoff, Jerald\nand Gray, Glenda E.\nand Roels, Sanne\nand Vandebosch, An\nand Stieh, Daniel J.\nand Le Gars, Mathieu\nand Vingerhoets, Johan\nand Grinsztejn, Beatriz\nand Goepfert, Paul A.\nand de Sousa, Leonardo Paiva\nand Silva, Mayara Secco Torres\nand Casapia, Martin\nand Losso, Marcelo H.\nand Little, Susan J.\nand Gaur, Aditya\nand Bekker, Linda-Gail\nand Garrett, Nigel\nand Truyers, Carla\nand Van Dromme, Ilse\nand Swann, Edith\nand Marovich, Mary A.\nand Follmann, Dean\nand Neuzil, Kathleen M.\nand Corey, Lawrence\nand Greninger, Alexander L.\nand Roychoudhury, Pavitra\nand Hyrien, Ollivier\nand Gilbert, Peter B.},\ntitle={Quantifying how single dose Ad26.COV2.S vaccine efficacy depends on Spike sequence features},\njournal={Nature Communications},\nyear={2024},\nmonth={Mar},\nday={11},\nvolume={15},\nnumber={1},\npages={2175},\nissn={2041-1723},\ndoi={10.1038/s41467-024-46536-w},\nurl={https://doi.org/10.1038/s41467-024-46536-w}\n}\n\n
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\n \n\n \n \n \n \n \n Deep learning model for the prediction of all-cause mortality among long term care people in China: a prospective cohort study.\n \n \n \n\n\n \n Tan, H.; Zeng, L.; Yang, S.; Hou, L.; Wu, J.; Cai, X.; Heng, F.; Gu, X.; Zhong, Y.; Dong, B.; and others\n\n\n \n\n\n\n Scientific Reports, 14(1): 14639. 2024.\n \n\n\n\n
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@article{tan2024deep,\n  title={Deep learning model for the prediction of all-cause mortality among long term care people in China: a prospective cohort study},\n  author={Tan, Huai-Cheng and Zeng, Li-Jun and Yang, Shu-Juan and Hou, Li-Sha and Wu, Jin-Hui and Cai, Xin-Hui and Heng, Fei and Gu, Xu-Yu and Zhong, Yue and Dong, Bi-Rong and others},\n  journal={Scientific Reports},\n  volume={14},\n  number={1},\n  pages={14639},\n  year={2024},\n  publisher={Nature Publishing Group UK London}\n}\n\n
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\n \n\n \n \n \n \n \n \n Homelessness and Risk of End-Stage Kidney Disease and Death in Veterans With Chronic Kidney Disease.\n \n \n \n \n\n\n \n Koyama, A. K.; Nee, R.; Yu, W.; Choudhury, D.; Heng, F.; Cheung, A. K.; Cho, M. E.; Norris, K. C.; and Yan, G.\n\n\n \n\n\n\n JAMA Network Open, 7(9): e2431973-e2431973. 09 2024.\n \n\n\n\n
\n\n\n\n \n \n \"HomelessnessPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{koyama2024,\n    author = {Koyama, Alain K. and Nee, Robert and Yu, Wei and Choudhury, Devasmita and Heng, Fei and Cheung, Alfred K. and Cho, Monique E. and Norris, Keith C. and Yan, Guofen},\n    title = {Homelessness and Risk of End-Stage Kidney Disease and Death in Veterans With Chronic Kidney Disease},\n    journal = {JAMA Network Open},\n    volume = {7},\n    number = {9},\n    pages = {e2431973-e2431973},\n    year = {2024},\n    month = {09},\n    abstract = {Adults experiencing homelessness in the US face numerous challenges, including the management of chronic kidney disease (CKD). The extent of a potentially greater risk of adverse health outcomes in the population with CKD experiencing homelessness has not been adequately explored.To evaluate the association between a history of homelessness and the risk of end-stage kidney disease (ESKD) and death among veterans with incident CKD.This retrospective cohort study was conducted between January 1, 2005, and December 31, 2017. Participants included veterans aged 18 years and older with incident stage 3 to 5 CKD utilizing the Veterans Health Administration health care network in the US. Patients were followed-up through December 31, 2018, for the occurrence of ESKD and death. Analyses were performed from September 2022 to October 2023.History of homelessness, based on utilization of homeless services in the Veterans Health Administration or International Classification of Diseases, Ninth Revision or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes. Homelessness was measured during the 2-year baseline period prior to the index date of incident CKD.The primary outcomes were ESKD, based on initiation of kidney replacement therapy, and all-cause death. Adjusted hazard ratios (HRs) were calculated to compare veterans with a history of homelessness with those without a history of homelessness.Among 836 361 veterans, the largest proportion were aged 65 to 74 years (274 371 veterans [32.8\\%]) or 75 to 84 years (270 890 veterans [32.4\\%]), and 809 584 (96.8\\%) were male. A total of 26 037 veterans (3.1\\%) developed ESKD, and 359 991 (43.0\\%) died. Compared with veterans who had not experienced homelessness, those with a history of homelessness showed a significantly greater risk of ESKD (adjusted HR, 1.15; 95\\% CI, 1.10-1.20). A greater risk of all-cause death was also observed (HR, 1.48; 95\\% CI, 1.46-1.50). After further adjustment for body mass index, comorbidities, and medication use, results were attenuated for all-cause death (HR, 1.09; 95\\% CI, 1.07-1.11) and were no longer significant for ESKD (HR, 1.04; 95\\% CI, 0.99-1.09).In this cohort study of veterans with incident stage 3 to 5 CKD, a history of homelessness was significantly associated with a greater risk of ESKD and death, underscoring the role of housing as a social determinant of health.},\n    issn = {2574-3805},\n    doi = {10.1001/jamanetworkopen.2024.31973},\n    url = {https://doi.org/10.1001/jamanetworkopen.2024.31973},\n    eprint = {https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2823325/koyama\\_2024\\_oi\\_240959\\_1724971275.76778.pdf},\n}\n\n
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\n Adults experiencing homelessness in the US face numerous challenges, including the management of chronic kidney disease (CKD). The extent of a potentially greater risk of adverse health outcomes in the population with CKD experiencing homelessness has not been adequately explored.To evaluate the association between a history of homelessness and the risk of end-stage kidney disease (ESKD) and death among veterans with incident CKD.This retrospective cohort study was conducted between January 1, 2005, and December 31, 2017. Participants included veterans aged 18 years and older with incident stage 3 to 5 CKD utilizing the Veterans Health Administration health care network in the US. Patients were followed-up through December 31, 2018, for the occurrence of ESKD and death. Analyses were performed from September 2022 to October 2023.History of homelessness, based on utilization of homeless services in the Veterans Health Administration or International Classification of Diseases, Ninth Revision or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes. Homelessness was measured during the 2-year baseline period prior to the index date of incident CKD.The primary outcomes were ESKD, based on initiation of kidney replacement therapy, and all-cause death. Adjusted hazard ratios (HRs) were calculated to compare veterans with a history of homelessness with those without a history of homelessness.Among 836 361 veterans, the largest proportion were aged 65 to 74 years (274 371 veterans [32.8%]) or 75 to 84 years (270 890 veterans [32.4%]), and 809 584 (96.8%) were male. A total of 26 037 veterans (3.1%) developed ESKD, and 359 991 (43.0%) died. Compared with veterans who had not experienced homelessness, those with a history of homelessness showed a significantly greater risk of ESKD (adjusted HR, 1.15; 95% CI, 1.10-1.20). A greater risk of all-cause death was also observed (HR, 1.48; 95% CI, 1.46-1.50). After further adjustment for body mass index, comorbidities, and medication use, results were attenuated for all-cause death (HR, 1.09; 95% CI, 1.07-1.11) and were no longer significant for ESKD (HR, 1.04; 95% CI, 0.99-1.09).In this cohort study of veterans with incident stage 3 to 5 CKD, a history of homelessness was significantly associated with a greater risk of ESKD and death, underscoring the role of housing as a social determinant of health.\n
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\n \n\n \n \n \n \n \n Estimation of conditional cumulative incidence functions under generalized semiparametric regression models with missing covariates, with application to analysis of biomarker correlates in vaccine trials.\n \n \n \n\n\n \n Sun, Y.; Heng, F.; Lee, U.; and Gilbert, P. B\n\n\n \n\n\n\n Canadian Journal of Statistics, 51(1): 235–257. 2023.\n \n\n\n\n
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@article{sun2023estimation,\n  title={Estimation of conditional cumulative incidence functions under generalized semiparametric regression models with missing covariates, with application to analysis of biomarker correlates in vaccine trials},\n  author={Sun, Yanqing and Heng, Fei and Lee, Unkyung and Gilbert, Peter B},\n  journal={Canadian Journal of Statistics},\n  volume={51},\n  number={1},\n  pages={235--257},\n  year={2023},\n  publisher={John Wiley \\& Sons, Inc. Hoboken, USA}\n}\n\n
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\n \n\n \n \n \n \n \n Brief report: risk stratification following curative therapy for stage I NSCLC.\n \n \n \n\n\n \n Butts, E.; Gococo-Benore, D.; Pai, T.; Moustafa, M. A.; Heng, F.; Chen, R.; Zhao, Y.; Manochakian, R.; and Lou, Y.\n\n\n \n\n\n\n Frontiers in oncology, 13. 2023.\n \n\n\n\n
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@article{butts2023brief,\n  title={Brief report: risk stratification following curative therapy for stage I NSCLC},\n  author={Butts, Emily and Gococo-Benore, Denise and Pai, Tanmayi and Moustafa, Muhamad Alhaj and Heng, Fei and Chen, Ruqin and Zhao, Yujie and Manochakian, Rami and Lou, Yanyan},\n  journal={Frontiers in oncology},\n  volume={13},\n  year={2023},\n  publisher={Frontiers Media SA}\n}\n\n
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\n \n\n \n \n \n \n \n Role of Anemia in Dementia risk among veterans with incident CKD.\n \n \n \n\n\n \n Koyama, A. K; Nee, R.; Yu, W.; Choudhury, D.; Heng, F.; Cheung, A. K; Norris, K. C; Cho, M. E; and Yan, G.\n\n\n \n\n\n\n American Journal of Kidney Diseases, 82(6): 706–714. 2023.\n \n\n\n\n
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@article{koyama2023,\n  title={Role of Anemia in Dementia risk among veterans with incident CKD},\n  author={Koyama, Alain K and Nee, Robert and Yu, Wei and Choudhury, Devasmita and Heng, Fei and Cheung, Alfred K and Norris, Keith C and Cho, Monique E and Yan, Guofen},\n  journal={American Journal of Kidney Diseases},\n  volume={82},\n  number={6},\n  pages={706--714},\n  year={2023},\n  publisher={Elsevier}\n}\n\n
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\n  \n 2022\n \n \n (3)\n \n \n
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\n \n\n \n \n \n \n \n Genomic landscape of lung adenocarcinomas in different races.\n \n \n \n\n\n \n Shi, H.; Seegobin, K.; Heng, F.; Zhou, K.; Chen, R.; Qin, H.; Manochakian, R.; Zhao, Y.; and Lou, Y.\n\n\n \n\n\n\n Frontiers in oncology, 12: 946625. 2022.\n \n\n\n\n
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@article{shi2022genomic,\n  title={Genomic landscape of lung adenocarcinomas in different races},\n  author={Shi, Huashan and Seegobin, Karan and Heng, Fei and Zhou, Kexun and Chen, Ruqin and Qin, Hong and Manochakian, Rami and Zhao, Yujie and Lou, Yanyan},\n  journal={Frontiers in oncology},\n  volume={12},\n  pages={946625},\n  year={2022},\n  publisher={Frontiers}\n}\n\n
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\n \n\n \n \n \n \n \n Neutralizing antibody correlates of sequence specific dengue disease in a tetravalent dengue vaccine efficacy trial in Asia.\n \n \n \n\n\n \n Qi, L.; Sun, Y.; Juraska, M.; Moodie, Z.; Magaret, C. A; Heng, F.; Carpp, L. N; and Gilbert, P. B\n\n\n \n\n\n\n Vaccine, 40(41): 5912–5923. 2022.\n \n\n\n\n
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@article{qi2022neutralizing,\n  title={Neutralizing antibody correlates of sequence specific dengue disease in a tetravalent dengue vaccine efficacy trial in Asia},\n  author={Qi, Li and Sun, Yanqing and Juraska, Michal and Moodie, Zoe and Magaret, Craig A and Heng, Fei and Carpp, Lindsay N and Gilbert, Peter B},\n  journal={Vaccine},\n  volume={40},\n  number={41},\n  pages={5912--5923},\n  year={2022},\n  publisher={Elsevier}\n}\n\n
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\n \n\n \n \n \n \n \n Acclimation to elevated temperatures in Acropora cervicornis: effects of host genotype and symbiont shuffling.\n \n \n \n\n\n \n Matz O. Indergard, A. B.; and Mauricio Rodriguez-Lanetty, F. H.\n\n\n \n\n\n\n Marine Ecology Progress Series, 701: 41–65. 2022.\n \n\n\n\n
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@article{matz2022acclimation,\n  title={Acclimation to elevated temperatures in Acropora cervicornis: effects of host genotype and symbiont shuffling},\n  author={Matz O. Indergard, Anthony Bellantuono, Mauricio Rodriguez-Lanetty, Fei Heng, Matthew R. Gilg},\n  journal={Marine Ecology Progress Series},\n  volume={701},\n  pages={41--65},\n  year={2022}\n}\n\n
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\n  \n 2021\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n Semiparametric additive time-varying coefficients model for longitudinal data with censored time origin.\n \n \n \n\n\n \n Sun, Y.; Shou, Q.; Gilbert, P. B; Heng, F.; and Qian, X.\n\n\n \n\n\n\n Biometrics. 2021.\n \n\n\n\n
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@article{sun2021semiparametric,\n  title={Semiparametric additive time-varying coefficients model for longitudinal data with censored time origin},\n  author={Sun, Yanqing and Shou, Qiong and Gilbert, Peter B and Heng, Fei and Qian, Xiyuan},\n  journal={Biometrics},\n  year={2021}\n}\n\n
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\n \n\n \n \n \n \n \n A hybrid approach for the stratified mark-specific proportional hazards model with missing covariates and missing marks, with application to vaccine efficacy trials.\n \n \n \n\n\n \n Sun, Y.; Qi, L.; Heng, F.; and Gilbert, P. B\n\n\n \n\n\n\n Journal of the Royal Statistical Society: Series C (Applied Statistics), 69(4): 791–814. 2020.\n \n\n\n\n
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@article{sun2020hybrid,\n  title={A hybrid approach for the stratified mark-specific proportional hazards model with missing covariates and missing marks, with application to vaccine efficacy trials},\n  author={Sun, Yanqing and Qi, Li and Heng, Fei and Gilbert, Peter B},\n  journal={Journal of the Royal Statistical Society: Series C (Applied Statistics)},\n  volume={69},\n  number={4},\n  pages={791--814},\n  year={2020},\n  publisher={Wiley Online Library}\n}\n\n
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\n \n\n \n \n \n \n \n Analysis of the time-varying Cox model for the cause-specific hazard functions with missing causes.\n \n \n \n\n\n \n Heng, F.; Sun, Y.; Hyun, S.; and Gilbert, P. B\n\n\n \n\n\n\n Lifetime data analysis, 26(4): 731–760. 2020.\n \n\n\n\n
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@article{heng2020analysis,\n  title={Analysis of the time-varying Cox model for the cause-specific hazard functions with missing causes},\n  author={Heng, Fei and Sun, Yanqing and Hyun, Seunggeun and Gilbert, Peter B},\n  journal={Lifetime data analysis},\n  volume={26},\n  number={4},\n  pages={731--760},\n  year={2020},\n  publisher={Springer US}\n}\n\n
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\n  \n 2019\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n Dynamic Modeling of Incomplete Event History Data.\n \n \n \n\n\n \n Heng, F.\n\n\n \n\n\n\n Ph.D. Thesis, The University of North Carolina at Charlotte, 2019.\n \n\n\n\n
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@phdthesis{heng2019dynamic,\n  title={Dynamic Modeling of Incomplete Event History Data},\n  author={Heng, Fei},\n  year={2019},\n  school={The University of North Carolina at Charlotte}\n}\n\n
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\n \n\n \n \n \n \n \n Analysis of generalized semiparametric mixed varying-coefficients models for longitudinal data.\n \n \n \n\n\n \n Sun, Y.; Qi, L.; Heng, F.; and Gilbert, P. B\n\n\n \n\n\n\n Canadian Journal of Statistics, 47(3): 352–373. 2019.\n \n\n\n\n
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@article{sun2019analysis,\n  title={Analysis of generalized semiparametric mixed varying-coefficients models for longitudinal data},\n  author={Sun, Yanqing and Qi, Li and Heng, Fei and Gilbert, Peter B},\n  journal={Canadian Journal of Statistics},\n  volume={47},\n  number={3},\n  pages={352--373},\n  year={2019},\n  publisher={Wiley Online Library}\n}\n\n
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