Browsing by Author "Zeng, Donglin"

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Open Access
Bayesian inference and dynamic prediction for multivariate longitudinal and survival data
(The Annals of Applied Statistics, 2023-09-01) Zou, Haotian; Zeng, Donglin; Xiao, Luo; Luo, Sheng
Open Access
Bayesian inference and dynamic prediction of multivariate joint model with functional data: An application to Alzheimer's disease.
(Statistics in medicine, 2021-10-14) Zou, Haotian; Li, Kan; Zeng, Donglin; Luo, Sheng; Alzheimer's Disease Neuroimaging Initiative
Alzheimer's disease (AD) is a severe neurodegenerative disorder impairing multiple domains, for example, cognition and behavior. Assessing the risk of AD progression and initiating timely interventions at early stages are critical to improve the quality of life for AD patients. Due to the heterogeneous nature and complex mechanisms of AD, one single longitudinal outcome is insufficient to assess AD severity and disease progression. Therefore, AD studies collect multiple longitudinal outcomes, including cognitive and behavioral measurements, as well as structural brain images such as magnetic resonance imaging (MRI). How to utilize the multivariate longitudinal outcomes and MRI data to make efficient statistical inference and prediction is an open question. In this article, we propose a multivariate joint model with functional data (MJM-FD) framework that relates multiple correlated longitudinal outcomes to a survival outcome, and use the scalar-on-function regression method to include voxel-based whole-brain MRI data as functional predictors in both longitudinal and survival models. We adopt a Bayesian paradigm to make statistical inference and develop a dynamic prediction framework to predict an individual's future longitudinal outcomes and risk of a survival event. We validate the MJM-FD framework through extensive simulation studies and apply it to the motivating Alzheimer's Disease Neuroimaging Initiative (ADNI) study.
Open Access
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.
(Proceedings of the National Academy of Sciences of the United States of America, 2022-04) Cramer, Estee Y; Ray, Evan L; Lopez, Velma K; Bracher, Johannes; Brennen, Andrea; Castro Rivadeneira, Alvaro J; Gerding, Aaron; Gneiting, Tilmann; House, Katie H; Huang, Yuxin; Jayawardena, Dasuni; Kanji, Abdul H; Khandelwal, Ayush; Le, Khoa; Mühlemann, Anja; Niemi, Jarad; Shah, Apurv; Stark, Ariane; Wang, Yijin; Wattanachit, Nutcha; Zorn, Martha W; Gu, Youyang; Jain, Sansiddh; Bannur, Nayana; Deva, Ayush; Kulkarni, Mihir; Merugu, Srujana; Raval, Alpan; Shingi, Siddhant; Tiwari, Avtansh; White, Jerome; Abernethy, Neil F; Woody, Spencer; Dahan, Maytal; Fox, Spencer; Gaither, Kelly; Lachmann, Michael; Meyers, Lauren Ancel; Scott, James G; Tec, Mauricio; Srivastava, Ajitesh; George, Glover E; Cegan, Jeffrey C; Dettwiller, Ian D; England, William P; Farthing, Matthew W; Hunter, Robert H; Lafferty, Brandon; Linkov, Igor; Mayo, Michael L; Parno, Matthew D; Rowland, Michael A; Trump, Benjamin D; Zhang-James, Yanli; Chen, Samuel; Faraone, Stephen V; Hess, Jonathan; Morley, Christopher P; Salekin, Asif; Wang, Dongliang; Corsetti, Sabrina M; Baer, Thomas M; Eisenberg, Marisa C; Falb, Karl; Huang, Yitao; Martin, Emily T; McCauley, Ella; Myers, Robert L; Schwarz, Tom; Sheldon, Daniel; Gibson, Graham Casey; Yu, Rose; Gao, Liyao; Ma, Yian; Wu, Dongxia; Yan, Xifeng; Jin, Xiaoyong; Wang, Yu-Xiang; Chen, YangQuan; Guo, Lihong; Zhao, Yanting; Gu, Quanquan; Chen, Jinghui; Wang, Lingxiao; Xu, Pan; Zhang, Weitong; Zou, Difan; Biegel, Hannah; Lega, Joceline; McConnell, Steve; Nagraj, VP; Guertin, Stephanie L; Hulme-Lowe, Christopher; Turner, Stephen D; Shi, Yunfeng; Ban, Xuegang; Walraven, Robert; Hong, Qi-Jun; Kong, Stanley; van de Walle, Axel; Turtle, James A; Ben-Nun, Michal; Riley, Steven; Riley, Pete; Koyluoglu, Ugur; DesRoches, David; Forli, Pedro; Hamory, Bruce; Kyriakides, Christina; Leis, Helen; Milliken, John; Moloney, Michael; Morgan, James; Nirgudkar, Ninad; Ozcan, Gokce; Piwonka, Noah; Ravi, Matt; Schrader, Chris; Shakhnovich, Elizabeth; Siegel, Daniel; Spatz, Ryan; Stiefeling, Chris; Wilkinson, Barrie; Wong, Alexander; Cavany, Sean; España, Guido; Moore, Sean; Oidtman, Rachel; Perkins, Alex; Kraus, David; Kraus, Andrea; Gao, Zhifeng; Bian, Jiang; Cao, Wei; Lavista Ferres, Juan; Li, Chaozhuo; Liu, Tie-Yan; Xie, Xing; Zhang, Shun; Zheng, Shun; Vespignani, Alessandro; Chinazzi, Matteo; Davis, Jessica T; Mu, Kunpeng; Pastore Y Piontti, Ana; Xiong, Xinyue; Zheng, Andrew; Baek, Jackie; Farias, Vivek; Georgescu, Andreea; Levi, Retsef; Sinha, Deeksha; Wilde, Joshua; Perakis, Georgia; Bennouna, Mohammed Amine; Nze-Ndong, David; Singhvi, Divya; Spantidakis, Ioannis; Thayaparan, Leann; Tsiourvas, Asterios; Sarker, Arnab; Jadbabaie, Ali; Shah, Devavrat; Della Penna, Nicolas; Celi, Leo A; Sundar, Saketh; Wolfinger, Russ; Osthus, Dave; Castro, Lauren; Fairchild, Geoffrey; Michaud, Isaac; Karlen, Dean; Kinsey, Matt; Mullany, Luke C; Rainwater-Lovett, Kaitlin; Shin, Lauren; Tallaksen, Katharine; Wilson, Shelby; Lee, Elizabeth C; Dent, Juan; Grantz, Kyra H; Hill, Alison L; Kaminsky, Joshua; Kaminsky, Kathryn; Keegan, Lindsay T; Lauer, Stephen A; Lemaitre, Joseph C; Lessler, Justin; Meredith, Hannah R; Perez-Saez, Javier; Shah, Sam; Smith, Claire P; Truelove, Shaun A; Wills, Josh; Marshall, Maximilian; Gardner, Lauren; Nixon, Kristen; Burant, John C; Wang, Lily; Gao, Lei; Gu, Zhiling; Kim, Myungjin; Li, Xinyi; Wang, Guannan; Wang, Yueying; Yu, Shan; Reiner, Robert C; Barber, Ryan; Gakidou, Emmanuela; Hay, Simon I; Lim, Steve; Murray, Chris; Pigott, David; Gurung, Heidi L; Baccam, Prasith; Stage, Steven A; Suchoski, Bradley T; Prakash, B Aditya; Adhikari, Bijaya; Cui, Jiaming; Rodríguez, Alexander; Tabassum, Anika; Xie, Jiajia; Keskinocak, Pinar; Asplund, John; Baxter, Arden; Oruc, Buse Eylul; Serban, Nicoleta; Arik, Sercan O; Dusenberry, Mike; Epshteyn, Arkady; Kanal, Elli; Le, Long T; Li, Chun-Liang; Pfister, Tomas; Sava, Dario; Sinha, Rajarishi; Tsai, Thomas; Yoder, Nate; Yoon, Jinsung; Zhang, Leyou; Abbott, Sam; Bosse, Nikos I; Funk, Sebastian; Hellewell, Joel; Meakin, Sophie R; Sherratt, Katharine; Zhou, Mingyuan; Kalantari, Rahi; Yamana, Teresa K; Pei, Sen; Shaman, Jeffrey; Li, Michael L; Bertsimas, Dimitris; Skali Lami, Omar; Soni, Saksham; Tazi Bouardi, Hamza; Ayer, Turgay; Adee, Madeline; Chhatwal, Jagpreet; Dalgic, Ozden O; Ladd, Mary A; Linas, Benjamin P; Mueller, Peter; Xiao, Jade; Wang, Yuanjia; Wang, Qinxia; Xie, Shanghong; Zeng, Donglin; Green, Alden; Bien, Jacob; Brooks, Logan; Hu, Addison J; Jahja, Maria; McDonald, Daniel; Narasimhan, Balasubramanian; Politsch, Collin; Rajanala, Samyak; Rumack, Aaron; Simon, Noah; Tibshirani, Ryan J; Tibshirani, Rob; Ventura, Valerie; Wasserman, Larry; O'Dea, Eamon B; Drake, John M; Pagano, Robert; Tran, Quoc T; Ho, Lam Si Tung; Huynh, Huong; Walker, Jo W; Slayton, Rachel B; Johansson, Michael A; Biggerstaff, Matthew; Reich, Nicholas G
Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.
Open Access
Multivariate functional mixed model with MRI data: An application to Alzheimer's disease.
(Statistics in medicine, 2023-02) Zou, Haotian; Xiao, Luo; Zeng, Donglin; Luo, Sheng; Alzheimer's Disease Neuroimaging Initiative
Alzheimer's Disease (AD) is the leading cause of dementia and impairment in various domains. Recent AD studies, (ie, Alzheimer's Disease Neuroimaging Initiative (ADNI) study), collect multimodal data, including longitudinal neurological assessments and magnetic resonance imaging (MRI) data, to better study the disease progression. Adopting early interventions is essential to slow AD progression for subjects with mild cognitive impairment (MCI). It is of particular interest to develop an AD predictive model that leverages multimodal data and provides accurate personalized predictions. In this article, we propose a multivariate functional mixed model with MRI data (MFMM-MRI) that simultaneously models longitudinal neurological assessments, baseline MRI data, and the survival outcome (ie, dementia onset) for subjects with MCI at baseline. Two functional forms (the random-effects model and instantaneous model) linking the longitudinal and survival process are investigated. We use Markov Chain Monte Carlo (MCMC) method based on No-U-Turn Sampling (NUTS) algorithm to obtain posterior samples. We develop a dynamic prediction framework that provides accurate personalized predictions of longitudinal trajectories and survival probability. We apply MFMM-MRI to the ADNI study and identify significant associations among longitudinal outcomes, MRI data, and the risk of dementia onset. The instantaneous model with voxels from the whole brain has the best prediction performance among all candidate models. The simulation study supports the validity of the estimation and dynamic prediction method.