Chaeryon Kang, PhD

Associate Professor, Department of Psychiatry (Primary)

University of Pittsburgh


 

Contact

3811 O'Hara St., Pittsburgh, PA 15213

Email: crkang [at]pitt[dot]edu

About me

I am an Associate professor (biostatistics faculty) in the department of Psychiatry at the University of Pittsburgh. Prior to September 2024, I was an Assistant professor in the department of Biostatistics at the University of Pittsburgh. I received my Ph.D. in Biostatistics from the University of North Carolina at Chapel Hill in 2011, under the guidance of Dr. Michael R. Kosorok . From 2011-2014, I was a post-doctoral researcher at the Fred Hutchison Cancer Center in Seattle, mentored by Dr. Hollay Janes and Dr. Ying Huang . As a statistician, I have extensive experience in statistical analysis and modeling. Throughout my career, I have worked on various issues related to clinical trials and observational studies, such as statistical methods in biomarker evaluation for treatment selection and risk prediction, precision (personalized) medicine and predictive modeling using statistical and machine learning techniques, statistical approach in HIV vaccine efficacy trials, survival data analysis, and meta-analysis. I also have significant experience in collaborative research, including studies on cognitive psychology, psychiatric and neurodevelopmental disorders, brain and aging, breast cancer, women's health issues, and Medicare/Medicaid electronic health records. My current research focuses on developing and applying statistical methods and machine learning techniques for evaluating heterogeneity in treatment effects and latent subgroup analysis in clinical trials, observational studies, and mobile health studies.



Academic Position

2024 - Present   Associate Professor, Department of Psychiatry (Primary), School of Medicine, University of Pittsburgh

2014 - 2024       Assistant professor, Department of Biostatistics , School of Public Health, University of Pittsburgh


Education/ Training

2011               PhD, Department of Biostatistics , Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC

2011-2014         Postdoctoral Research Fellow, Vaccines and Infectious Diseases Division /Public Health Sciences Division, Fred Hutchinson Cancer Research Center , Seattle, WA



Research Interests

Precision medicine, Individualized treatment rule, Mobile health, Statistical/Machine learning, Latent subgroup analysis, Longitudinal-clustered data analysis, Functional data analysis

Differential equation modeling, Integrate multiple modalities of data, Empirical processes, Brain and aging, Mental health, Statistical methods in HIV vaccine efficacy trials.



Teaching

BIOST 2051, Statistical Estimation Theory: PhD-level core theory course, every fall semester between 2015-2023 (9 semesters), Department of Biostatistics, University of Pittsburgh

BIOST 2025, Biostatistics Seminar: fall 2016 to spring 2018 (4 semesters), Department of Biostatistics, University of Pittsburgh



Contribution to Science and selected publications

(_) underlines indicate students under supervision/co-supervision

(+) as a corresponding author

(*) as co first-authors

1. Statistical methods and theories for Precision medicine, Personalized medicine, and Latent subgroup analysis

To achieve the goal of providing the most effective treatment to each individual at the most appropriate time ("the right treatment for the right person at the right time"), I have developed and implemented statistical theories and methods for identifying and classifying latent subgroups. These subgroups may have different relationships, heterogeneous treatment effects, or distinct characteristics. I aim to develop personalized treatment recommendations that are optimal for each person.

  • Kang, C.+ , Cho, H., Song, R., Banerjee, M., Laber, E. B., and Kosorok, M. R.   Inference for change-plane regression. Accepted by Bernoulli (2024+). arxiv.org/abs/2206.06140

  • Gorczyca, M. T. and Kang, C.+.   On quantifying heterogeneous treatment effects with regression-based individualized treatment rules: Loss function families and bounds on estimation error. Stat (2024), 13(2), e680. doi:10.1002/sta4.680

  • Bo, N.*, Wei, Y.* , Zeng, L., Kang, C., and Ding, Y.   A Meta-Learner Framework to Estimate Individualized Treatment Effects for Survival Outcomes. Journal of Data Science (2024), 1-19, doi:10.6339/24-JDS1119. (an earlier version won the 2022 JSM Lifetime Data Analysis section student paper award)

  • Kang, C.+ and Huang, Y.   Identification of immune response combinations associated with heterogeneous infection risk in the immune correlate analysis of HIV vaccine studies. Annals of Applied Statistics (2023), 17(2): 1199-1219. doi:10.1214/22-aoas1665

  • Kang, C.+ , Janes, H., Tajik, P., Groen, H., Mol, B.W.J., Koopmans, C.M., Broekhuijsen, K., Zwertbroek, E., van Pampus, M. G., and Franssen, M.T.M.   Evaluation of biomarkers for treatment selection using individual participant data from multiple clinical trials. Statistics in Medicine (2018), 37(9):1439-1453. doi:10.1002/sim.7608

  • Kang, C.+ , Janes, H., and Huang, Y.   Combining biomarkers to optimize patient treatment recommendation. Biometrics (2014), 70(3): 695-707. (with discussion). doi:10.1111/biom.12191 (This paper was discussed by four leading statistical research groups in precision medicine and was ranked the top-cited paper in Biometrics in 2016)

  • Kang, C.+ , Janes, H., and Huang, Y.   Rejoinder: Combining biomarkers to optimize patient treatment recommendation. Biometrics (2014), 70(3): 695-707. (with discussion). doi:10.1111/biom.12191


2. Statistical methods for Clustered, Interactively associated, Survival data analysis

I have been working on statistical methods to analyze clustered or interactively associated data from biomedical research. First, I developed an Interactive Decision Committee (IDC) method to improve prediction accuracy in binary classification problems when high-dimensional feature variables are grouped into feature categories. The prediction models used random forests, support vector machines, and logistic regression as base learners. Second, I proposed using kappa statistics based on bootstrap sampling to improve the variance estimation of the kappa statistic to measure agreements in communication between doctors and patients. Third, I have been working on the application and development of statistical methods for HIV/AIDS studies. The results can be used to design a more efficient randomized clinical trial study for vaccine development.

  • Kang, C.+, Zhang, D. , Schuster, J., Kogan, J., Nikolajski, C., and Reynolds III., C.F.  Bias-corrected and doubly robust inference for the three-level longitudinal cluster-randomized trials with missing continuous outcomes and small number of clusters: simulation study and application to a study for adults with serious mental illnesses. Contemporary Clinical Trials Communications: study design & statistical methods (2023). Vol. 35, p.101194. doi:10.1016/j.conctc.2023.101194

  • Gao, X. , Xinxin, D., Kang, C., and Wahed, A. S.   Inference on Mean Quality-adjusted Lifetime Using Joint Models for Continuous Quality of Life Process and Time to Event. Journal of Statistical Research (2019), Vol. 53, No. 2, p. 165-189. https://doi.org/doi:10.47302/jsr.2019530205

  • Kang, C., Huang, Y., and Miller, C. J.   A discrete time survival model with random effects for designing and analyzing repeated low-dose challenge experiments. Biostatistics (2015), 16(2): 295-310. doi:10.1093/biostatistics/kxu040

  • Kang C., Qaqish B, Monaco J, Sheridan SL, Cai J.   Kappa statistic for clustered dichotomous responses from physicians and patients. Statistics in Medicine (2013). 32(21):3700-19. doi:10.1002/sim.5796

  • Kang, C., Zhu, H., Wright, F. A., Zou, F., and Kosorok, M. R.   The Interactive Decision Committee for Chemical Toxicity analysis. Journal of Statistical Research (2012), 46(2):157- 186. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3887560 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3887560


3. Mobile Health (mHealth), Functional data analysis

I am researching mHealth (mobile health) for physical activities, pain management, dynamic predictions, and precision medicine. I received the National Science Foundation grant (NSF 1557765) for methodological development in a pain study as one of the five PIs. Currently, I am focusing on functional data analysis of actigraphy and Fitbit data.

  • Jonassaint, C.R., Kang, C., Prussien, K., Yarboi, J., and Sanger. M.   Feasibility of Implementing Mobile Technology-Delivered Mental Health Treatment in Routine Adult Sickle Cell Disease Care. Translational Behavioral Medicine (2020), 10 (1):58-67. doi.org/10.1093/tbm/iby107

  • Jonassaint, C.R., Kang, C. , Jonassaint, J.C., Castro, L.D., Abrams, D.M., Li, J.J., Mao, J.,Jia, Y., Long, Q., and Shah, N.   Understanding Patterns and Correlates of Daily Pain using the Sickle Cell Disease Mobile Application to Record Symptoms via Technology (SMART). British Journal of Haematology (2018), 183(2): 306-308. doi:10.1111/bjh.14956

  • Clifton, S. M., Kang, C. , Li, J., Long, Q., Shah,N., and Abrams, D. M.   Hybrid statistical and mechanistic mathematical model guides mobile health intervention for chronic pain. Journal of Computational Biology (2017), 24(7):675-688. doi:10.1089/cmb.2017.0059 (This paper was supported by the NSF grant 1557765, QuBBD: Advancing mHealth using Big Data Analytics: Statistical and Dynamical Systems Modeling of Real-Time Adaptive m-Intervention for Pain, where Kang was one of the Multi-PIs.)


4. Research in Physical activities, Brain and Aging, Cognitive Psychology, and Alzheimer's Disease

I have participated in multiple projects for research in physical activity, brain aging, and cognitive health as a site PI, co-I, primary statistician, and statistical mentor, including IGNITE (R01AG053952, Investigating Gains in Neurocognition in an Intervention Trial of Exercise), FLAME (R01AG083156, Examining the Persistence of Neurocognitive Benefits of Exercise), eBACH (P01HL040962, Biobehavioral studies of cardiovascular disease) and REACT (R01AG060741-01, Rhythm Experience and African Culture Trial).

  • Molina Hidalgo C, Collins AM, Crisafio ME, Grove G, Kamarck TW, Kang C , Leckie RL, MacDonald M, Manuck SB, Marsland AL, Muldoon MF, Rasero J, Scudder MR, Velazquez-Diaz D, Verstynen T, Wan L, Gianaros PJ, Erickson KI.  Effects of a laboratory-based aerobic exercise intervention on brain volume and cardiovascular health markers: protocol for a randomised clinical trial. BMJ Open (2023). 13(11): e077905. doi:10.1136/bmjopen-2023-077905

  • Stillman, C.M., Jakicic, J.M., Rogers, R.J., Roecklein. K.A., Watt, J.C., Barrett, G., Kang,C , and Erickson, K.I.   The Relationship Between FTO Polymorphism rs993609 and Resting Cerebral Blood Flow in A Midlife Sample with Overweight and Obesity. Frontiers Human Neuroscience (2022), p. 496. doi:10.3389/fnhum.2022.904545.

  • Aghjayan, S.L., Bournias, T., Kang, C. , Zhou, X. , Stillman, C.M., Donofry, S.D., Kamarck, T.W., Marsland, A.L., Voss, M. W., Fraundorf, S.H., Erickson, K.I.   Aerobic Exercise Improves Episodic Memory in Late Adulthood: A meta-analysis. Communications Medicine (2022), 2(1): pp.1-11. doi:10.1038/s43856-022-00079-7

  • Wilckens KA, Stillman CM, Waiwood AM, Kang C , Leckie RL, Peven JC, Foust JE, Fraundorf SH, Erickson KI.   Exercise interventions preserve hippocampal volume: A meta-analysis. Hippocampus (2021). 31(3):335-347. doi:https://doi.org/10.1073/pnas.1015950108

  • Erickson, K. I., Grove, G., Burns, J. M., Hillman, C., Kramer, A. F., McAuley, E., Vidoni, E. D., Becker, J., Butters, M., Grey, K., Huang, H., Jakicic, J., Kang, C. , Klunk, W., Lee, P., Marsland, A., Mettenburg, J., Rogers, R., Stillman, C., Sutton, B. , Szabo-Reed, A., Verstynen, T., Watt, J., Weinstein, A., Wollam, M.   Investigating Gains in Neurocognition in an Intervention Trial of Exercise (IGNITE): Protocol. Contemporary Clinical Trials (2019). 85: 105832. doi.org/10.1016/j.cct.2019.105832.

5. Research in Mental health and Psychiatry

I worked as a graduate research assistant at the UNC-CH Schizophrenia Research Center. Also, I have been involved in several PCORI-funded projects focusing on mental health research, working closely with the UPMC Center for High-Value Health Care team.

  • MacDonald-Wilson KL, Williams K, Nikolajski CE, McHugo G, Kang C , Deegan P, Carpenter-Song E, Kogan JN.  Promoting collaborative psychiatric care decision-making in community mental health centers: Insights from a patient-centered comparative effectiveness trial. Psychiatric Rehabilitation Journal (2021). 44(1):11-21. doi:10.1037/prj0000455.

  • Kearney SM, Williams K, Nikolajski C, Park MJ, Kraemer KL, Landsittel D, Kang C , Malito A, Schuster J.  Stakeholder impact on the implementation of integrated care: Opportunities to consider for patient-centered outcomes research. Contemporary Clinical Trials (2021). 101:106256. doi.org/10.1016/j.cct.2020.106256.

  • Kogan JN, Schuster J, Nikolajski C, Schake P, Carney T, Morton SC, Kang C , Reynolds III., C.F.   Challenges encountered in the conduct of Optimal Health: A patient-centered comparative effectiveness study of interventions for adults with serious mental illness. Clinical Trials (2017). 14(1):5-16. doi:10.1177/1740774516670895

  • Knickmeyer RC, Gouttard S, Kang C , Evans D, Wilber K, Smith JK, Hamer RM, Lin W, Gerig G, Gilmore JH.   A structural MRI study of human brain development from birth to 2 years. Journal of Neuroscience (2008). 28(47):12176-12182. doi.org/doi:10.1523/JNEUROSCI.3479-08.2008

  • Gilmore, J. H., Kang, C. , Evans, D. D., Wolfe, H. M., Smith, M. D., Lieberman, J. A., Lin, W., Hamer, R. M., Styner, M. and Gerig, G.   Prenatal and Neonatal Brain Structure and White Matter Maturation in Children at High Risk for Schizophrenia. American Journal of Psychiatry (2010), 167(9): 1083-1091. doi:10.1176/appi.ajp.2010.09101492


Complete List of Published Work in My Bibliography : https://www.ncbi.nlm.nih.gov/myncbi/chae ryon.kang.1/bibliography/public/



Primary Research Projects

1. Current Projects

Examining the Persistence of Neurocognitive Benefits of Exercise (FLAME)
National Institute of Health,   R01AG083156 (PI: Erickson),   09/01/2023 - 06/30/2028
This is a follow-up Longitudinal Analysis of Moderate-intensity Exercise (FLAME) that aims to test the long-term effects of a previously conducted randomized exercise clinical trial initiated in 2016 called, "IGNITE" which examined the effects of exercise on cognitive and brain health in cognitively normal older adults. FLAME will examine 1) whether participating in a moderate-intensity exercise intervention influences the rate of cognitive performance changes and risk for Alzheimer's disease pathology 5-years later 2) the extent to which participants maintained the exercise routines they started in the IGNITE study 3) determine whether there are any factors predicting long-term adherence to exercise behaviors.
Role: Co-Investigator (08/16/2024-present)/   Pitt Site PI (09/02/2023- 08/15/2024).


2. Completed Projects

QuBBD: Advancing mHealth using Big Data Analytics: Statistical and Dynamical Systems Modeling of Real-Time Adaptive m-Intervention for Pain
National Science Foundation,   1557765 (PI: Abrams, Kang, Long, Shah, and Li),   09/15/2015 - 08/31/2016
Role: Principal Investigator (MPI)

Estimation of optimal individualized treatment rule balancing multiple patient outcomes
University of Pittsburgh,  Central Research Development Fund (PI: Kang),   09/01/2018- 06/30/2020 (including no-cost extension (NCE) due to the Pandemic)
Role: Principal Investigator

Physical Activity and Dementia: Mechanisms of Action
National Institute of Health,   R35, 7R35AG072307-03 (PI: Erickson),   06/13/2022 - 04/30/2026 (effort ends 08/31/2024)
Role: Co-Investigator (06/13/2022- October, 2023 and 05/01/2024-08/31/2024)/   Pitt Site PI (October, 2023- 04/30/2024)

Examining effects of a dose-dependent exercise intervention on cerebrovascular plasticity and cognition
National Institute on Aging,  1R01AG082700-01 (PI:Kim),   09/01/2023 - 05/31/2027 (effort ends 08/31/2024)
Role: Co-Investigator

Optimizing Approaches to Select Implementation Strategies (OASIS)
HSR&D, VA Research,   (PI:Rogal),   7/1/2023 - 6/30/2027 (effort ends 08/31/2024)
Role: Statistician

Evaluating the Impact of PAServes on Receipt of Veteran Benefits, VHA Healthcare Use, and Associated Healthcare Costs
The Heinz Endowments, (PI:Hausmann), 10/1/2023 - 9/30/2024 (effort ends 08/31/2024)
Role: Co-Investigator

Consortium for the Study of Pancreatitis: Pittsburgh Clinical Center
NCI/NIDDK,  5U01DK108306 (PI: Yadav),   09/30/2021 - 06/30/25 (effort ends 08/31/2024)
Role: Statistician

UPMC Clinical Center for the Study of Diabetes After Acute Pancreatitis
NIDDK,  5U01DK108306 (PI: Yadav),   9/30/2021- 07/31/2025 (effort ends 08/31/2024)
Role: Statistician

Leveraging Integrated Models of Care to Improve Patient-Centered Outcomes for Publicly-Insured Adults with Complex Health Care Needs
Patient-Centered Outcomes Research Institute (PCORI),   1609-36670 (PI: Schuster),   01/01/2018-05/31/2024 (including NCE due to the Pandemic)
Role: Co-Investigator

Biobehavioral Studies of Cardiovascular Disease (eBACH)
National Heart, Lung, and Blood Institute,   P01   2P01HL040962-21A1 (PI: Gianaro),   08/15/2018-08/31/2024 (including NCE due to the Pandemic)
This study applies integrative, multi-level and multi-method approaches in community samples of midlife men and women to understand brain phenotypes for cardiovascular disease risk.
Role: Co-Investigator

Rhythm Experience an Africiana Culture Trial (REACT)
National Institute of Health,   1R01AG060741-01 (PI: Erickson),   09/01/2018-08/31/2024 (including NCE due to the Pandemic)
In this project, we will collect measures of physical and psychosocial health such as waist circumference, blood pressure, blood glucose and lipid levels, mood, anxiety, depression, and loneliness and examine whether intervention-related changes to these measures mediate improvements in cognitive performance.
Role: Co-Investigator

Antioxidant imaging marker of investigating gains in neurocognition in an intervention trial of exercise (AIM-IGNITE)
National Institute of Health,   1R01AG060050-01A1 (PI: Lee),   02/15/19 - 11/30/24
Role: Statistician

Investigating Gains in Neurocognition in an Intervention Trial of Exercise (IGNITE)
National Institute on Aging,  R01AG053952 (PI: Erickson),   09/15/2016-08/31/2024 (including NCEs due to the Pandemic)
The goal of this 12-month randomized clinical trial is to more definitively address whether monitored exercise influences cognitive and brain health in cognitively normal older adults.
Role: Co-Investigator

Amplifying the Patient's Voice: Person-Centered Versus Measurement-Based Approaches in Mental Health
Patient-Centered Outcomes Research Institute (PCORI),   402 (PI: MacDonald-Wilson),   04/01/2014 - 02/28/2018
Role: Co-Investigator

Optimizing Behavioral Health Homes by Focusing On Outcomes That Matter Most for Adults with Serious Mental Illness
Patient-Centered Outcomes Research Institute (PCORI),   673 (PI: Schuster),   05/01/2013 - 05/31/2017
Role: Co-Investigator



Service Activities

1. Editorial activities /Journal Reviewer

March 2013- Feb. 2016,  Editorial Board,   Journal of Health Informatics and Statistics

2013 - Present,  Reviewer,  Annals of Statistics
2013 - Present,  Reviewer,  Statistics in Medicine
2013 - Present,  Reviewer,  Clinical Trials
2015 - Present,  Reviewer,  Journal of the American Statistical Association
2015 - Present,  Reviewer,  Biometrics
2016 - Present,  Reviewer,  Contemporary Clinical Trials Communications
2016 - Present,  Reviewer,  Journal of Biopharmaceutical Statistics
2016 - Present,  Reviewer,  BMC Medical Research Methodology
2016 - Present,  Reviewer,  Statistica Sinica
2017 - Present,  Reviewer,  Biometrical Journal
2017 - Present,  Reviewer,  Electronic Journal of Statistics
2018 - Present,  Reviewer,  Sankhya B: The Indian Journal of Statistics
2019 - Present,  Reviewer,  Communications in Statistics
2019 - Present,  Reviewer,  Contemporary Clinical Trials
2022 - Present,  Reviewer,  American Journal of Epidemiology
2023 - Present,  Reviewer,  Asia Pacific Education Review
2024 - Present,  Reviewer,  Artificial Intelligence in Medicine


2. Data and Safety Monitoring Boards (DSMBs)

October. 2022- Present,   Committee member,   R01AG076669 ( PI: Christina E. Hugenschmidt).
Establishing the optimal frequency of dance movement for neurocognitive and physical outcomes in people at risk of Alzheimer's disease (IGROOVE)



Professional Memberships

2008 - Present,   Member,  American Statistical Association (ASA)
2009 - Present,   Member,   Eastern North American Region, The International Biometric Society (ENAR)
2015 - Present,   Member,   Korean International Statistical Society
2013 - 2014,      Member,   Western North American Region, The International Biometric Society (WNAR)