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Maiying Kong, PhD

Maiying Kong imageBioinformatics and Biostatistics
Professor
485 E. Gray St., Room 138
Louisville, KY 40202
Phone: 502-852-3988
Fax: 502-852-3294
maiying.kong@louisville.edu

CURRICULUM VITAE

Education
B.S., Computational Mathematics, Xian Jiaotong University, Shaanxi, China.
M.S., Computational Mathematics, Xian Jiaotong University, Shaanxi, China.
Ph.D., Statistics, 2004, Indiana University, Bloomington, Indiana. (Advisor: Rabi Bhattacharya)

Professional Experience
Wendell Cherry Chair in Clinical Trial Research, July 2023--, University of Louisville
Director, July 2023--, Biostatistics Shared Facility, James Graham Brown Cancer Center, School of Medicine, University of Louisville.
Professor, July 2019-Current, Department of Bioinformatics and Biostatistics, SPHIS, University of Louisville. 
Associate Professor, July 2012-June 2019, Department of Bioinformatics and Biostatistics, SPHIS, University of Louisville
Associate Member, May 2018-June 2023, Department of Pediatrics, School of Medicine, University of Louisville.
Associate Member, July 2006-Current, James Graham Brown Cancer Center, University of Louisville. 
Assistant Professor, July 2006- June 2012, Department of Bioinformatics and Biostatistics, SPHIS, University of Louisville
Post-doc in Biostatistics, 2004-2006, University of Texas, M. D. Anderson Cancer Center (Mentor: J. Jack Lee).

 

Research Interests
One of my current research interests is to study and develop appropriate statistical methods to compare effectiveness of different treatments and procedures based on observational data such as Medicaid data and electronic health record data. My other current research interest is to study and develop statistical methods for clinical trials and high dimensional data such as mass spectrometry data, and link them with clinical outcomes. My previous experience and expertise includes longitudinal data analysis, mixed effect models, generalized linear models, smoothing splines, high dimensional data analyses, modeling count data, pre-clinical studies, assessing drug interactions, and assessing drug activity in xenograft models.

Advising Activities as a Primary Advisor for MS students

  1. Ming Wang (co-advised with Dr. Somnath Datta), MS in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, graduated in summer 2008. Thesis title “Clustered longitudinal data analysis”. 
  2. Lin Sun, MS in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, graduated in summer 2010. Thesis title “Comparisons of different statistical methods for analyzing longitudinal data with missing observations”.
  3. Hyejeong Jang, MS in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, graduated in the spring 2011. Thesis title “Mixed-effects models for modeling cardiac functions and treatment effects”.
  4. Lei Zhou, MS in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, Fall 2012. Thesis title “Application of linear mixed-effects models to crossover designs”.
  5. Sheng Xu (co-advised with Dr. Somnath Datta), MS in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, Spring 2013. Thesis title “Generalized estimating equation based zero-inflated models with application to examining the relationship between dental caries and fluoride exposures”. 
  6.  Kristopher Cody Gardner, MS in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, Spring 2014. Thesis title “Statistical methods for assessing treatment effects for observational studies”.
  7. John Craycroft, MS in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, Spring 2016. Thesis title “Propensity score methods: a simulation and case study involving breast cancer patients”. 
  8. Onajia Josiah Stubblefield, MS in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, Summer 2021. Thesis title “Predictive modeling of clinical outcomes for hospitalized COVID-19 patients utilizing CyTOF and clinical data”

Advising Activities as a Primary Advisor for Ph.D. students

  1. Yubing Wan (co-advise with Dr. Susmita Datta), Ph.D. in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, Summer 2014. Dissertation title: “Penalized regressions for variable selection model, single index model and an analysis of mass spectrometry data”.
  2. Younathan Abdia (co-advise with Dr. Somnath Datta & Dr. K.B. Kulasekera), Ph.D. in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, Summer 2016. Dissertation title: “Propensity score based methods for estimating the treatment effects based on observational studies”.
  3. You Wu (co-advise with Dr. Susmita Datta), Ph.D. in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, summer 2017. Dissertation title: “Bayesian approach on short time-course data of protein phosphorylation, casual inference for ordinal outcome and causal analysis of dietary and physical activity in T2DM using NHANES data”.
  4. Soutik Ghosal, Ph.D. in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, Graduated in summer 2018. Dissertation title: “Generalized spatiotemporal modeling and causal inference for assessing treatment effects for multiple groups for ordinal outcome."
  5. Xiaofang Yan, Ph.D. in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, graduated in December 2019. Dissertation title: “Statistical methods for estimating and testing treatment effect for multiple treatment groups in observational studies”.
  6. John A Craycroft, Ph.D. in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, graduated in December 2020. Dissertation title: “Aspects of causal inference”.
  7. Jingchao Sun (co-advised with Dr. Subhadip Pal), Ph.D. Candidate in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, graduated in December 2020. Dissertation title: “Modied-Half-Normal distribution and different methods to estimate average treatment effect”.
  8. Indranil Ghosh, Ph.D. in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, graduated in December 2021. Dissertation title: “Estimating Treatment Effect on Medical Cost and Examining Medical Cost Trajectory using Splines and Change-point Techniques”.
  9. Qian Xu, Ph.D. Ph.D in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, graduated in May 2022. Dissertation title: “Statistical methods for assessing drug interactions and identifying effect modifiers using observational data."
  10. Sudaraka Tholkage (co-advising with Dr. KB Kulasekera and Qi Zheng), Ph.D. in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, graduated in July 2022. Dissertation title: “Statistical methods for personalized treatment selection and survival data analysis based on observational data with high-dimensional covariates.”  
  11. Triparna Poddar, Ph.D. Candidate in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, expect to graduate in December 2023.
  12. Huirong Hu (co-advisor with Riten Mitra), Ph.D. Candidate in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, expect to graduate in summer 2023.
  13. Yuchen Han (co-advisor with Riten Mitra), Ph.D. Candidate in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, expect to graduate in summer 2024.
  14. Mst Sharmin Akter Sumy, Ph.D. student in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, expect to graduate in summer 2025.
  15. Stanley Kotey, Ph.D. student in Biostatistics, Department of Bioinformatics and Biostatistics, University of Louisville, expect to graduate in summer 2025.

Teaching Activities
I have taught the following doctoral level courses:
PHST 781:   Advanced Linear Model
PHST 782:   Generalized Linear Model
PHST 704:   Mixed Effect Models and Longitudinal Data Analyses
PHST 751:   High Throughput Data Analysis
PHST 710:   Advanced Computing I

In addition, I, along with my colleagues, developed the following elective course for Ph.D. students in SPHIS:
PHST 650:  Intermediate Biostatistics for Health Sciences

Honors and Awards
Elected Member of the International Statistical Institute (ISI), 2023
Associate Editor, Journal of Statistical Computation and Simulation, 2014-Current.
Trainee Excellence Award Winner, M.D. Anderson Alumni and Faculty Association, March 2006.
Starr Fellowship, International Program, Indiana University, 2003-2004.
William B. Wilcox Mathematics Award, Indiana University, May 2002.

Active Grants

Different services for patients diagnosed with alcohol use disorder: telehealth, certified peer support services, and targeted case management.
Funding Agency:  KY Medicaid and University of Louisville SPHIS
Grant Number: ON2 746 2300003442 (C4379)        
 Role: Principal-Investigator (20% FTE).
 Duration: 7/1/2023-6/30/2024

University of Louisville Center for Integrative Environmental Health Sciences
Funding agency: National Institute of Environmental Health Sciences
Grant Number: P30 ES030283 (PI: J.C. States)
Role: Co-Director for Biostatistics and Informatics Facility Core (10% FTE)
Duration: 7/15/2020-3/31/2025

Disparities along the care continuum for rural cancer patients
Funding agency: American Cancer Society
Grant Number: CSDG-22-125-01-HOPS (PI: M. Egger)
Role: Co-Investigator (5% FTE +100% for a Ph.D student)
Duration: 1/1/2023-12/31/2026
 
Low Density Neutrophils Link Inflammation and Coagulopathy in COVID-19
Grant Number: R01HL158779 (PIs: J. Yan, J. Huang, S.M. Uriarte)
Funding Agency: National Heart, Lung, and Blood Institute
Role: Co-Investigator (5% FTE + 50% for a Ph.D. student)
Duration: 9/2022-8/2026

Neutrophil Heterogeneity and Immunopathogenesis of COVID-19 ARDS
Grant Number: R01 AI172873 (PIs: J. Yan, J. Huang, S.M. Uriarte)
Funding Agency: National Institute of Allergy and Infectious Diseases
Role: Co-Investigator (1% FTE)
Duration: 1/1/2023-12/31/2027

Determine the molecular and metabolic mechanisms by which A-FABP links dysregulated lipid metabolism-induced obesity/breast cancer risk
Funding Agency: National Cancer Institute
Grant Number: 1U01CA272424-01 to University of Iowa (PI: B. Li)
Role: Subcontract PI (5%)
Duration: 08/1/2022 – 07/31/2027
 
Immunomodulatory mechanisms of E-FABP in psoriasis pathogenesis
Funding Agency: National Institute of Allergy and Infectious Diseases
Grant Number: R01AI137324 to University of Iowa (PI: B Li)
Role: Subcontract PI (5%)
Duration: 09/1/2021 – 08/31/2023

Selected Completed Grants 

Alcohol Use Disorder: Its Risk Factors, Comorbidity, and Long-term Care Cost.
Funding Agency:  KY Medicaid and University of Louisville SPHIS
Role: Principal Investigator (Co-PI: KB Kulasekera).
Percentage effort: 15%
Period: 07/01/2020-6/30/2022

Identification of Proteins from Mass Spectrometry Data: A Statistical Approach
Funding Agency:  National Cancer Institute 
Grant Number: 1R15CA170091-01A1 
Role: Principle-Investigator (20%) 
Duration: 03/04/2015 – 02/28/2017. 

A wearable sensor system for hand hygiene compliance tracking
Funding Agency: National Institutes of Health 
Grant Number: 1R44AG060848-01
Role: Principal Investigator for subcontract (PI: P. Liu)
Percent Effort: 5% FTE and 50% student support for second year
Duration: 09/30/2019 – 08/30/2022 

Protection of ischemic myocardium.
Funding Agency: National Institutes of Health | Grant Number: P01HL078825-11 Rev.                    
Role: Co-Investigator (10%; PI: R. Bolli) | Duration: 9/1/2017 – 8/30/2022

Reversing MDSC-mediated immunosuppression by Beta-glucan treatment
Funding Agency:  American Cancer Society
Role: Co-Investigator (5%; PI: C. Ding) | Duration: 1/1/2015 – 12/30/2018

MicroRNAs as Biomarkers for Tobacco Exposure and Heart Disease
Funding Agency:  National Institutes of Health | Grant Number:  1R21HL120050-01A1
Role: Co-Investigator (5%; PI: Y. Li) | Duration: 06/01/14-05/31/15.  

GE Industrial Athlete Program.
Funding Agency: GE Appliance Park. 
Role: Principle Investigator (15%) | Duration: 1/1/2014-12/31/2014. 

Novel Treatments of Acrolein-induced Cardiotoxicity
Funding Agency:  National Institutes of Health (NIH: R21)
Role: Co-Investigator (10%; PI: D. Conklin) | Duration: 9/1/2013-8/31/2015. 

Consortium for preclinicAl assESsment of cARdioprotective therapies (CAESAR).
Funding Agency: NIH/ NHLBI | Grant Number: U24 HL094373.
Role: Director of the Biostatistics Core (7% first year and 20% thereafter; PI: Roberto Bolli)
Duration: 8/1/2010-7/31/2015 ($9,560,000).

Center of Excellence in Diabetes and Obesity Research.
Funding Agency: NIH/NCRR | Grant Number: 2 P20 RR024489
Role: Biostatistician (10%; PI: A. Bhatnagar) | Duration: 8/1/2013-4/31/2014. 

Publications

Journal Articles

  1. Xu Q, Antimisiaris D, Kong M (2023). Statistical methods for assessing drug interactions using observational data. Journal of Applied Statistics (in press), 1-26.
  2. Sun J, Kong M and Pal S (2023). The Modified-Half-Normal distribution: Properties and an efficient sampling scheme. Communications in Statistics-Theory and Methods, 52(5), pp.1591-1613.
  3. Kulasekera KB, Tholkage S, Kong M (2022). Personalized treatment selection using observational data. Journal of Applied Statistics, 50(5), pp.1115-1127.

  4.  Yan X, Zheng Q, Kong M (2022). Weighted χ2 tests for multiple group comparisons in observational studies. Journal of Statistical Computation and Simulation, 92(13), pp.2667-2685.

  5. Wu Y, Datta S, Little BB, Kong M (2021). Magnesium dietary intake and physical activity in Type 2 diabetes by gender in White, African‐American and Mexican American: NHANES 2011‐2014 (2021). Endocrinology, Diabetes & Metabolism 4(1):e00203.

  6. Ghosal S, Lau TS, Gaskins J, Kong M (2020). A hierarchical mixed effect hurdle model for spatiotemporal count data and its application to identifying factors impacting health professional shortages. Journal of the Royal Statistical Society: Series C (Applied Statistics) 69(5):1121-1144.

  7. Craycroft J, Huang J, Kong M (2020). Propensity score specification for optimal estimation of average treatment effect with binary response. Statistical Methods in Medical Research, 29(12), pp.3623-3640.

  8. Yan XF, Abdia Y, Datta S, Kulasekera KB, Ugiliweneza B, Boakye M, Kong M (2019). Estimation of average treatment effects among multiple treatment groups by using an ensemble approach.  Statistics in Medicine 38 (15) 2828-2846. https://doi.org/10.1002/sim.8146
  9. Satten GA, Kong M, Datta S (2018). Multi-sample adjusted U-statistics that account for confounding covariates. Statistics in Medicine 37(23):3357-3372.  DOI: 10.1002/sim.7825.
  10. Wu Y, Gaskins J, Kong M, Datta S (2018). Profilng the effects of short time-course cold ischemia on tumor protein phosphorylation using a Bayesian approach. Biometrics. 74(1):331-341. doi: 10.1111/biom.12742. [PMID: 28742267]
  11. Ghosal S, Trivedi J, Chen J, Rogers M, Cheng A, Slaughter MS, Kong M, Huang J (2017).  Regional cerebral oxygen saturation level predicts 30 day mortality rate after left ventricular assist device surgery. Journal of cardiothoracic and Vascular Anesthesia. S1053-0770(17)30711-5. PMID: 29158058.
  12. Li B, Hao J, Yan X, Kong M, Sauter ER (2017). A-FABP decreases in the wean milk of nursing women with a family history of breast cancer. International Journal of Women's Health and Wellness. 3(4), 3:063. DOI: 10.23937/2474-1353/151006.
  13. Abdia Y, Kulasekera KB, Datta S, Boakye M and Kong M (2017). Propensity scores based methods for estimating average treatment effect and average treatment effect among treated: a comparative study. Biometrical Journal 59(5):967-985. DOI: 10.1002/bimj.201600094  [PMID: 28436047]
  14. Wan Y, Datta S, Lee JJ, Kong M (2017). Monotonic single-index models to assess drug interactions. Statistics in Medicine 36: 655–670. [PMID: 27804146]
  15. Conklin DJ, Haberzettla P, Jagatheesan G, Kong M, and Hoyle GW (2017). Role of TRPA1 in acute cardiopulmonary toxicity of inhaled acrolein. Toxicology and applied pharmacology, 324: 61-72. [PMID: 27592100]
  16. Akhter S, Marcus M, Kerber RA, Kong M, Taylor KC (2016). The impact of periconceptional maternal stress on fecundability. Ann Epidemiol. 26(10):710-716. [PMID: 27623482]
  17. Chen J, Wang S, Luo M, Zhang Z, Dai X, Kong M, Cai L, Wang Y, Shi B, Tan Y. (2016). Zinc deficiency worsens and supplementation prevents high-fat diet induced vascular inflammation, oxidative stress, and pathological remodeling. Toxicological Sciences 153(1):124-136. [PMID: 27370414]
  18. Ma Z, Zhang YP, Liu W, Yan G, Li Y, Shields LB, Walker M, Chen K, Huang W, Kong M, Lu Y, Brommer B, Chen X, Xu X, Shields CB (2016). A controlled spinal cord contusion for the rhesus macaque monkey. Experimental Neurology 279:261-273. [PMID: 26875994]
  19. Wu H, Kong L, Tan Y, Epstein PN, Zeng J, Gu J, Liang G, Kong M, Chen X, Miao L, Cai L (2016). C66 ameliorates diabetic nephropathy in mice by both upregulating NRF2 function via increase in miR-200a and inhibiting miR-21. Diabetologia 59:1558–1568 [PMID: 27115417]
  20. Wan Y, Datta S, Conklin D, Kong M (2015). Variable selection models based on multiple imputation with an application for predicting median effective dose and maximum effect. Journal of Statistical Computation and Simulation 85(9), 1902-1916. [PMID: 26412909]
  21. Kong M, Xu S, Levy S, and Datta S (2015). GEE type inference for clustered zero-inflated negative binomial regression with application to dental caries. Computational Statistics and Data Analysis 85, 54-66. [PMID: 25620827]
  22. S Jones, X Tang, Y Guo,C Steenbergen, D Lefer, R Kukreja, M Kong, Q Li, S Bhushan, X Zhu, J Du, Y Nong, H Stowers, K Kondo, G Hunt, T Goodchild, A Orr, C Chang, R Ockaili, F Salloum, and R Bolli (2015). The NHLBI-sponsored Consortium for preclinicAl assESsment of cARdioprotective therapies (CAESAR): a new paradigm for rigorous, accurate, and reproducible evaluation of putative infarct-sparing interventions in mice, rabbits, and pigs. Circulation Research 116, 572-586. [PMID: 25499773]
  23. Conklin DJ, Kong M (2015). Assessment of plasma markers and cardiovascular responses in rats after chronic exposure to new-technology diesel exhaust in the ACES bioassay. HEI Health Review Committee. Res Rep Health Eff Inst. 184, 111-139. [PMID: 25842618]
  24. Sokhadze EM, Frederick J, Wang Y, Kong M, El-Baz AS, Tasman A, Casanova MF. Event-related potential (ERP) study of facial expression processing deficits in autism. Journal of Communications Research. 2015 Nov 1;7(4): 391-412.
  25. Ugiliweneza B, Kong M, Nosova K, Huang KT, Babu R, Lad SP, Boakye M (2014). Spinal surgery: variations in healthcare costs and implications for episode-based bundled payments. Spine 39(15):1235-1242. [PMID: 24831503]
  26. Zhang Z, Wang S, Zhou S, Yan X, Wang Y, Chen J, Mellen N, Kong M, Gu J, Tan Y, Zheng Y, Cai L (2014). Sulforaphane prevents the development of cardiomyopathy in type 2 diabetic mice probably by reversing oxidative stress-induced inhibition of LKB1/AMPK pathway. Journal of Molecular and Cellular Cardiology 77:42-52. [PMID: 25268649]
  27. Bavle A, Raj A, Kong M, Bertolone S (2014). Impact of long-term erythrocytapheresis on growth and peak height velocity of children with sickle cell disease. Pediatric Blood & Cancer 61(11), 2024-30. [PMID: 25111886]
  28. Xia Y, Zhou S, Zheng Y, Tan Y, Kong M, Wang B, Feng W, Epstein PN, Cai J, and Cai L (2014). Metallothionein as a compensatory component prevents intermittent hypoxia-induced cardiomyopathy in mice. Toxicology and applied pharmacology 277(1): 58-66. [PMID: 24657099]
  29. Crutcher II C, Ugiliweneza B, Hodes JE, Kong M, Boayke M (2014). Alcohol intoxication and its effects on traumatic spinal cord injury outcomes. Journal of Neurotrauma 31(9): 798-802. [PMID: 24617326]
  30. Litvan I, Kong M (2014). Rate of decline in progressive supranuclear palsy. Movement Disorders 29(4):463-468. [PMID: 24615741]
  31. Wei X, Shi X, Kim S, Patrick JF, Binkley J, Kong M, McClain C, Zhang X (2014). Data dependent peak model-based spectrum deconvolution for analysis of LC-MS data. Analytical Chemistry 86, 2156−2165. [PMID: 24533635]
  32. Kong M, Rai S, Bolli R (2014). Statistical methods for selecting maximum effective dose and evaluating treatment effect when dose - response is monotonic. Statistics in Biopharmaceutical Research6 (1), 16-29. [PMID: 25067994]
  33. Mansfield K, Meyer K, Ugiliweneza B, Kong M, Nosova  K, and Boakye M (2014). Traumatic spinal cord injury with concomitant brain injury: in-hospital complication rates and resource utilization. JSM Neurosurgery and Spine 2(2): 1017.
  34. Lad SP, Babu R, Baker AA, Ugiliweneza B, Kong M, Bagley CA, Gottfried ON, Isaacs RE, Patil CG, Boakye M (2013). Complications, reoperation rates, and health-care cost following surgical treatment of lumbar spondylolisthesis. The Journal of Bone & Joint Surgery, 95(21), e162:1-10. [PMID: 24196474]
  35. Deel MD, Kong M, Cross KP, Bertolone SJ (2013). Absolute lymphoctye counts as prognostic indicators for immune thrombocytopenia outcomes in children. Pediatric blood & cancer 60 (12), 1967-1974. [PMID: 24038723]
  36. Lad SP, Bagley JH, Karikari IO, Babu R, Ugiliweneza B, Isaacs R, Bagley CA, Gottfried ON, Kong M, Patil CG, Boakye M (2013). Cancer after spinal fusion: the role of bone morphogenetic protein. Neurosurgery, 73(3), 440-449. [PMID: 23756740]
  37. Khalifa F, Beache GM, El-Ghar MA, El-Diasty T, Gimel’farb G, Kong M, El-Baz A (2013). Dynamic contrast-enhanced MRI-based early detection of acute renal transplant rejection. IEEE Trans Med Imaging 32(10), 1910-1927. [PMID: 23797240]
  38. Jang H, Conklin DJ, Kong M (2013).  Piecewise nonlinear mixed-effects models for modeling cardiac function and assessing treatment effects. Computer Methods and Programs in Biomedicine 110, 240-252. [PMID 23253450]
  39. Boakye M, Moore R, Kong M, Skirboll SL, & Arrigo RT (2013). Health-related quality-of-life status in Veterans with spinal disorders. Quality of Life Research 22(1), 45-52. [PMID:22311250]
  40. Bull-Otterson L, Feng W, Kirpich I, Wang Y, Qin X, Liu Y, Gobejishvili L, Joshi-Barve S, Ayvaz T, Petrosino J, Kong M, Barker D, McClain C,  Barve S (2013). Metagenomic Analyses of Alcohol Induced Pathogenic Alterations in the Intestinal Microbiome and the Effect of Lactobacillus rhamnosus GG Treatment. PLoS One 8(1):e53028. [PMID 23326376]
  41. Lad SP, Bagley JH, Kenney KT, Ugiliweneza B, Kong M, Bagley CA, Gottfried ON, Isaacs RE,  Patil CG, Boakye M (2013). Racial disparities in outcomes of spinal surgery for lumbar stenosis. Spine 38(11): 927–935. [PMID 23232216]
  42. Becker LE, Lu Z, Chen W, Xiong W, Kong M, Li Y (2012).  A systematic screen reveals microRNA clusters that significantly regulate four major signaling pathways. Public Library of Science (PLoS) One 7(11):e48474. [PMID 23144891, PMCID PMC3493556]
  43. Conklin DJ, Kong M (2012). Part 4. Effects of subchronic diesel engine emissions exposure on plasma markers in rodents: report on 1- and 3-month exposures in the ACES bioassay. HEI Health Review Committee. Research Report Health Effects Institute 166:189-223. [PMID: 23156843]
  44. Smith MJ, Kong M, Cambon A, Woods CR (2012). Effectiveness of antimicrobial guidelines for community-acquired pneumonia in children. Pediatrics 129(5),  e1326 -e1333 [doi: 10.1542/peds.2011-2412] [PMID:22492769]
  45. Das B, Raj A, Recto M, Kong M, Bertolone S (2012). Utility of impedance cardiography for the detection of hemodynamic changes in stable patients with sickle cell disease. Journal of Pediatric Hematology and Oncology 34(5):336-339. [PMID:22713705]
  46. Kong M, Cambon A, Smith MJ (2012). Extended logistic regression model for studies with interrupted events, seasonal trends, and serial correlation. Communications in Statistics – Theory and Methods41(19), 3528-3543.
  47. Kong M, Yan J (2011). Modeling and testing treated tumor growth using cubic smoothing splines. Biometrical Journal 53, 595-613. [PMID 21604288]
  48. Li C, Kong M, Wu D (2011). The statistical effects of measuring myocytes with different image zoom rates. Open Access Medical Statistics 1, 3-12.
  49. Wang M, Kong M, Datta S (2011). Inference for marginal linear models with clustered longitudinal data for potentially informative cluster sizes. Statistical Methods in Medical Research20, 347-367. [PMID: 20223781]
  50. Godfrey GJ, Lockwood W, Kong M,Bertolone S, Raj A (2010). Antibody development in pediatric Sickle Cell patients undergoing erythrocytapheresis: an 11-year review. Pediatric Blood & Cancer 55(6), 1134-1137. [PMID: 20979172]
  51. Fujimoto J, Kong M, Lee JJ, Hong WK, and Lotan R (2010). Validation of a novel statistical model for assessing the synergy of combined agents in cancer chemoprevention. Cancer Prevention Research 3, 917-928. (co-first author; front cover) [PMID: 20663979]
  52. Kong M, Zhang M, Gopalakrishnan V, and Wolff J (2010). Dose-time-effect modeling for in vitro experiments. Statistics in Biopharmaceutical Research 2(1), 84-96. [doi:10.1198/sbr.2009.0050]
  53. Lee JJ, Lin HY, Liu DD, and Kong M (2010). Applying Emax model and interaction index for assessing drug interaction in combination studies. Frontiers in Bioscience (E2), 582-601. [PMID: 20036904; PMCID: PMC2974574]
  54. Kong M, Lee JJ (2010). Applying Emax model and bivariate thin plate splines to assessing drug interactions. Frontiers in Bioscience (E2), 279-292. [PMID: 20036878]
  55. Kajiwara Y, Panchabhai S, Liu DD, Kong M, Lee JJ, and Levin VA (2009). Melding a new 3-dimensional agarose colony assay with the Emax model to determine the effects of drug combinations on cancer cells. Technology in Cancer Research & Treatment 8(2), 163-172. [PMID: 19334798]
  56. Lee JJ, Kong M (2009). Confidence intervals of interaction index for assessing multiple drug interaction. Statistics in Biopharmaceutical Research 1, 4-17. [PMID: 2003766; PMCID: PMC2796809]
  57. Kong M, Lee JJ (2008). A semiparametric model for assessing drug interaction. Biometrics 64, 396-405. [PMID: 17900314]
  58. Salvador C, Li B, Hansen R, Cramer DE, Kong M, Yan J (2008). Yeast-derived b-Glucan augments the therapeutic efficacy mediated by anti-vascular Endothelial Growth Factor monoclonal antibody in human carcinoma xenograft models. Clinical Cancer Research 14, 1239-1247. [PMID: 18281559; PMCID: PMC2394864]
  59. Lee JJ, Kong M, Ayers GD, Lotan R (2007). Interaction index and different methods for determining drug interaction in combination therapy. Journal of Biopharmaceutical Statistics 17, 461-480. [PMID: 17479394]
  60. Bhattacharya R, Kong M (2007). Consistency and asymptotic normal property of estimated effective doses in bioassay. Journal of Statistical Planning and Inference 137, 643-658. [doi:10.1016/j.jspi.2006.06.027]
  61. Kong M, Lee JJ (2006). A generalized response surface model with varying relative potency for assessing drug interaction.  Biometrics 62 (4), 986–995. [PMID: 17156272]
  62. Kong M, Eubank R (2006). Monotone smoothing with application to dose-response curves. Communication in Statistics—Simulation and Computation 35, 991-1004.[doi: 10.1080/03610910600880492]
  63. Kong M, Bhattacharya R, James C, Basu A (2005). A statistical approach to estimate the 3D size distribution of spheres from 2D size distributions. Geological Society of America Bulletin 117, 244-249. [doi: 10.1130/B25000.1]

Letters to Editor

  1. Lee JJ, Kong M (2011). Combined treatment of pancreatic cancer with mithramycin A and tolfenamic acid promotes Sp1 degradation and synergistic antitumor activity-Response. Cancer Research 71, 2794-2795. [PMID: 21447746]
  2. Lee JJ, Kong M (2011). Rebuttal to the Response of Chou. Cancer Research 71, 2798-2800. [PMID: 21447741]

Book Chapter

  1. Kong M, Lee JJ (2015). Confidence Interval for Interaction Index. In Zhao W and Yang H (Eds), Statistical Methods in Drug Combination Studies (pp. 55-72). Boca Raton, FL: Chapman & Hall/CRC.


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