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Personal profile

Research interests

The research of Dr. Ming Wang focuses on the development of statistical methods for longitudinal data analysis, survival data analysis, spatial statistics, high-dimensional data, risk prediction and model diagnosis.

These statistical methods have been implemented into a range of biomedical problems, such as biomarker studies, outcome analysis, genetic expression data and environmental research.

By applying these methods for rigorous analysis, Dr. Wang's research team closely collaborate with clinicians/physicians to better understand the biomedical mechanisms that underlie various human diseases, including cancer, kidney, cardiovascular and neurodegenerative disease, among others. 

Fingerprint Dive into the research topics where Ming Wang is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

  • 2 Similar Profiles
Generalized Estimating Equations Mathematics
Craniocerebral Trauma Medicine & Life Sciences
Pediatrics Medicine & Life Sciences
Peripheral Arterial Disease Medicine & Life Sciences
Recurrent Events Mathematics
Longitudinal Data Mathematics
Small Sample Mathematics
Acute Myeloid Leukemia Medicine & Life Sciences

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2011 2020

  • 262 Citations
  • 9 h-Index
  • 52 Article
  • 3 Letter
Copula Models
Recurrent Events
Hierarchical Model
Myocardial Infarction

Temporal and spatial trends and determinants of aggressive prostate cancer among Black and White men with prostate cancer

Wang, M., Chi, G., Bodovski, Y., Holder, S. L., Lengerich, E. J., Wasserman, E. & McDonald, A. C., Jan 1 2020, In : Cancer Causes and Control. 31, 1, p. 63-71 9 p.

Research output: Contribution to journalArticle

Prostatic Neoplasms
Neoplasm Grading
1 Citation (Scopus)

A Bayesian joint model of recurrent events and a terminal event

Li, Z., Chinchilli, V. M. & Wang, M., Jan 2019, In : Biometrical Journal. 61, 1, p. 187-202 16 p.

Research output: Contribution to journalArticle

Recurrent Events
Joint Model
Bayesian Model
Frailty Model

An R package for model fitting, model selection and the simulation for longitudinal data with dropout missingness

Xu, C., Li, Z., Xue, Y., Zhang, L. & Wang, M., Oct 21 2019, In : Communications in Statistics: Simulation and Computation. 48, 9, p. 2812-2829 18 p.

Research output: Contribution to journalArticle

Drop out
Model Fitting
Longitudinal Data
Model Selection
Weighted Estimating Equations

Assessing predictive accuracy of survival regressions subject to nonindependent censoring

Wang, M., Long, Q., Chen, C. & Zhang, L., Jan 1 2019, (Accepted/In press) In : Statistics in Medicine. 39, 4, p. 469-480 12 p.

Research output: Contribution to journalArticle

Survival Time