Chengcheng Hu
Director, Biostatistics - Phoenix Campus
Professor, BIO5 Institute
Professor, Public Health
Professor, Statistics-GIDP
Primary Department
Department Affiliations
(520) 626-9308
Work Summary
Chengcheng Hu has worked on a broad range of areas including cancer, occupational health, HIV/AIDS, and aging. He has extensive collaborative research in conducting methodological research in the areas of survival analysis, longitudinal data, high-dimensional data, and measurement error. His current methodological interest, arising from studies of viral and human genetics and biomarkers, is to develop innovative methods to investigate the relationship between high-dimensional information and longitudinal outcomes or survival endpoints.
Research Interest
Chengcheng Hu, Ph.D., is an Associate Professor, Public Health and Director, Biostatistics, Phoenix campus at the Mel and Enid Zuckerman College of Public Health, University of Arizona. He is also Director of the Biometry Core on the Chemoprevention of Skin Cancer Project at the University of Arizona Cancer Center. Hu has worked on multiple federal grants in a broad range of areas including cancer, occupational health, HIV/AIDS, and aging. In addition to extensive experience in collaborative research, he has conducted methodological research in the areas of survival analysis, longitudinal data, high-dimensional data, and measurement error. His current methodological interest, arising from studies of viral and human genetics and biomarkers, is to develop innovative methods to investigate the relationship between high-dimensional information and longitudinal outcomes or survival endpoints. Hu joined the UA Mel and Enid Zuckerman College of Public Health in 2008. Prior to this he was an assistant professor of Biostatistics at the Harvard School of Public Health from 2002 to 2008. While at Harvard, he also served as senior statistician in the Pediatric AIDS Clinical Trials Group (PACTG) and the International Maternal Pediatric Adolescent AIDS Clinical Trials Group (IMPAACT). Hu received his Ph.D. and M.S. in Biostatistics from the University of Washington and a M.A. in Mathematics from the Johns Hopkins University.

Publications

Vasquez, M. M., Hu, C., Roe, D. J., Chen, Z., Halonen, M., & Guerra, S. (2016). Least absolute shrinkage and selection operator type methods for the identification of serum biomarkers of overweight and obesity: simulation and application. BMC medical research methodology, 16(1), 154.
BIO5 Collaborators
Zhao Chen, Stefano Guerra, Chengcheng Hu

The study of circulating biomarkers and their association with disease outcomes has become progressively complex due to advances in the measurement of these biomarkers through multiplex technologies. The Least Absolute Shrinkage and Selection Operator (LASSO) is a data analysis method that may be utilized for biomarker selection in these high dimensional data. However, it is unclear which LASSO-type method is preferable when considering data scenarios that may be present in serum biomarker research, such as high correlation between biomarkers, weak associations with the outcome, and sparse number of true signals. The goal of this study was to compare the LASSO to five LASSO-type methods given these scenarios.

Huang, S., Chengcheng, H., Bell, M., Billheimer, D., Guerra, S., Roe, D., Monica, V., & Bedrick, E. (2018). Regularized Continuous-Time Markov Model via Elastic Net. Biometrics.
BIO5 Collaborators
Dean Billheimer, Stefano Guerra, Chengcheng Hu
Vasquez, M. M., Hu, C., Roe, D. J., Halonen, M., & Guerra, S. (2017). Measurement error correction in the least absolute shrinkage and selection operator model when validation data are available. Statistical methods in medical research, 962280217734241.
BIO5 Collaborators
Stefano Guerra, Chengcheng Hu

Measurement of serum biomarkers by multiplex assays may be more variable as compared to single biomarker assays. Measurement error in these data may bias parameter estimates in regression analysis, which could mask true associations of serum biomarkers with an outcome. The Least Absolute Shrinkage and Selection Operator (LASSO) can be used for variable selection in these high-dimensional data. Furthermore, when the distribution of measurement error is assumed to be known or estimated with replication data, a simple measurement error correction method can be applied to the LASSO method. However, in practice the distribution of the measurement error is unknown and is expensive to estimate through replication both in monetary cost and need for greater amount of sample which is often limited in quantity. We adapt an existing bias correction approach by estimating the measurement error using validation data in which a subset of serum biomarkers are re-measured on a random subset of the study sample. We evaluate this method using simulated data and data from the Tucson Epidemiological Study of Airway Obstructive Disease (TESAOD). We show that the bias in parameter estimation is reduced and variable selection is improved.

Klimentidis, Y. C., Bea, J. W., Thompson, P., Klimecki, W. T., Hu, C., Wu, G., Nicholas, S., Ryckman, K. K., & Chen, Z. (2016). Genetic Variant in ACVR2B Is Associated with Lean Mass. Medicine and science in sports and exercise.
BIO5 Collaborators
Zhao Chen, Chengcheng Hu, Walter Klimecki, Yann C Klimentidis

Low lean mass (LM) is a risk factor for chronic disease, a major cause of disability and diminished quality of life, and is a heritable trait. However, relatively few specific genetic factors have been identified as potentially influencing this trait.

Vasquez, M., others, ., & Guerra, S. (2016). Low lung function in young adult life is associated with early mortality. The American Journal of Respiratory and Critical Care Medicine.
BIO5 Collaborators
Stefano Guerra, Chengcheng Hu