SPHIS Home » Directory » Dongfeng Wu, PhD
Dongfeng Wu, PhD
Bioinformatics and Biostatistics
Professor (Tenured)
Room No. 131, 485 E. Gray St.
Louisville, KY 40202
Phone: 502-852-1888
Fax: 502-852-3294
dongfeng.wu@louisville.edu
Education:
B.S. in Probability and Statistics, Peking University, P.R. China. 1986-1990.
M.S. in Probability and Statistics, Peking University, P.R. China. 1990-1993.
M.S. in Computer Science, University of California, Santa Barbara. 1997-1999.
Ph.D. in Statistics, University of California, Santa Barbara. 1995-1999.
Positions and Employment:
07/2021-Present. Professor with tenure, Dept. of Bioinformatics and Biostatistics, University of Louisville.
09/2007-06/2021. Associate Professor, Dept. of Bioinformatics and Biostatistics, Univ. of Louisville. Tenured 2014.
08/2001-08/2007. Assistant Professor, Department of Math. & Stat., Mississippi State Univ.
Tenured and promoted to associate professor in 05/2007.
01/2000-08/2001. Research Associate, Department of Biostat., Univ. of Texas, M.D. Anderson Cancer Center. Houston, TX.
Research:
My main research area is in probability modeling and statistical inferences in periodic cancer screening. Besides that, I am interested in Bayesian inference, statistical decision theory, time series, smoothing spline, wavelet regression and all kinds of statistical problems in medical research.
My book is recently published by Chapman and Hall/CRC:
https://www.taylorfrancis.com/books/mono/10.1201/9781003404125/probability-modeling-statistical-inference-cancer-screening-dongfeng-wu
Completed Research Support:
NIH/NCI R15CA242482 (Wu, D.) 9/1/2019-8/31/2023. $435,309.
Project Title: Dynamic scheduling of the upcoming screening exam based on screening history and other parameters.
Description: The project is to develop statistical methods and software that can be used to make informed decisions regarding future scheduling of screening exams for people with a history of negative screening results. Using eight cohorts from mass lung cancer screening trials (the National Lung Screening Trials, and the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial for lung cancer part), the developed methods can be applied to other kinds of screening for chronic diseases. The specific aims are: 1). to estimate the three key parameters in the eight cohorts. 2). to schedule the next screening exam dynamically for asymptomatic people with any screening history; and 3). To develop a user-friendly software for Aims 2.
Selected Publications:
Wu D (2022). When to initiate cancer screening exam? Statistics and Its Interface.15(4), 503-514.
Wu D, Rai SN, and Seow A. (2022). Estimation of preclinical state onset age and sojourn time for heavy smokers in lung cancer. Statistics and Its Interface. 15(3), 349-358. NIHMS ID: 1734205.
Liu R, Wu D, and Rai SN. (2021). Estimation of the lead time distribution for individuals with screening history. Statistics and Its Interface. 14(2), 131–149.
Wu D and Kim S. (2020). Problems in the estimation of the key parameters using MLE in lung cancer screening. Journal of Clinical Research and Reports. 5(3). DOI:10.31579/2690-1919/117.
Liu R, Wu D, and Rai SN. (2020). Estimation of the lead time distribution for individuals with screening history. Statistics and Its Interface. 14. (2021) 131–149.
Wu D (2019). Scheduling mammogram and physical exam for a healthy woman. Annals of Women’s Health. 2019; 3(1): 1016.
Liu R, Perez A, Wu D. (2018). The lead time distribution in the National Lung Screening Trial study. Journal of Healthcare Informatics Research. (2018) 2:353–366. https://doi.org/10.1007/s41666-018-0027-8
Wu D, Kafadar K, and Rai SN. (2018). Inference of long-term screening outcomes for individuals with screening histories. Statistics and Public Policy, 5:1, 1-10. https://dio.org/10.1080/2330443X.2018.1438939.
Liu R, Gaskin JT, Mitra R, and Wu D. (2017). A review of estimation of key parameters and lead time in cancer screening. Revista Colombiana de Estadistica (Colombian Journal of Statistics). Vol. 40. Issue 2. 263-278. DOI: http://dx.doi.org/10.15446/rce.v40n2.60642.
Wang D, Levitt B, Riley T, and Wu D. (2017). Estimation of sojourn time and transition probability of lung cancer for smokers using the PLCO data. Journal of Biometrics and Biostatistics. 8: 360. doi: 10.4172/2155-6180.1000360.
Wu D, Liu R, Levitt B, Riley T, Baumgartner, KB (2016). Evaluating long-term outcomes via computed tomography in lung cancer screening. Journal of Biometrics and Biostatistics. 7:313. doi:10.4172/2155-6180.1000313.
Kim S, and Wu D (2016). Estimation of sensitivity depending on sojourn time and time spent in preclinical state. Statistical Methods in Medical Research. 2016,Vol. 25(2), 728-740. DOI: 10.1177/0962280212465499.
Liu R, Levitt B, Riley T, Wu D. (2015). Bayesian estimation of the three key parameters in CT for the National Lung Screening Trial data. Journal of Biometrics and Biostatistics. 6: 263. doi:10.4172/2155-6180.1000263
Kim S, Jang H, Wu D, Abrams J. (2015) A Bayesian nonlinear mixed-effects disease progression model. Journal of Biometrics and Biostatistics. 6:271. doi:10.4172/2155-6180.1000271.
Kendrick SK, Rai SN and Wu D. (2015). Simulation study for the sensitivity and mean sojourn time specific lead time in cancer screening when human lifetime is a competing risk. Journal of Biometrics and Biostatistics. 6:247. DOI:10.4172/2155-6180.1000247.
Wu D, Kafadar K, and Rosner GL. (2014). Inference of long term effects and over-diagnosis in periodic cancer screening. Statistica Sinica. 2014; 24: 815-831.
Chen Y, Erwin D, and Wu D. (2014). Over-diagnosis in lung cancer screening using the MSKC-LCSPdata. Journal of Biometrics and Biostatistics. 5:201. DOI: 10.4172/2155-6180.1000201.
Jang H, Kim S, and Wu D. (2013). Bayesian lead time calculation for the Johns Hopkins lung project data. Journal of Epidemiology and Global Health. Vol. 3, 157-173.
Kim S, Erwin D, and Wu D. (2012). Efficacy of dual lung cancer screening by chest x-ray and sputum cytology using Johns Hopkins lung project data. Journal of Biometrics and Biostatistics. 3:139. doi:10.4172/2155-6180.1000139
Wu D, Kafadar, K, Rosner GL, Broemeling LD. (2012). The lead time distribution when lifetime is subject to competing risks in cancer screening. The International Journal of Biostatistics. Volume 8: Issue 1, Article 6, ISSN: 1557-4679, DOI:10.1515/1557-4679.1363, April 2012.
Luo D, Cambon AC, Wu D. (2012). Evaluating long term effect of FOBT in colon cancer screening. Cancer Epidemiology. 36 (2012), e54-e60. DOI: 10.1016/j.canep.2011.09.011.
Wu D and Perez A. (2012). Chapter 24: Modeling and inference in screening: exemplification with the faecal occult blood test. Colorectal Cancer-From Prevention to Patient Care, Editor: Ettarh, R. (eds.) InTech Publisher. ISBN: 978-953-51-0028-7. February 2012.
Wu D, Erwin D, and Kim S. (2011). Projection of long-term outcomes using x-rays and pooled cytology in lung cancer screening. Open Access Medical Statistics. 2011: 1 13-19. DOI:10.2147/OAMS. S22987.
Wu D and Perez A. (2011). A limited review of over-diagnosis methods and long term effects in breast cancer screening. Oncology Reviews (2011) 5:143-147.
Shows J and Wu D. (2011) Inferences for the lead time in breast cancer screening trials under a stable disease model. Cancers. 2011, 3(2), 2131-2140; DOI:10.3390/cancers3022131.
Wu D, Erwin D. and Rosner GL. (2011). Sojourn time and lead time projection in lung cancer screening. Lung Cancer. 72 (2011) 322-326.
Wu D and Rosner GL. (2010). Chapter 10: Probability modeling and statistical inference in periodic cancer screening. Frontiers in Computational and System Biology, Editors: J. Feng et al (eds.) Computational Biology 15. Springer-Verlag London, 2010. ISBN: 978-1-84996-195-0. June 2010.
Chen Y, Brock G, and Wu, D. (2010). Estimating key parameters in periodic breast cancer screening - application to the Canadian National Breast Screening Study data. Cancer Epidemiology. 34, 429-433. DOI:10.1016/j.canep.2010.04.001.
Wu D, Erwin D, and Rosner GL. (2009). A projection of benefits due to fecal occult blood test for colorectal cancer. Cancer Epidemiology. 33, 212-215. DOI: 10.1016/j.canep.2009.08.001.
Wu D, Erwin D. and Rosner GL. (2009) Estimating key parameters in FOBT screening for colorectal cancer. Cancer Causes and Control (2009) 20: 41-46.
Wu D, Carino RL, and Wu X. (2008). When sensitivity is a function of age and time spent in the preclinical state in periodic cancer screening. Journal of Modern Applied Statistical Methods. Vol. 7, No. 1, 297-303.
Wu D, Rosner GL, and Broemeling LD. (2007). Bayesian inference for the lead time in periodic cancer screening. Biometrics. Vol. 63, No. 3, 873–880.
Wu D, Rosner GL, and Broemeling LD. (2005). MLE and Bayesian inference of age-dependent sensitivity and transition probability in periodic screening. Biometrics. Vol.61, No.4, 1056-1063.
Teaching at University of Louisville (2007-Present):
2021-22: Fall: PHST624-Clinical Trial I (online). Spring:PHST691-Bayesian Inference. PHST625-Clinical Trial II (online).
2020-21: Fall: *PHST440-Statistics Study Design and Research (only 1 student registered and dropped after a few weeks and the class was closed early). Spring: PHST691-Bayesian Inference. PHST302-Intermediate Statistical Analysis. Summer: PHST563-Math Tools III.
2019-20:Fall: *PHST440-Statistics Study Design and Research (only 1 student registered and dropped after a few weeks and the class was closed early). Spring: PHST691-Bayesian Inference. Summer: PHST563-Math Tools III.
2018-19: Fall: PHST683-Survival Analysis. Spring: PHST691-Bayesian Inference.PHST724-Advanced Clinical Trials.
2017-18: Fall: PHST683-Survival Analysis. Spring: PHST691-Bayesian Inference.
2016-17: Fall: PHST682-Multivariate Statistical Analysis; PHST 683-Survival Analysis. Spring: PHST724-Advanced Clinical Trials (joint w/ Dr. Rai); PHST691-Bayesian Inference.
2015-16:Fall: PHST683-Survival Analysis; PHST680-Biostat. Method I (joint w/ Dr. Rai); Spring: PHST662-Math. Stat; Summer: PHST671-Special Topics in Biostat: Probability Model and Stat. Inference in Cancer Screening.
2014-15: Fall: PHST683-Survival Analysis; PHST680-Biostat. Method I (joint w/ Dr. Rai); Spring: PHST724-Advanced Clinical Trials (joint w/ Dr. Rai); PHST662-Math. Stat.
2013-14: Fall: PHST683-Survival Analysis; Spring: PHST662-Math. Stat.; Summer: PHST671-Special Topics in Biostat: Probability Model and Stat. Inference in Cancer Screening.
2012-13: Fall: PHST661-Probability; Spring: PHST691-Bayesian Inference; PHST662-Math. Stat.; Summer: PHST671-Special Topics in Biostat: Probability Model and Stat. Inference in Cancer Screening.
Ph.D./MS Students at University of Louisville (2007-Present):
Farhin Rahman, Ph.D. Estimation of the key parameters and over-diagnosis estimation in cancer screening. Expected August 2022.
Ruiqi Liu, Ph.D. Estimation of the three key parameters and lead time distribution in lung cancer screening. August 2017.
Xiaohong Li Ph.D. (co-advisor with Dr. Shesh N. Rai). Sample size calculations and normalization methods for analysis of RNA-seq Data. December 2017.
Dengzhi Wang, MS. Estimation of sojourn time and transition probability of lung cancer for smokers using the PLCO data. Dec. 2016.
Jiying Ling, MS. Evaluating nutrition and physical activity trend in middle school students at four underserved regions in Kentucky. May 2013.
Sarah K Kendrick, MS. Simulation study for the lead time in cancer screening when human lifetime is a competing risk. May 2013.
Vikranth Shetty, MS. (w/ Dr. Rai). Statistical methods to find miRNA related to aging in mice. August 2012.
Dianhong Luo, MS. The trend and disparities in the diagnosis of breast cancer by mobile mammography at a comprehensive cancer center. August 2012.
Xinyuan Duan, MS. Evaluating Fiberoptic Intubation Simulator Training in Reaching Proficiency of Skills. December 2010.
Chengxin Li, MS. The statistical effects on measuring myocyte with different image zoom rates. August 2010.
Yinlu Chen, MS. Breast Cancer Screening Model – Application to the Canadian Study. August 2009.
