Alexander Tsodikov, PhD
- Professor, Biostatistics
Alex Tsodikov received his PhD in Applied Mathematics in 1991 from St. Petersburg
State Technical University, Russia. Prior to joining University of Michigan, he has
been a Postdoctoral Scholar at the Curie Institute (Paris, France), a staff statistician
at the University of Leipzig (Germany), Research Assistant/Associate Professor at
the University of Utah and Associate Professor/Professor at the University of California,
Davis. Dr. Tsodikov's research interests are in various areas of biostatistics and
biomathematics, including failure time and survival analysis models, cure models,
semiparametric inference, stochastic models, optimal control, inference algorithms
based on self-consistency.
2011-present: Elected Member, International Statistical Institute
American Statistical Association
International Biometric Society
- PhD, Mathematics/Application of Computing, Mathematical Models and Methods in Natural Sciences, St. Petersburg State Technical University, 1991
- M.Sc., Applied Mathematics, St. Petersburg State Technical University, 1988
My research interests have mainly been evolving around cancer research. My recent methodological interest has centered on the idea of artificial mixture or frailty models and its self-consistency generalization as a tool to derive computationally efficient inference procedures for a wide variety of statistical models. Much of this methodology was initially developed for so-called semiparametric cure models that incorporate improper distributions showing a tail defect. I'm working on a project funded by the National Cancer Institute applying these methods to build a comprehensive model of the dynamics of the national incidence and mortality trends in prostate cancer in the presence of variable utilization of screening. I am also interested in multivariate semiparametric survival models, age-period-cohort models, categorical data analysis and computational approaches to statistical inference such as EM and MM algorithms. I also have a broad ongoing statistical consulting experience in basic and clinical research as well as epidemiology and population sciences
Tran, Q., Kidwell, K., Tsodikov, A. (2018) A Joint Model of Cancer Incidence, Metastasis, and Mortality, Lifetime Data Analysis, 24/3, 385-406. ( Tsodikov's PhD student)
Rice, J., Tsodikov, A. (2017) Semiparametric Time-to-Event Modeling in the Presence of a Latent Progression Event, Biometrics, 73/2, 463-472. PMCID: PMC5325816. ( Tsodikov's PhD student)
Ha, J. and Tsodikov, A. (2015) Semiparametric Estimation in the Proportional Hazard Model Accounting for a Misclassified Cause of Failure, Biometrics, 71/4, 941-9, PMCID: PMC4689683. ( Tsodikov's PhD student)
Hu, C., Tsodikov, A. (2014) Semiparametric Regression Analysis for Time-to-Event Marked Endpoints in Cancer Studies, Biostatistics, 15/3, 513-25, PMCID:PMC4102917. ( Tsodikov's PhD student)
Tsodikov, A., Chefo, S. (2008) Generalized Self-Consistency: Multinomial logit model and Poisson likelihood, Journal of Statistical Planning and Inference, 138, 2380-2397, PMCID: PMC2516948.
Tsodikov, A., Szabo, A., and Wegelin, J. (2006) A population model of prostate cancer incidence, Statistics in Medicine, 25, 2846-2866.
Tsodikov, A., Ibrahim, J.G., and Yakovlev, A.Y. (2003) Estimating Cure Rates from Survival Data: An Alternative to Two-Component Mixture Models, Journal of the American Statistical Association, 98, 1063-1078.
Tsodikov, A. (2003) Semiparametric models: A generalized self-consistency approach, Journal of the Royal Statistical Society, Series B, 65, 759-774.
Tsodikov, A., Szabo, A., Jones, D. (2002) Adjustments and tests for differential expression with microarray data, Bioinformatics, 18, 251-260.
Tsodikov, A.D. and W. Muller, (1998) Modeling carcinogenesis under a time-changing exposure, Mathematical Biosciences, 152, 179-191.
Tsodikov, A.D., (1998) A proportional hazards model taking account of long-term survivors, Biometrics, 54, 1508-1516.
Tsodikov A., Asselain B., Fourquet A., Hoang T., Yakovlev A. (1995) Discrete strategies of cancer post treatment surveillance. Estimation and optimization problems, Biometrics, 51, 437-447.
Areas of Expertise: Biostatistics