Associate Professor, Department of Mathematical and Statistical Sciences
Currently, I am an Associate Professor in the School of Mathematical and Statistical Sciences at Clemson University. I completed my Bachelor’s degree majoring in mathematics, minoring in physics, at Austin Peay State University, Clarksville, TN. I then earned a master’s degree in mathematics at Western Kentucky University, Bowling Green, Kentucky. I then completed a doctoral degree in statistics at the University of South Carolina, Columbia, SC. After completing my Ph.D., I joined Clemson University as an assistant professor, and I was recently promoted to the rank of associate. In addition to my teaching responsibilities, I also consult with Biorealm and serve as a Visiting Professor at BINUS University.
My research interests include, but are not limited to, categorical data analysis, group testing, survival data analysis, nonparametric methods, measurement error models, spatio-temporal modeling, statistical computing, Bayesian parametric/nonparametric estimation, high dimensional regression techniques, epidemiology/public health, statistical genetics, machine learning, and biomedical applications.
Gettings JR, Self SCW, McMahan CS, Brown DA, Nordone SK and Yabsley MJ. 2020. Local and regional temporal trends (2013-2019) of canine Ehrlichia spp. seroprevalence in the USA. Parasites and Vectors 13: 153. doi: 10.1186/s13071-020-04022-4.
Gettings JR, Self SCW, McMahan CS, Brown DA, Nordone SK and Yabsley MJ. 2020. Regional and local temporal trends of Borrelia burgdorferi and Anaplasma spp. seroprevalence in domestic dogs: Contiguous United States 2013-2019. Front. Vet. Sci. 7: 561592. doi: 10.3389/fvets.2020.561592.
Hou P, Tebbs J, Wang D, McMahan C and Bilder C. 2020. Array testing with multiplex assays. Biostatistics 21: 417-431. doi: 10.1093/biostatistics/kxy058.
Joyner C, McMahan C, Baurley J and Pardamean B. 2020. A two-phase Bayesian methodology for the analysis of binary phenotypes in genome-wide association studies. Biom J 62: 191-201. doi: 10.1002/bimj.201900050
Joyner CN, McMahan CS, Tebbs JM and Bilder CR. From mixed effects modeling to spike and slab variable selection: A Bayesian regression model for group testing data. Biometrics 76: 913-923. doi: 10.1111/biom.13176.
Sekhon R, Joyner C, Ackerman A, McMahan C, Cook D and Robertson D. 2020. Stalk bending strength is strongly associated with maize stalk lodging incidence across multiple Environments. Field Crops 249: e107737. doi: 10.1016/j.fcr.2020.107737.
Withana Gamage PW, Chaudari M, McMahan CS, Kim EH and Kosorok MR. 2020. An extended proportional hazards model for interval-censored data subject to instantaneous failures. Lifetime Data Anal 26: 158-182. doi: 10.1007/s10985-019-09467-z.
Bilder C, Tebbs J and McMahan C. 2019. Informative group testing for multiplex assays. Biometrics 75: 278-288. doi: 10.1111/biom.12988.
Gregory K, Wang D and McMahan C. 2019. Adaptive elastic net regression with group testing data. Biometrics 75: 13-23. doi: 10.1111/biom.12973.
Self SCW, Liu Y, Nordone SK, Yabsley MJ, Walden HS, Lund RB, Bowman DD, Carpenter C, McMahan CS and Gettings JR. 2019. Canine vector-borne disease: mapping and the accuracy of forecasting using big data from the veterinary community. Anim Health Res Rev 20: 47-60. doi: 10.1017/S1466252319000045.
Yang T, Gallagher CM and McMahan CS. 2019. A robust regression methodology via M-estimation. Communications in Statistics 48: 1092-1107. doi: 10.1080/03610926.2018.1423698.
Withana Gamage P, McMahan C, Wang L, Tu W. 2018. A Gamma-frailty proportional hazards model for bivariate interval-censored data. Computational Statistics and Data Analysis 128: 354-366.
Lu M, McMahan C. 2018. A partially linear proportional hazards model for current status data. Biometrics 74: 1240-1249.
Watson Self S, McMahan C, Brown D, Lund R, Gettings J, Yabsley M. 2018. A large scale spatio-temporal binomial regression model for estimating seroprevalence trends. Environmetrics 29: e2538.
Chaudhari M, Kim E, Withana Gamage P, McMahan C, Kosorok M. 2018. Study design with staggered sampling times for evaluating sustained unresponsiveness to peanut sublingual immunotherapy. Statistics in Medicine 37: 3944-3958.
Baurley J, McMahan C, Ervin C, Pardamean B, Bergen A. 2018. Biosignature discovery for substance use disorders using statistical learning. Trends in Molecular Medicine 24: 221-235.
Wang D, McMahan C, Tebbs J, Bilder C. 2018. Group testing case identification with biomarker information. Computational Statistics and Data Analysis 122: 156-166.
McMahan C, Baurley J, Bridges W, Joyner C, Fitra Kacamarga M, Lund R, Pardamean C, Paradmean B. 2017. A Bayesian hierarchical model for identifying significant polygenic effects while controlling for confounding and repeated measures. Statistical Applications in Genetics and Molecular Biology 16: 407-419.
Warasi S, McMahan C, Tebbs J, Bilder C. 2017. Group testing regression models with dilution submodels. Statistics in Medicine 36: 4860-4872.
McMahan C, Tebbs J, Hanson T, Bilder C. 2017. Bayesian regression models for group testing data. Biometrics 73: 1443-1452.
Liu Y, Watson S, Lund R, Gettings J, Nordone S, Yabsley M, McMahan C. 2017. A Bayesian spatio-temporal model for forecasting Anaplasma species seroprevalence in domestic dogs within the contiguous United States. PLoS One 12: e0182028.
Russell B, Wang D, McMahan C. 2017. Spatially modeling the effects of meteorological drivers of PM2.5 in the Eastern United States via a local linear penalized quantile regression estimator. Environmetrics 28: pii: e2448.
Liu Y, McMahan C, Gallagher C. 2017. A general regression framework for the regression analysis of pooled biomarker assessments. Statistics in Medicine 36: 2363-2377.
Hou P, Tebbs J, Bilder C, McMahan C. 2017. Hierarchical group testing for multiple infections. Biometrics 73: 656-665.
Watson S, Liu Y, Lund R, Gettings J, Nordone S, McMahan C, Yabsley M. 2017. A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Borrelia burgdorferi, causative agent of Lyme disease, in domestic dogs within the contiguous United States. PLoS One 12: e0174428.
Liu Y, Lund R, Nordone S, Yabsley M, McMahan C. 2017. A Bayesian spatio-temporal model for forecasting the prevalence of antibodies to Ehrlichia species in domestic dogs within the contiguous United States. Parasites and Vectors 10: 138.
Bowman D, Liu Y, McMahan C, Nordone S, Yabsley M, Lund R. 2016. Forecasting United States heartworm (Dirofilaria immitis) prevalence in dogs. Parasites and Vectors 9: 540.
Sapp S, Weinstein S, McMahan C, Yabsley M. 2016. Variable infection dynamics in four Peromyscus species following experimental inoculation with Baylisascaris procyonis. Journal of Parasitology 102: 538-544.
McDonald J, Gerard P, McMahan C, Schucany W. 2016. Exact-permutation based sign tests for clustered binary data via weighted and unweighted test statistics. Journal of Agricultural Biological and Environmental Statistics 21: 698-712.
Warasi S, Tebbs J, McMahan C, Bilder C. 2016. Estimating the prevalence of multiple diseases from two-stage hierarchical pooling. Statistics in Medicine 35: 3851-3864.
McMahan C, McLain A, Gallagher C, Schisterman E. 2016. Estimating covariate-adjusted measures of diagnostic accuracy based on pooled biomarker assessments. Biometrical Journal 58: 944-961.
McMahan C, Wang D, Beall M, Bowman D, Little S, Pithuia P, Sharp J, Stitch R, Yabsley M, Lund R. 2016. Factors associated with Anaplasma spp. seroprevalence among dogs in the United States. Parasites and Vectors 9: 169.
Wang L, McMahan C, Hudgens M, Qureshi Z. 2016. A flexible, computationally efficient method for fitting the proportional hazards model to interval-censored data. Biometrics 72: 222-231.
McMahan C, Tebbs J, Bilder C. 2016. Invited rejoinder to “A note on the evaluation of group testing algorithms in the presence of misclassification.” Biometrics 72: 303-304.