Kellie J. Archer, PhD
Chair and Professor
Biostatistics
“It is essential to conduct research, make evidence-based decisions and derive policies in public health using data from well-designed studies that are appropriately analyzed. Working as a biostatistician at Ohio State allows me to engage in a wide range of applications that are seeking to reduce disease burden and improve public health. Additionally, we are training the next generation of biostatistical researchers to advance methodologies for solving new statistical challenges posed by collecting complex, high-dimensional and high-volume data.”
Biography
Dr. Archer’s primary research area has been in the development of statistical methods and computational algorithms for analyzing high-dimensional datasets. Such datasets frequently arise in studies that use high-throughput genomic assays, which yield datasets consisting of a large number of candidate predictors (p, the number of genes or proteins) on a small number of observations (n, the number of samples). Therefore, fitting statistical models for overparameterized problems is an active area of methodological research. Her current research is focused on developing methods and software when the response is either ordinal, discrete, or a time-to-event response where subjects may also experience cure and the genomic data are collected cross-sectionally or longitudinally.
Education
- PhD
- College of Public Health, The Ohio State University, 2001
- Master Applied Statistics
- Department of Statistics, The Ohio State University, 1993
- BA
- Applied Mathematics and Philosophy, Franklin College, 1991
Research interests
Statistical methods for the analysis of data from high-throughput genomic assays; Discrete and ordinal response modeling; Mixture cure models; Statistical computing; Supervised learning and data mining
- Elected Fellow
- American Statistical Association
Active grants
- R01LM013879 - Penalized mixture cure models for identifying genomic features associated with outcome in acute myeloid leukemia
- R21AI172077 - Pretransplant comprehensive scores to predict long term graft outcomes
- R01DK109581 - Assessment of Donor Quality for Improving Kidney Transplant Outcomes
Select publications
- Archer KJ, Fu H, Mrozek K, Nicolet D, Mims AS, Uy GL, Stock W, Byrd JC, Hiddemann W, Braess J, Spiekermann K, Metzeler KH, Herold T, Eisfeld A-K. Identifying long-term survivors and those at higher or lower risk of relapse among patients with cytogenetically normal acute myeloid leukemia using a high-dimensional mixture cure model. Journal of Hematology & Oncology, 17:28, 2024.
- Archer KJ, Fu H, Mrozek K, Nicolet D, Mims AS, Uy GL, Stock W, Byrd JC, Hiddemann W, Metzeler KH, Rausch C, Krug U, Sauerland C, Gorlich D, Berdel WE, Woermann BJ, Braess J, Spiekermann K, Herold T, Eisfeld A-K. Improving risk stratification for 2022 European LeukemiaNet favorable-risk patients with acute myeloid leukemia. The Innovation, 5(6):100719, 2024.
- Seffernick AE, Mrózek K, Nicolet D, Stone RM, Eisfeld AK, Byrd JC, Archer KJ. High-dimensional genomic feature selection with the ordered stereotype logit model. Briefings in Bioinformatics, Nov 19;23(6):bbac414, 2022.
- Fu H, Nicolet D, Mrózek K, Stone RM, Eisfeld AK, Byrd JC, Archer KJ. Controlled variable selection in Weibull mixture cure models for high-dimensional data. Statistics in Medicine, Sep 30;41(22):4340-4366, 2022.
- Archer KJ, Bardhi E, Maluf DG, McDaniels J, Rousselle T, King A, Eason JD, Gallon L, Akalin E, Mueller TF, Mas VR. Pretransplant kidney transcriptome captures intrinsic donor organ quality and predicts 24-month outcomes. American Journal of Transplantation, 22(11):2515-2528, 2022.
- Archer KJ, Seffernick AE, Sun S, Zhang Y. ordinalbayes: Fitting Ordinal Bayesian Regression Models to High-Dimensional Data Using . Stats (Basel), Jun;5(2):371-384, 2022.
- Zhang Y, Archer KJ. Bayesian penalized cumulative logit model for high-dimensional data with an ordinal response. Statistics in Medicine, Mar 15;40(6):1453-1481, 2021.
- Zhang Y, Archer KJ. Bayesian variable selection for high-dimensional data with an ordinal response: identifying genes associated with prognostic risk group in acute myeloid leukemia. BMC Bioinformatics, Nov 2;22(1):539, 2021.
- Fu H, Archer KJ. High-dimensional variable selection for ordinal outcomes with error control. Briefings in Bioinformatics, Jan 18;22(1):334-345, 2021.
- Lehman RR, Archer KJ. Penalized negative binomial models for modeling an overdispersed count outcome with a high-dimensional predictor space: Application predicting micronuclei frequency. PLoS One,14(1):e0209923, 2019.