Chair and Professor
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.
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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.
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
Archer KJ, Dumur CI, Joel SE, Ramakrishnan V. Assessing quality of hybridized RNA in Affymetrix GeneChip experiments using mixed effects models. Biostatistics, 7(2):198-212, 2006.
Archer KJ and Kimes RV. Empirical characterization of random forest variable importance estimates. Computational Statistics and Data Analysis, 52(4): 2249-2260, 2008.
Archer KJ, Mas VR. Ordinal response prediction using bootstrap aggregation, with application to a high-throughput methylation dataset. Statistics in Medicine, Dec 20;28(29):3597-610, 2009.
Archer KJ, Reese SE. Detection Call Algorithms for High-throughput Gene Expression Microarray Data. Briefings in Bioinformatics, 11(2):244-52, 2010.
Archer KJ, Zhao Z, Guennel T, Maluf DG, Fisher RA, Mas VR. Identifying genes progressively silenced in preneoplastic and neoplastic liver tissues. International Journal of Computational Biology and Drug Design, 3(1), 52-67, 2010.
Asomaning N, Archer KJ. High-throughput DNA methylation datasets for evaluating false discovery rate methodologies. Computational Statistics and Data Analysis, 56(6):1748-1756, 2012.
Archer KJ, Williams AAA. L1 penalized continuation ratio models for ordinal response prediction using high-dimensional datasets. Statistics in Medicine, 31(14):1464-74, 2012.
Archer KJ, Hou J, Zhou Q, Ferber K, Layne JG, Gentry AE. ordinalgmifs: An R package for ordinal regression in high-dimensional data settings. Cancer Informatics, 13:187-95, 2014.
Hou J, Archer KJ. Regularization method for predicting an ordinal response using longitudinal high-dimensional genomic data. Statistical Applications in Genetics and Molecular Biology, 14(1):93-111, 2015.
Ferber K, Archer KJ. Modeling discrete survival time using genomic feature data. Cancer Informatics, 14(Suppl 2):37-43, 2015.
Makowski M, Archer KJ. Generalized monotone incremental forward stagewise method for modeling count data: Application predicting micronuclei frequency. Cancer Informatics, 14(Suppl 2):97-105, 2015.
Gentry AE, Jackson-Cook C, Lyon D, Archer KJ. Penalized Ordinal Regression Methods for Predicting Stage of Cancer in High-Dimensional Covariate Spaces. Cancer Informatics, 14(Suppl 2):201-8, 2015.
Siangphoe U, Archer KJ. Estimation of random effects and identifying heterogeneous genes in meta-analysis of gene expression studies. Briefings in Bioinformatics, 18(4):602-618, 2017.
Siangphoe U, Archer KJ, Mukhopadhyay ND. Classical and Bayesian random-effects meta-analysis models with sample quality weights in gene expression studies. BMC Bioinformatics, 20:18, 2019.
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.
Fu H, Archer KJ. High-dimensional variable selection for ordinal outcomes with error control. Briefings in Bioinformatics, in press.
