We have over 50 years of collective practicing experience and multiple co-authored publications. Here’s a sample of publications that represent our abilities to provide the statistical data you can count on.

Kuhn, M., and Johnson, K. (2013). Applied Predictive Modeling. Springer Series in Statistics.

Emir, B., Johnson, K., Kuhn, M., & Parsons, B. (2017). Predictive Modeling of Response to Pregabalin for the Treatment of Neuropathic Pain Using 6-Week Observational Data: A Spectrum of Modern Analytics Applications. Clinical Therapeutics, 39(1), 98-106. (DOI: http://dx.doi.org/10.1016/j.clinthera.2016.11.015).

Johnson, K., Guo, C., Gosink, M., Wang, V., and Hauben, M. (2012). Multinomial modeling and an evaluation of common data-mining algorithms for identifying signals of disproportionate reporting in pharmacovigilance databases. Bioinformatics, 28(23), 3123-3130. (DOI: 10.1093/bioinformatics/bts576)

Mandal, A., Johnson, K., Wu, C.F.J., and Bornemeier, D. (2007). Identifying promising compounds in drug discovery: Genetic algorithms and some new statistical techniques. Journal of Chemical Information and Modeling, 47(3), 981-988. (DOI: 10.1021/ci600556v) Highlighted in Drug Discovery News Online: http://www.drugdiscoverynews.com/index.php?newsarticle=1318

Thomson, J., Johnson, K., Chapin, R., Stedman, D., Kumpf, S., and Ozolins, T. (2011). Not a walk in the park: The ECVAM whole embryo culture model challenged with pharmaceuticals and attempted improvements with Random Forest design. Birth Defects Research (Part B), 92, 111-121. (DOI: 10.1002/bdrb.20289)

Culp M., Johnson K., and Michailidis G. (2010). The Ensemble Bridge Algorithm: A new modeling tool for drug discovery problems. Journal of Chemical Information and Modeling, 50, 309-316. (DOI: 10.1021/ci9003392)