Rebecca Andridge, PhD

Rebecca Andridge, PhD
Associate Professor
Biostatistics
Phone: 614-247-7912
Email: andridge.1@osu.edu
CV: PDF icon RebeccaAndridge_CV.pdf
1841 Neil Ave.
242 Cunz Hall
Columbus, OH, 43210

Background:

Dr. Andridge's research is focused on imputation methods for missing data, primarily in large-scale probability samples. In particular, she works on methods for imputing survey data when missingness is driven by the missing values themselves (missing not at random). She also is involved in research on statistical issues in group-randomized trials. She collaborates with researchers across campus, including the Institute for Behavioral Medicine Research, the Nisonger Center for Excellence in Developmental Disabilities, and The OSU Comprehensive Cancer Center.

Complete list of published work on PubMed: http://go.osu.edu/andridgepubs

Research Interests:

Developing and evaluating statistical methods for handling missing data and for group-randomized trials

Education:

PhD, Biostatistics, University of Michigan, 2009
MS, Biostatistics, University of Michigan, 2005
BA, Economics, Stanford University, 1999

Awards/Honors:

CPH Excellence in Teaching Award, 2011

Selected Publications:

  • Andridge RR, Noone AM, Howlader N (2017). Imputing estrogen receptor (ER) status in a population-based cancer registry: A sensitivity analysis. Statistics in Medicine, 36; 1014-1028.
  • Kline D (PhD student), Andridge RR, Kaizar E (2017). Comparing multiple imputation methods for systematically missing subject-level data. Research Synthesis Methods, 8; 136-148.
  • Alfano CM, Peng J, Andridge RR, Lindgren ME, Povoski SP, Lipari AM, Agnese DM, Farrar WB, Yee LD, Carson WE, Kiecolt-Glaser JK (2017) Inflammatory cytokines and comorbidity development in breast cancer survivors vs. non-cancer controls: Evidence for accelerated aging? Journal of Clinical Oncology, 35; 149-156.
  • Schwartz TA, Andridge RR, Sainani KL, Stangl DK, Neely ML. (2016) Diverse perspectives on a flipped biostatistics classroom. Journal of Statistics Education, 24; 74-84.
  • Andridge RR and Thompson JK (2015). Assessing nonresponse bias in a business survey: Proxy pattern-mixture analysis for skewed data. Annals of Applied Statistics 9; 2237-2265.
  • Andridge RR and Thompson JK (2015). Using the fraction of missing information to identify auxiliary variables for imputation procedures via proxy pattern-mixture models. International Statistical Review, 83; 472-492.
  • Sullivan D (PhD student) and Andridge RR (2015). A hot deck imputation procedure for multiply imputing nonignorable missing data: The proxy pattern- mixture hot deck. Computational Statistics and Data Analysis 82; 173-185.
  • Kiecolt-Glaser JK, Habash DL, Fagundes CL, Andridge R, Peng J, Malarkey WB, Glaser R, Belury MA (2015). Daily stressors, past depression, and metabolic responses to high-fat meals: A novel path to obesity. Biological Psychiatry 77; 653-660.
  • Andridge RR, Shoben AB, Muller KE, Murray DM (2014). Analytic methods for individually randomized group treatment trials and group-randomized trials when subjects belong to multiple groups. Statistics in Medicine 33; 2178-2190.
  • Martens MA, Seyfer DL, Andridge RR, Foster JE, McClure KE, Coury DL (2013). Caregiver survey of pharmacotherapy to treat Attention Deficit/Hyperactivity Disorder in individuals with Williams syndrome. Research in Developmental Disabilities 34; 1700-1709.
  • Kiecolt-Glaser JK, Belury MA, Andridge RR, Malarkey WB, Hwang B, Glaser R (2012). Omega-3 supplementation lowers inflammation in healthy middle-aged and older adults: A randomized controlled trial. Brain, Behavior, and Immunity 26; 988-995.
  • Andridge RR (2011). Quantifying the impact of fixed effects modeling of clusters in multiple imputation for cluster randomized trials. Biometrical Journal 53; 57-74.
  • Andridge RR, Little RJA (2011). Proxy pattern-mixture analysis for survey nonresponse. Journal of Official Statistics 27; 153-180.
  • Andridge RR, Little RJA (2010). A review of hot deck imputation for survey nonresponse. International Statistical Review 78; 40-64.
  • Taylor JMG, Ankerst DP, Andridge RR (2008). Validation of biomarker-based risk prediction models. Clinical Cancer Research 14; 5977-5983.