Rebecca Andridge, PhD

Dr. Andridge's research is focused on imputation methods for missing data, primarily when missingness is driven by the missing values themselves (missing not at random), and on measures of selection bias for nonprobability samples. She also works on statistical challenges that arise in analysis of data from 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, and serves as Lead Methodologist for several state-sponsored population-based surveys. She is an Elected Fellow of the American Statistical Association (2020).

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

Developing and evaluating statistical methods for handling missing data, for assessing nonignorable selection bias, and for group-randomized trials.

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

Elected Fellow, American Statistical Association, 2020

CPH Excellence in Teaching Award, 2011

• West B, Andridge RR (2023). Evaluating pre-election polling estimates using a new measure of non-ignorable selection bias. Public Opinion Quarterly, online advance access, https://doi.org/10.1093/poq/nfad018
• Andridge RR, Thompson JK (2023). Adapting nearest neighbor for multiple imputation: Advantages, challenges, and drawbacks. Journal of Survey Statistics and Methodology, 11; 213-233.
• West BT, Little RJ, Andridge RR, Boonstra P, Ware EB, Pandit A, Alvarado-Leiton F. (2021) Assessing selection bias in regression coefficients estimated from nonprobability samples with applications to genetics and demographic surveys. Annals of Applied Statistics, 15; 1556- 1581.
• Bailey B (PhD student), Andridge R, Shoben A. (2020) Multiple imputation by predictive mean matching in cluster-randomized trials. BMC Medical Research Methodology, 20; 72.
• Andridge RR, Bechtel L, Thompson KJ. (2021) Finding a flexible hot deck imputation method for multinomial data. Journal of Survey Statistics and Methodology, 9; 789-809.
• Andridge RR, Little RJA. (2020) Proxy pattern-mixture analysis for a binary variable subject to nonresponse. Journal of Official Statistics, 36; 703-728.
• Andridge RR, West B, Little RJA, Boonstra P, Alvarado-Leiton, F. (2019) Indices of non- ignorable selection bias for proportions estimated from non-probability samples. Journal of the Royal Statistical Society – Series C, 68; 1465-1483.
• 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.