Donna Coffman
Ph.D., University of North Carolina, Chapel Hill,
Associate Professor
Email: [email protected]
Office: Hamilton College, Room 359A
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Dr. Coffman joined the Department of Psychology in the fall of 2022. Previously, she was an Associate Professor in the Department of Epidemiology and Biostatistics at Temple University and Research Associate Professor at the Methodology Center in the College of Health and Human Development at Pennsylvania State University. She graduated with a PhD in Quantitative Psychology from the University of North Carolina, Chapel Hill.  

Dr. Coffman works at the interface of quantitative methodology and behavioral health. Specifically, she focuses on developing and applying methods for analyzing data from wearable and mobile devices that enable development of adaptive, individualized, health-behavior interventions, with the ultimate goal of enabling individuals to change and maintain healthy behaviors, such as smoking cessation, prevention and treatment of substance use disorders, and increasing physical activity and decreasing sedentary behavior. She is particularly interested in developing methods for mediation with intensive longitudinal data, such as that collected using ecological momentary assessments, and for strengthening causal inferences, particularly in mediation analysis. 

 

Cai, X., Coffman, D. L., Piper, M. E., & Li, R. (2022). Estimation and inference for the mediation effect in a time-varying mediation model. BMC Medical Research Methodology, 22(1), 1-12. doi: 10.1186/s12874-022-01585-x  

Chakraborti, Y., Coffman, D. L., & Piper, M. E. (2022). Time-varying mediation of pharmacological smoking cessation treatments on smoking lapse via craving, cessation fatigue, and negative mood. Nicotine and Tobacco Research, 24(10), 1548-1555. doi: 10.1093/ntr/ntac068  

Le Guen, C. L., Muir, K. C., Simmons, M., Coffman, D. L., & Soans, R. (2022). The impact of smoking status and smoking-related comorbidities on COVID-19 patient outcomes: A causal mediation analysis. Nicotine and Tobacco Research. doi: 10.1093/ntr/ntac193 

Mennis, J., McKeon, T. P., Coatsworth, J. D., Russell, M. A., Coffman, D. L., & Mason, M. J. (2022). Neighborhood disadvantage moderates the effect of a mobile health intervention on adolescent depression. Health & Place, 73. doi: 10.1016/j.healthplace.2021.102728  

Xu, S., Coffman, D. L., Liu, B., Xu, Y., He, J., & Niaura, R. S. (2022). Relationships between e-cigarette use and subsequent cigarette initiation among adolescents: A propensity score analysis using data from the PATH study. Prevention Science, 23(4), 608-617. doi: 10.1007/s11121-021-01326-4  

Forman, E. M., Chwyl, C., Berry, M. P., Taylor, L. C., Butryn, M. L., Coffman, D. L., Juarascio, A., & Manasse, S. M. (2021). Evaluating the efficacy of mindfulness and acceptance-based treatment components for weight loss: Protocol for a multiphase optimization strategy trial. Contemporary Clinical Trials, 110, 106573. doi: 10.1016/j.cct.2021.106573  

Coffman, D. L., Zhou, J., & Cai, X. (2020). Comparison of methods for handling covariate missingness in propensity score estimation with a binary exposure. BMC Medical Research Methodology, 20:168. doi: 10.1186/s.1287264-020-01053-4  

Coffman, D. L., Cai, X., Li, R., & Leonard, N. R. (2020). Challenges and opportunities in collecting and modeling ambulatory electrodermal activity data as an assessment of stress. Journal of Medical Internet Research: Biomedical Engineering, 5(1), e17106. doi: 10.2196/17106  

Dziak, J. J., Coffman, D. L., Reimherr, M., Petrovich, J., Li, R., Shiffman, S., & Shiyko, M. P. (2019). Scalar-on- function regression for predicting distal outcomes from with intensively gathered longitudinal data: Interpretability for applied scientists. Statistics Surveys, 13, 150-180. doi: 10.1214/19-SS126  

Hiremath, S. V., Amiri, A. M., Chhetry, B. T., Snethen, G., Schmidt-Read, M., Lamboy, M. R., Coffman, D. L., & Intille, S. S. (2019). Mobile health-based physical activity intervention for individuals with spinal cord injury in the community: A pilot study. PLOS ONE, 14(10), e0223762. doi: 10.1371/journal.pone.0223762  

Coffman, D. L., Zhou, J., Cai, X., & Graham, J. W. (2018). Addressing missing data in confounders when estimating propensity scores for continuous exposures. Health Services and Outcomes Research Methodology, 18(4), 265-286. doi: 10.1007/s10742-018-0191-6  

Zhu, Y., Ghosh, D., Coffman, D. L., & Savage-Williams, J. (2016). Estimating controlled direct effects of restrictive feeding practices in the ‘Early dieting in girls’ study. Journal of the Royal Statistical Society, Series C (Applied Statistics), 65(1), 115-130. doi: 10.1111/rssc.12109  

Zhu, Y., Coffman, D. L., & Ghosh, D. (2015). A boosting algorithm for estimating generalized propensity scores with continuous treatments. Journal of Causal Inference, 3, 25-40. doi: 10.1515/jci-2014-0022  

Coffman, D. L., & Zhong, W. (2012). Assessing mediation using marginal structural models in the presence of confounding and moderation. Psychological Methods, 17(4), 642-664. doi: 10.1037/a0029311