Methodological/Independent Research

My methodological research mainly focuses in the area of precision and personalized health. Currently, my primary research focuses on studying heterogeneous treatment effect (HTE) to understand the varying treatment effect across patients for personalized treatment recommendation, especially in survival data. I'm also interested in methods and applications of deep learning/machine learning, data integration, generative models and representation learning.

Publication:

• Bo, N., & Ding, Y. (2026). Estimation of the interpretable heterogeneous treatment effect with causal subgroup discovery in survival outcomes. Lifetime Data Analysis, 32(1), 11. https://doi.org/10.1007/s10985-026-09688-z

• Bo, N., Jeong, J., Forno, E., Ding, Y. (2025). Evaluating meta-learners for analyzing treatment heterogeneity in survival outcomes: application to pediatric asthma care under COVID-19 disruption. Statistics in Medicine, 44: e10333. https://doi.org/10.1002/sim.10333

    🎉The earlier version won ASA Health Policy Statistics Section (2024) student paper award. 

• Bo, N.#, Wei, Y.#, Zeng, L., Kang, C., Ding, Y. (2024) A meta-learner framework to estimate individualized treatment effects for survival outcomes. Journal of Data Science, 1-19, DOI 10.6339/24-JDS1119.

        🎉The earlier version won ASA Lifetime Data Science Section (2022) student paper award.

Publication:

• Bo, N., Ding, Y., et al. (2026+) Characterizing heterogeneous treatment effect through a win probability-based subgroup identification procedure for RCTs with survival endpoints. (in preparation)

• Liu, J., Bo N., Nana-Sinkam P., Ding Y. (2026+) Brief Report: Reassessment of bTMB as a Predictive Biomarker in Non–Small Cell Lung Cancer Immunotherapy: A Reanalysis of OAK and POPLAR (submitted)

Publication:

• Bo, N.#, Lang, Z.#, Kuang, Z., Ding, Y. (2026+) A tutorial on deep learning in survival outcomes. Invited paper from Lifetime Data Analysis (in preparation)

     The earlier version was presented at the ASA LiDS Webinar: deep learning in survival outcomes.

     The extended version was presented as ASA Lifetime Data Science Conference (2025) short course.

• Liu, J., Bo, N., Zhou, X., Forno, E., Ding, Y. (2024) Predicting pediatric asthma severe outcomes using machine learning methods for EHR data with repeated clinic visits. Journal of Statistical Research, 58(1), 131–149. https://doi.org/10.3329/jsr.v58i1.75419.