(* indicates first author or corresponding author)

Working papers

Cho, Y., Li, L. and Johnson, B. (2024+). Semiparametric Estimation of the Mean Outcome Amidst Informative Treatment Discontinuation with Observational Data.

Cho, Y. and Auer, P. (2024+). Mendelian Randomization Analysis under the Accelerated Failure Time Model.

Yang, X., Cho. Y., Wahed, A. and Cheng, Y. (2024+). Imputation-based Q-learning with regression trees for censored survival data.

Statistical Methodology

Causal inference

Cho, Y., Zhu, Y., Kwon, S., Hyun, J. and Hall, C. (2024). Mediation analysis with multiple mediators for survival data, International Journal of Biostatistics, submitted.

Cho, Y., Zheng, C., Qi, L., Prentice, R. and Zhang, M-J. (2024). Causal Effect Estimation for Competing Risk Data in Randomized Trial: Adjusting High-dimensional Covariates to Gain Efficiency, Journal of Applied Statistics, under revision.

Cho, Y. (2024). Regression discontinuity for survival data. Communications for Statistical Applications and Methods, 31(1), 155-178.

Cao, Z., Cho, Y*. and Li, F. (2024), Transporting randomized trial results to estimate counterfactual survival functions in target populations. Pharmaceutical Statistics, https://doi.org/10.1002/pst.2354.

Su, X., Cho, Y*., Ni, L., Liu, L., and Dusseldorp, E. (2023), Refined Moderation Analysis with Categorical Outcomes in Precision Medicine. Statistics in Medicine, 42(4), 470-486 https://doi.org/10.1002/sim.9627.

Cho, Y.*, Hu, C. and Ghosh, D. (2021). Analysis of regression discontinuity designs using censored data. Journal of Statistical Research, 55(1), 225-248.

Cho, Y.*, Rau, A., Reiner, A., Auer, P. (2021). Mendelian randomization analysis with survival outcomes. Genetic Epidemiology, 45(1), 16-23. https://doi.org/10.1002/gepi.22354.

Ghosh, D. and Cho, Y. (2018). Predictive directions for individualized treatment selection in clinical trials. preprint arXiv:1807.03375v1 [stat.ME].

Machine learning

Cho, Y.*, Auer, P. and Ghosh, D. (2024). Nonlinear estimation methods for Mendelian randomization in genetic studies. Sankya B, https://doi.org/10.1007/s13571-023-00309-5.

Cho, Y*., Zhan, X and Ghosh, D. (2022). Nonlinear predictive directions in clinical trials. Computational Statistics & Data Analysis, 174 (107476), https://doi.org/10.1016/j.csda.2022.107476.

Cho, Y*., Molinaro, A., Hu, C. and Strawderman, R. (2022). Regression trees and ensembles for cumulative incidence functions. International Journal of Biostatistics, 18(2) 397-419. doi: 10.1515/ijb-2021-0014.

Cho, Y*. and Ghosh, D. (2021). Quantile-based subgroup identification for randomized clinical trials. Statistics in Biosciences, 13(1), 90-128. https://doi.org/10.1007/s12561-020-09286-z

Cho, Y*. and Ghosh, D. (2020). Bridging linearity-based and kernel-based sufficient dimension reduction. preprint arXiv:2010.15009.

Cho, Y., Molinaro, A., Hu, C. and Strawderman, R. (2020). Regression Trees for Cumulative Incidence Functions. preprint arXiv:2011.06706 [stat.ME]

Survival analysis

Cho, Y., Kim, S. and Ahn, K. W. (2024). Efficient estimation for the multivariate Cox model with missing covariates, Statistica Neerlandica, revision submitted.

Cho, Y*. and Ghosh, D. (2021). Covariate adjustment via propensity scores for recurrent events in the presence of dependent censoring. Communications in Statistics - Theory and Methods, 50(1), 216-236, DOI: 10.1080/03610926.2019.1634208.

Cho, Y.*, Hu, C. and Ghosh, D. (2018). Covariate adjustment using propensity scores for dependent censoring problems in the accelerated failure time model. Statistics in Medicine, 37(3), 390-404. DOI: 10.1002/sim.7513. https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7513

Cho, Y*. and Ghosh, D. (2017). A general approach to goodness of fit for U-processes. Statistica Sinica, 27(3), 1175-1192. DOI:10.5705/ss.202014.0141. http://www3.stat.sinica.edu.tw/sstest/oldpdf/A27n311.pdf

Cho, Y*. and Ghosh, D. (2015). Weighted estimation of the accelerated failure time model in the presence of dependent censoring. PLOS ONE, DOI: 10.1371/journal.pone.0124381. http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0124381&type=printable

Collaboration

Gwon, S. H., Cho, Y., Laestadius, L. I., Wang, Y. and Lee, H. J. (2021). Factors Associated with Opposition to a Vape-Free Campus Policy. Metropolitan Universities, 32(1), 124-145.

Ohn, J., Hur, K., Cho, Y., Park, J., Kim, J. Y., Lee, S. J., Park, H. and Mun, J. H. (2021). Developing a predictive model for distinguishing invasive nail unit melanoma from nail unit melanoma in situ. Journal of the European Academy of Dermatology and Venereology, 35(4), 906-911.

Lee, Y., Cho, Y*. and Oh, H. (2021). Multiple chronic conditions and risk of dementia and cognitive impairment among older Americans: findings from the ADAMS. Aging, Neuropsychology and Cognition, 28(4), 493-507. doi.org/10.1080/13825585.2020.1790492.

Jo, G., Cho, S. I., Cho, Y., Ohn, J. and Mun, J-H. (2020). Tumor growth rate as a prognostic factor of acral melanoma in a Korean population. Medicine, 99(21)(e19936). http://dx.doi.org/10.1097/MD.0000000000019936.

Ohn, J., Jo, G., Cho, Y., Sheu, S. L., Cho, K. H. and Mun, J-H. (2018). Assessment of a predictive scoring model for dermoscopy of subungual melanoma in situ. JAMA Dermatology. 154(8), 890-896. doi:10.1001/jamadermatol.2018.1372 https://jamanetwork.com/journals/jamadermatology/article-abstract/2684586.

Ko, K., Gwak, H., Lee, E., Kim, G., Moon, D., Cho, Y. and Kim, S. (2021). Action Recognition Model to Monitor Illegal Dumping using Zoom-In Image Sequence Data. Quantitative Bio-Science, 40(2), 95-102.

Lim, S.S., Hui, L, Ohn, J., Cho, Y., Oh, C. C. and Mun, J-H. (2022). Diagnostic accuracy of dermoscopy for onychomycosis: a systematic review. Frontiers in Medicine. https://doi.org/10.3389/fmed.2022.1048913

Gwon, S. H., Cho, Y., Kim, Y., Paek, S. and Lee, H. J. (2024). Differences in attentional bias toward e-cigarette cues between e-cigarette users and nonusers, Journal of Addictions Nursing, 35(3), 156-165.

Thalji, S., Cortina, C. Frebault, J., Cho, Y., Thorgerson, A., Bergom, C., Huang, C-C and Kong, A. (2023). Associations between Hospital Volume on Overall Survival after Surgery in Older Patients with Breast Cancer. Annals of Surgical Oncology, 30, 6462–6470. https://doi.org/10.1245/s10434-023-13725-8 .