Oh, E. J., Ahn, S., Tham, T., & Qian, M. (2025). Leveraging two-phase data for improved prediction of survival outcomes with application to nasopharyngeal cancer. Biometrics, 81(2). https://doi.org/10.1093/biomtc/ujaf080

Zhao, Y., Lan, T., Zhong, G., Hagen, J., Pan, H., Chung, W. K., & Shen, Y. (2025). A probabilistic graphical model for estimating selection coefficients of nonsynonymous variants from human population sequence data. Nature Communications, 16(1). https://doi.org/10.1038/s41467-025-59937-2

Shook-Sa, B. E., Zivich, P. N., Lee, C., Xue, K., Ross, R. K., Edwards, J. K., Stringer, J. S. A., & Cole, S. R. (2025). Double robust variance estimation with parametric working models. Biometrics, 81(2). https://doi.org/10.1093/biomtc/ujaf054

BJ, W., S, K., L, D., KA, A.-N., M, M., C, T., E, G., AM, C., M, O., DJ, J., O, A., AG, L., DJ, R., EA, B.-K., P, F.-L., AM, M., R, H., BA, C., & KP, A. (2025). Evaluation of gestational nonpersistent pesticide exposure with newborn size and gestational length in rural Ghana using a novel time-varying extension of multiple informant models. Environment International, 196, 109292. https://doi.org/10.1016/j.envint.2025.109292

Sheng, Y., & Sun, Y. (2024). Statistical Inference for Counting Processes Under Shape Heterogeneity. Statistics in Medicine, 43(30), 5849–5861. Portico. https://doi.org/10.1002/sim.10280

Kim, S., Kim, Y., & Wang, Y. (2024). Temporal generative models for learning heterogeneous group dynamics of ecological momentary assessment data. Biometrics, 80(4). https://doi.org/10.1093/biomtc/ujae115

McDonnell, E. I., Xie, S., Marder, K., Cui, F., & Wang, Y. (2024). Dynamic undirected graphical models for time‐varying clinical symptom and neuroimaging networks. Statistics in Medicine, 43(21), 4131–4147. Portico. https://doi.org/10.1002/sim.10143

Xie, S., Zeng, D., & Wang, Y. (2024). Identifying temporal pathways using biomarkers in the presence of latent non-Gaussian components. Biometrics, 80(2). https://doi.org/10.1093/biomtc/ujae033

Díaz, I., Hoffman, K. L., & Hejazi, N. S. (2023). Causal survival analysis under competing risks using longitudinal modified treatment policies. Lifetime Data Analysis, 30(1), 213–236. https://doi.org/10.1007/s10985-023-09606-7

Renson, A., Hudgens, M. G., Keil, A. P., Zivich, P. N., & Aiello, A. E. (2023). Identifying and Estimating Effects of Sustained Interventions under Parallel Trends Assumptions. Biometrics, 79(4), 2998–3009. https://doi.org/10.1111/biom.13862

Rudolph, K. E., Williams, N., & Díaz, I. (2023). Efficient and Flexible Estimation of Natural Direct and Indirect Effects under Intermediate Confounding and Monotonicity Constraints. Biometrics, 79(4), 3126–3139. https://doi.org/10.1111/biom.13850

Hejazi, N. S., Rudolph, K. E., Van Der Laan, M. J., & Díaz, I. (2022). Nonparametric causal mediation analysis for stochastic interventional (in)direct effects. Biostatistics, 24(3), 686–707. https://doi.org/10.1093/biostatistics/kxac002

Zelner, J., Masters, N. B., Naraharisetti, R., Mojola, S. A., Chowkwanyun, M., & Malosh, R. (2022). There are no equal opportunity infectors: Epidemiological modelers must rethink our approach to inequality in infection risk. PLOS Computational Biology, 18(2), e1009795. https://doi.org/10.1371/journal.pcbi.1009795

Leoce, N. M., Jin, Z., Kehm, R. D., Roh, J. M., Laurent, C. A., Kushi, L. H., & Terry, M. B. (2021). Modeling risks of cardiovascular and cancer mortality following a diagnosis of loco-regional breast cancer. Breast Cancer Research, 23(1). https://doi.org/10.1186/s13058-021-01469-w

Lyu, T., Luo, X., Huang, C.-Y., & Sun, Y. (2021). Additive rates model for recurrent event data with intermittently observed time-dependent covariates. Statistical Methods in Medical Research, 30(10), 2239–2255. https://doi.org/10.1177/09622802211027593