ASA BIOP Scientific Working Group COMPASS




Selected Publications from Members of COMPASS

2026:

Li, J., Donohue, J. M. and Tang, L. (2026). Distributed fusion R-learner of heterogeneous treatment effect using distributed medicaid data. Biometrics, 82(1), ujag034.

Zhan, T. (2026). Deep-Learning-Assisted Statistical Methods with Examples in R, Chapman and Hall/CRC.

Zhan, T. and, Gu, Y. (2026). Semiparametric Weighted Spline Regression (SWSR) in Confirmatory Clinical Trials with Time-Varying Placebo Effects. Journal of Computational and Graphical Statistics, 35 (1), 111-118.


Prior to 2026:

Chen, X., Talisa, V.B., Tan, X., Qi, Z., Kennedy, J.N., Chang, C.C.H., Seymour, C.W. and Tang, L., 2025. Federated learning of robust individualized decision rules with application to heterogeneous multihospital sepsis population. The Annals of Applied Statistics, 19(2), 1270-1291.

Sechidis, K., Sun, S., Chen, Y., Lu, J., Zhang, C., Baillie, M., Ohlssen, D., Vandemeulebroecke, M., Hemmings, R., Ruberg, S. and Bornkamp, B. (2025). WATCH: a workflow to assess treatment effect heterogeneity in drug development for clinical trial sponsors. Pharmaceutical Statistics, 24(2), e2463.

Sui, Z., Ding, Y. and Tang, L. (2025). Robust transfer learning for individualized treatment rules in the presence of missing data. Biostatistics, 26(1), kxaf023.

Yuan, R., Rong, Z., Hu, H., Liu, T., Tao, S., Chen, W. and Tang, L. (2025). Harmony-based data integration for distributed single-cell multi-omics data. PLOS Computational Biology, 21(9), e1013526.

Zhan, T. and Kang, J. (2025). A general, flexible and harmonious framework to construct interpretable functions in regression analysis. Biometrics, (81)1, ujaf014.

Zhang, F. and Gou, J. (2025). Using Multiple Biomarkers for Patient Enrichment in Two-Stage Clinical Designs. Contemporary Clinical Trials, 156, 108012.

Gou, J. (2024). Reverse Graphical Approaches for Multiple Test Procedures. Journal of Biopharmaceutical Statistics, 34(1), 90–110.

Sun, S., Sechidis, K., Chen, Y., Lu, J., Ma, C., Mirshani, A., Ohlssen, D., Vandemeulebroecke, M. and Bornkamp, B. (2024). Comparing algorithms for characterizing treatment effect heterogeneity in randomized trials. Biometrical Journal, 66(1), 2100337.

Zhan, T. (2024). A class of computational methods to reduce selection bias when designing Phase 3 clinical trials. Statistics in Medicine, 43(10), 1993-2006.

Zhang, F. and Gou, J. (2023). Sample Size Optimization for Clinical Trials Using Graphical Approaches for Multiplicity Adjustment. Statistics in Medicine, 42(28), 5229–5246.

Lu, J. and Li, H. (2022). Hypothesis testing in high-dimensional instrumental variables regression with an application to genomics data. Statistica Sinica, 32, 613-633.

Prince (Liublinska), V., Han, M. X., Zhang, S. (2022). R Package "TippingPoint".

Zhan, T., Hartford, A., Kang, J. and Offen, W. (2022). Optimizing Graphical Procedures for Multiplicity Control in a Confirmatory Clinical Trial via Deep Learning. Statistics in Biopharmaceutical Research, 14(1), 92-102.

Vazquez-Cintron, E., Tenezaca, L., Angeles, C., Syngkon, A., Prince (Liublinska), V., Ichtchenko, K., & Band, P. (2016). Pre-clinical study of a novel recombinant botulinum neurotoxin derivative engineered for improved safety. Scientific Reports, 6(1), 30429.

Hunter, T., Yoon, R. S., Hutzler, L., Band, P., Prince (Liublinska), V., Slover, J., & Bosco III, J. A. (2015). No evidence for race and socioeconomic status as independent predictors of 30-day readmission rates following orthopedic surgery. American Journal of Medical Quality, 30(5), 484-488.