Navigation
Home
Publications
Software

Papers

Statistical Theory and Methodology

  • Gao, J., Hu, F., and Ma, W. (2024) Response-adaptive randomization procedure in clinical trials with surrogate endpoints. Statistics in Medicine, accepted.

  • Tu, F., Ma, W., and Liu, H. (2024) A unified framework for covariate adjustment under stratified randomization. Stat, accepted.

  • Wang, T. and Ma, W. (2024) The impact of misclassification on covariate-adaptive randomized clinical trials with generalized linear models. Journal of Statistical Planning and Inference, accepted.

  • Ye, X., Hu, F., and Ma, W. (2024) Robustness of response-adaptive randomization. Biometrics, 80(2), ujae049.

  • Ma, W., Li, P., Zhang, L. X., and Hu, F. (2024) A new and unified family of covariate adaptive randomization procedures and their properties. Journal of the American Statistical Association, 119(545), 151–162.

  • Qin, Y., Li, Y., Ma, W., Yang, H., and Hu, F. (2024) Adaptive randomization via Mahalanobis distance. Statistica Sinica, 34(1), 353–375.

  • Gu, Y., Liu, H., and Ma, W. (2023) Regression-based multiple treatment effect estimation under covariate-adaptive randomization. Biometrics, 79(4), 2869–2880.

  • Ma, W., Ye, X., Tu, F., and Hu, F. (2023) carat: An R package for covariate-adaptive randomization in clinical trials. Journal of Statistical Software, 102(2), 1–47.

  • Liu, H., Tu, F., and Ma, W. (2023) Lasso-adjusted treatment effect estimation under covariate-adaptive randomization. Biometrika, 110(2), 431–447.

  • Wang, T., He, K., Ma, W., Bandyopadhyay, D., and Sinha, S. (2023) Minorize-maximize algorithm for the generalized odds rate model for clustered current status data. Canadian Journal of Statistics, 51(4), 1150–1170.

  • Ma, W., Tu, F., and Liu, H. (2022). Regression analysis for covariate-adaptive randomization: A robust and efficient inference perspective. Statistics in Medicine, 41(29), 5645–5661.

  • Ma, W., Wang, M., and Zhu, H. (2022) Seamless phase II/III clinical trials with covariate adaptive randomization. Statistica Sinica, 32(2), 1079–1098.

  • Zhao, W., Ma, W., Wang, F., and Hu, F. (2022) Incorporating covariates information in adaptive clinical trials for precision medicine. Pharmaceutical Statistics, 21(1), 176–195.

  • Li, Y., Ma, W., Qin, Y., and Hu, F. (2021) Testing for treatment effect in covariate-adaptive randomized trials with generalized linear models and omitted covariates. Statistical Methods in Medical Research, 30(9), 2148–2164.

  • Ma, W., Zhang, L. X., and Hu, F. (2021) Comment on ‘Inference after covariate-adaptive randomisation: aspects of methodology and theory’. Statistical Theory and Related Fields, 5(3), 187–189.

  • Li, X., Ma, W., and Hu, F. (2021) Sample size re‐estimation for covariate‐adaptive randomized clinical trials. Statistics in Medicine, 40(12), 2839–2851.

  • Wang, T. and Ma, W. (2021) The impact of misclassification on covariate-adaptive randomized clinical trials. Biometrics, 77(2), 451–464.

  • Gao, J., Ma, W., Cheung, S. H., and Hu, F. (2020) Response-adaptive randomization in clinical trials: Recent advances and future perspectives. Journal of Applied Statistics and Management, 39(4), 595–610.

  • Ma, W., Qin, Y., Li, Y., and Hu, F. (2020) Statistical inference for covariate-adaptive randomization procedures. Journal of the American Statistical Association, 115(531), 1488–1497.

  • Hu, F., Hu, Y., Ma, W., Zhang L. X., and Zhu, H. (2015) Statistical inference of adaptive randomized clinical trials for personalized medicine. Clinical Investigation, 5(4), 415–425.

  • Ma, W., Hu, F., and Zhang, L. X. (2015) Testing hypotheses of covariate-adaptive randomized clinical trials. Journal of the American Statistical Association, 110(510), 669–680.

Medicine, Biology and Public Health

  • Cui, J., Pang, J., Zhang, L., Li, J., Wu, X., Zhu, X., Ma, W., Luo, S., Gao, H., Wang, Z., and Cai, J. (2024) Peng Zu study on healthy aging in China (Peng Zu cohort): Design and goals. China CDC Weekly, 6(34), 876–882.

  • Liang, W., Yang, Y., Gong, S., Wei, M., Ma, Y., Feng, R., Gao, J., Liu, X., Tu, F., Ma, W., Yi, X., Liang, Z., Wang, F., Wang, L., Chen, D., Shu, W., E. Miller, B., Tal-Singer, R., Donaldson, G., Wedzicha, J., Singh, D., Wilkinson, T., Brightling, C., Chen, R., Zhong, N., Wang, Z. (2023) Airway dysbiosis accelerates lung function decline in chronic obstructive pulmonary disease. Cell Host & Microbe, 31(6), 1054–1070.

  • Yan, Z., Chen, B., Yang, Y., Yi, X., Wei, M., Ecklu-Mensah, G., Bushmann, M., Liu, H., Gao, J., Liang, W., Liu, X., Yang, J., Ma, W., Liang, Z., Wang, F., Chen, D., Wang, L., Shi, W., Stampfli, M. R., Li, P., Gong, S., Chen, X., Shu, W., El-Omar, E. M., Gilbert, J. A., Blaser, M. J., Zhou, H., Chen, R., and Wang, Z. (2022) Multi-omics analyses of airway host-microbe interactions in chronic obstructive pulmonary disease identify potential therapeutic interventions. Nature Microbiology, 7, 1361–1375.

  • Wang, Z., Locantore, N., Haldar, K., Ramsheh, M., Beech, A., Ma, W., Brown, J., Tal-Singer, R., Barer, M., Bafadhel, M., Donaldson, G., Wedzicha, J., Singh, D., Wilkinson, T., Miller, B., and Brightling, C. (2021) Inflammatory endotype–associated airway microbiome in chronic obstructive pulmonary disease clinical stability and exacerbations: A multicohort longitudinal analysis. American Journal of Respiratory and Critical Care Medicine, 203(12), 1488–1502.

  • Zhang, Y., Liu, M., Wu, S. S., Jiang, H., Zhang, J., Wang, S., Ma, W., Li, Q., Ma, Y., Liu, Y., Feng, W., Amsalu, E., Li, X., Wang, W., Li, W., and Guo, X. (2019) Spatial distribution of tuberculosis and its association with meteorological factors in mainland China. BMC Infectious Diseases, 19, 379.

  • Cohan, S., Kappos, L., Giovannoni, G., Wiendl, H., Selmaj, K., Havrdova, E., Rose, J., Greenberg, S., Phillips, G., Ma, W., Wang, P., Lima, G., and Sabatella, G. (2018) Efficacy of daclizumab beta versus intramuscular interferon beta-1a on disability progression across patient demographic and disease activity subgroups in DECIDE. Multiple Sclerosis Journal, 24(14), 1883–1891.

Educational Data Mining and Learning Analytics

  • Baker, R. S., Ma, W., Zhao, Y., Wang, S., and Ma, Z. (2020) The results of implementing zone of proximal development on learning outcomes. In Proceedings of the 13th International Conference on Educational Data Mining, 749–753.

  • Zou, X., Ma, W., Ma, Z., and Baker, R. S. (2019) Towards helping teachers select optimal content for students. In Proceedings of the 20th International Conference on Artificial Intelligence in Education, 413–417.

  • Baker, R. S., Wang, F., Ma, Z., Ma, W., and Zheng, S. (2018) Studying the effectiveness of an online language learning platform in China. Journal of Interactive Learning Research, 29(1), 5–24.

Posters at Medical Conferences

  • Giovannoni, G., Ziemssen, T., Ma, W., and Fam, S. Daclizumab HYP efficacy on disease outcome measures using an expanded definition of highly active relapsing-remitting multiple sclerosis: Results from SELECT and DECIDE. Congress of the European Academy of Neurology, Copenhagen, Denmark, May 2016.

  • Fam, S., McCroskery, P., Ma, W., Chiodo, L., and Sabatella, G. Liver transaminase elevations in the DECIDE study of daclizumab HYP versus intramuscular interferon beta-1a. Congress of the European Academy of Neurology, Copenhagen, Denmark, May 2016.

  • Elkins, J., Kappos, L., Selmaj, K., Ma, W., and Riester, K. Tentative and confirmed disability progression in MS clinical trials. American Academy of Neurology Annual Meeting, Vancouver, Canada, Apr 2016.

  • Arnold, D., Kappos, L., Khan, O., Gauthier, S., Greenberg, S., Ma, W., Wang, P., Elkins, J., and Sabatella, G. Reduction in brain volume loss in patients receiving daclizumab HYP versus intramuscular interferon beta-1a: Results of the DECIDE study. Congress of the European Committee for Treatment and Research in Multiple Sclerosis, Barcelona, Spain, Oct 2015.

  • Cohan, S., Kappos, L., Selmaj, K., Havrdova, E., Kaufman, M., Rose, J., Greenberg, S., Amaravadi, L., Ma, W., and Elkins, J. Efficacy of daclizumab HYP vs intramuscular interferon beta-1a on disability progression across patient demographic and disease activity subgroups in DECIDE. Congress of the European Committee for Treatment and Research in Multiple Sclerosis, Barcelona, Spain, Oct 2015.

  • Kaufman, M., Kappos, L., Selmaj, K., Arnold, D., Havrdova, E., Boyko, A., Wiendl, H., Rose, J., Greenberg, S., Sweetser, M., Ma, W., Wang, P., and Beatty, M. The effect of daclizumab high-yield process (DAC HYP) on patient-centered functional outcomes: Results from the DECIDE study. Consortium of Multiple Sclerosis Centers Annual Meeting, Indianapolis, IN, May 2015.