Research

She is passionate about developing effective statistical solutions for applied research questions. Please see the Google Scholar page for a full list of publications. Here are some highlights of her recent research work.

Publications

Y Wang*, S Zhou*, F Yang*, X Qi, X Wang, X Guan, C Shen, N Duma, JV Aguilera, A Chintakuntlawar, KA Price, JR Molina, LC Pagliaro, TR Halfdanarson, A Grothey, SN Markovic, GS Nowakowski, SM Ansell, ML Wang. Treatment-related adverse events of PD-1 and PD-L1 inhibitors in clinical trials: a systematic review and meta-analysis. JAMA oncology 5, no. 7 (2019): 1008-1019.

NJ Short*, S Zhou*, C Fu, DA Berry, RB Walter, SD Freeman, CS Hourigan, X Huang, GN Gonzalez, H Hwang, X Qi, H Kantarjian, F Ravandi. Association of measurable residual disease with survival outcomes in patients with acute myeloid leukemia: a systematic review and meta-analysis. JAMA oncology 6, no. 12 (2020): 1890-1899.

HA Hill*, X Qi*, P Jain, K Nomie, Y Wang, S Zhou, and ML Wang. Genetic mutations and features of mantle cell lymphoma: a systematic review and meta-analysis. Blood advances 4, no. 13 (2020): 2927-2938.

H Lee, JB Wong, B Jia, X Qi, and ER DeLong. Empirical use of causal inference methods to evaluate survival differences in a real‐world registry vs those found in randomized clinical trials. Statistics in medicine 39, no. 22 (2020): 3003-3021.

Y Wang*, S Zhou*, X Qi, F Yang, MJ Maurer, TM Habermann, TE Witzig, ML Wang, and GS Nowakowski. Efficacy of front-line immunochemotherapy for follicular lymphoma: a network meta-analysis of randomized controlled trials. Blood cancer journal 12, no. 1 (2022): 1-9.

X Qi, S Zhou, and M Plummer. On Bayesian modeling of censored data in JAGS. BMC bioinformatics 23, no. 1 (2022): 1-13.

X Qi, S Zhou, Y Wang, CB Peterson. Bayesian sparse modeling to identify high-risk subgroups in meta-analysis of safety data. Research synthesis methods, accepted (2022+)

* indicates co-first authorship