Jing Zhou, PhD

“I am passionate about using big data and cutting-edge statistical methods to address health-related problems that can help people live a healthier life through better health care.”

Jing Zhou, PhD

Biostatistician, Kaiser Permanente Washington Health Research Institute

Biography

Jing Zhou, PhD, is a biostatistician with expertise in Bayesian modeling, machine learning methods, latent class analysis, dimension reduction and variable selection methods, group sequential design in clinical trials, etc. She is particularly interested in leveraging big data, applying appropriate methods or developing novel methods to address challenging health-related research problems in helping patients to achieve better health care.

Before joining KPWHRI, she was a statistician in Genentech/Roche, a world-leading pharmaceutical company specializing in oncology. She worked in an interdisciplinary environment collaborating with scientific partners and project teams in multiple clinical trials. She contributed as the lead statistician in designing, monitoring, analyzing clinical trials leading to concise interpretations and conclusions, which lead to scientific publications and drug approvals. During her second year at Genentech, she mentored an intern in developing a new Bayes method to effectively borrow information for an early-phase multi-cohort trial.

Dr. Zhou completed her PhD in Biostatistics at the University of North Carolina at Chapel Hill. Her dissertation research proposed methods to identify the associations between birth defects and parental and environmental factors in the National Birth Defects Prevention Study from a huge national database. In this research, she developed innovative non-parametric Bayes methods to conquer the high-dimension curse and detect significant risk factors to multiple birth defects.

Research interests and experience

  • Biostatistics

    Bayesian modeling, dimension reduction, variable selection, longitudinal analysis, machine learning methods

  • Cancer

    Biostatistics, breast cancer, melanoma, lung cancer, clinical trials, adaptive design, survival analysis

  • Women's Health

    Biostatistics, breast cancer, clinical trials, observational study, disease during pregnancy, birth defects

Recent publications

Soria JC, Adjei AA, Bahleda R, Besse B, Ferte C, Planchard D, Zhou J, Ware J, Morrissey K, Shankar G, Lin W, Schutzman JL, Dy GK, Groen HJM. A phase IB dose-escalation study of the safety and pharmacokinetics of pictilisib in combination with either paclitaxel and carboplatin or pemetrexed and cisplatin in patients with advanced non-small cell lung cancer. Eur J Cancer. 2017 Nov;86:186-196. doi: 10.1016/j.ejca.2017.08.027. Epub 2017 Oct 6. PubMed

Leong S, Moss RA, Bowles DW, Ware JA, Zhou J, Spoerke JM, Lackner MR, Shankar G, Schutzman JL, van der Noll R, Voest EE, Schellens JHM. A phase I dose-escalation study of the safety and pharmacokinetics of pictilisib in combination with erlotinib in patients with advanced solid tumors. Oncologist. 2017 Aug 10. pii: theoncologist.2017-0090. doi: 10.1634/theoncologist.2017-0090. [Epub ahead of print]. PubMed

Vuylsteke P, Huizing M, Petrakova K, Roylance R, Laing R, Chan S, Abell F, Gendreau S, Rooney I, Apt D, Zhou J, Singel S, Fehrenbacher L. Pictilisib plus paclitaxel for the treatment of hormone receptor-positive, HER2-negative, locally recurrent, or metastatic breast cancer: interim analysis of the multicentre, placebo-controlled, phase II randomised PEGGY studypeggy study Ann Oncol. 2016 Nov;27(11):2059-2066. Epub 2016 Aug 29. PubMed

Zhou J, Herring AH, Bhattacharya A, Olshan AF, Dunson DB, National Birth Defects Prevention Study. Nonparametric Bayes modeling for case control studies with many predictors. Biometrics. 2016 Mar;72(1):184-92. doi: 10.1111/biom.12411. Epub 2015 Sep 22. PubMed

Zhou J, Adewale A, Shentu Y, Liu J, Anderson K. Information-based sample size re-estimation in group sequential design for longitudinal trials. Stat Med. 2014 Sep 28;33(22):3801-14. doi: 10.1002/sim.6192. Epub 2014 May 4. PubMed

Zhou J, Bhattacharya A, Herring AH, Dunson DB. Bayesian factorizations of big sparse tensors. J Am Stat Assoc.110(512):1562-1576.

 

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