CIMR-Lan Liu

Research Group

Lan Liu

liulan(at)cimrbj.ac.cn

Associate Investigator

Design and Statistical Analysis of Phase I-IV Clinical Trials,

Observational Study, Real World Study

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liulan(at)cimrbj.ac.cn

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B.S., Department of Mathematics, University of Science and Technology of China

Ph.D., Department of Biostatistics, University of North Carolina at Chapel Hill, USA

Work Experience

2024.6-Present

Associate Investigator, Chinese Institutes for Medical Research, Beijing, China

2024.6-Present

Professor, Beijing Institute for Brain Disorders, Beijing, China

2024.6-Present

Professor, Capital Medical University, China

2021.9-2024.5

Associate Professor, School of Statistics, University of Minnesota at Twin Cities, USA

2015.8-2021.8

Assistant Professor, School of Statistics, University of Minnesota at Twin Cities, USA

2013.7-2015.7

Postdoctoral Fellow, Harvard University, USA

Honors and Awards

2013

ICSA Student Paper Award, International Chinese Statistics Association

2012

ORISE Fellowship, Food and Drug Administration

2006-2009

Outstanding Students Scholarship, University of Science and Technology of China

Research Interests

The Liu laboratory at CIMR focuses on statistical theory and applied methodology for biomedical and public health research. The lab aims to integrate methodological innovation with real-world applications in epidemiology, clinical trials, and neuroimaging studies.

The lab's work spans both theoretical development and applied research. On the theoretical side, the lab studies the foundations of statistical inference under complex data-generating mechanisms. On the applied side, the lab collaborates closely with clinicians and interdisciplinary teams to address practical challenges in study design and data analysis, ensuring that methodological advances are motivated by and validated in real biomedical settings.

Major Contributions

1. In the interference problem in epidemiology and sociology, Prof. Liu proved the large sample properties of the two-stage randomization method.

2. In the non-compliance and missing not at random problems in clinical research, Prof. Liu proposed new identification and estimation methods to correct the bias.

3. For the dimension reduction problem in fMRI data, Prof. Liu proposed efficient statistical analysis method based on the feature that voxels of related functional areas are highly correlated.

Representative Publications *：Co-first author; #：Co-corresponding author

Li WL, Lan J, Wei M, Liu L, Hou CB, Qi ZF, Li CH, Jiao LQ, Yang Q, Chen WH, Liu SL, Yue XC, Dong QL, Yuan HC, Gao ZG, Wu XG , Wen CM, Li T, Jiang CC, Li D, Chen ZQ, Shi JF , Shi WC, Yuan JL, Qin YJ, Li BL, Marc Fisher, Feng WW, Liu KJ, Ji XM. Normobaric hyperoxia combined with endovascular treatment for acute ischaemic stroke in China (OPENS-2 trial): a multicentre, randomised, single-blind, sham-controlled trial. The Lancet, 2025, 405: 486-497. DOI: 10.1016/S0140-6736(24)02809-5

Zhao W, Li S, Li C, Wu C, Wang J, Xing L, Wan Y, Qin J, Xu Y, Wang R, Wen C, Wang A, Liu L, Wang J, Song H, Feng W, Ma Q, Ji XM. Effects of Tirofiban on Neurological Deterioration in Patients With Acute Ischemic Stroke: A Randomized Clinical Trial. JAMA Neurology, 2024, 81: 594-602. DOI: 10.1001/jamaneurol.2024.0868

Li W, Wang S, Liu L, Chen J, Lan J, Ding J, Chen Z, Yuan S, Qi Z, Wei M, Ji X. Normobaric Hyperoxia Combined With Endovascular Treatment Based on Temporal Gradient: A Dose-Escalation Study. Stroke, 2024, 55: 1468-1476. DOI: 10.1161/STROKEAHA.123.046106

Cook DR, Forzani L, Liu L^#. Partial least squares for simultaneous reductionof response and predictor vectors in regression. Journal of Multivariate Analysis, 2023, 196: 105163. DOI: 10.1016/j.jmva.2023.105163

Liu L*, Li W^#, Su Z, Cook DR, Vizioli L, Yacoub E. The Efficiency Boosting via Envelope Chain for task-invoked fMRI study. Scandianvian Journal of Statistics, 2021. DOI: 10.1111/sjos.12522

Liu L, Tchetgen Tchetgen E. Indirect Adjustment for Homophily Bias with a Negative Control Variable in Peer Effect Analysis. Biometrics, 2021, 78: 668-678. DOI: 10.1111/biom.13483

Li W, Gu Y, Liu L^#. Demystifying a Class of Multiple Robust Estimators. Biometrika, 2020, 207: 919-933. DOI: 10.1093/biomet/asaa026

Liu L^#, Hudgens MG, Becker-Dreps S. On Inverse Probability Weighted Estimators in the Presence of Interference. Biometrika, 2016, 103: 829-842. DOI: 10.1093/biomet/asw047

Liu L, Hudgens MG. Large Sample Randomization Inference of Causal Effects in the Presence of Interference. Journal of the American StatisticalAssociation (JASA) Theory and Methods Section, 2014, 109: 288-301. DOI: 10.1080/01621459.2013.844698