CIMR-Lan Liu

Research Group

Lan Liu

gliulan1815(at)outlook.com

Associate Investigator

gliulan1815(at)outlook.com

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

B.S. in Pure Mathematics, University of Science and Technology of China, China

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 Univesity, 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

Research Direction

The Liu laboratory at CIMR works on statistical methodology development including causal inference, missing data, sufficient dimension reduction, statistical applications including the design and analysis of Phase I--III clinical trials, and various observational studies.

Major Research Projects

1. Causal Inference (herd immunity, surrogate paradox, mediation analysis, negative control, instrumental variables, propensity score methods, robust analsis)

2. Sufficient Dimension Reduction with application in high dimensional data such as fMRI and longitudinal data

3. Design and Analysis of Clinical Trials (such as BAOCHE trial, published in NEJM; TREND trial, published in Lancet Neurology)

Major Contributions

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

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

Liu, M. G. Hudgens. Large Sample Randomization Inference of Causal Effects in the Presence of Interference. Journal of the American StatisticalAssociation (JASA) Theory and Methods Section, 2014, 109(505):288-301. DOI: 10.1080/01621459.2013.844698

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

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

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

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

Liu, E. Tchetgen Tchetgen. Indirect Adjustment for Homophily Bias with aNegative Control Variable in Peer Effect Analysis. Biometrics, 2021.DOI: 10.1111/biom.13483

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

Zhao; S. Li; C. Li; C. Wu; J. Wang; L. Xing; Y. Wan; J. Qin; Y. Xu; Wang; C. Wen; A. Wang; L. Liu; J. Wang; H. Song; W. Feng; Q. Ma;X. Ji. Safety and Efficacy of Intravenous Tirofiban for Early Neurological Deterioration Prevention in Patients with Acute Ischemic Stroke (TREND):A Multicentre, Prospective, Randomised, Open-Label, Blinded-endpoint Trial.The Lancet Neurology, 2024. DOI:10.4103/bc.bc_93_23