Teng Fei, PhD

Assistant Attending Biostatistician

Teng Fei


Emory University

Current Research Interest

Dr. Fei’s research interests are in the development of latent class analysis methods for joint longitudinal and time-to-event data, and batch effect adjustment methods for omics data. His current research interests also include developing novel statistical methods for microbiome data preprocessing and data analysis. Dr. Fei collaborates with researchers in the Bone Marrow Transplant Service in supporting microbiota-related studies.


Selected peer-reviewed publications:

  1. Fei T, Hanfelt JJ, Peng L. Evaluating the association between latent classes and competing risks outcomes with multi-phenotype data. Biometrics. 2021 Sep 17. PMID: 3453289.
  2. Hart KR, Fei T, Hanfelt JJ. Scalable and robust latent trajectory class analysis using artificial likelihood. Biometrics. 2021 Sep 8; 77(3): 1118-1128. PMID: 32896901. 
  3. Tian S, Switchenko J, Fei T, Press RH, Abugideiri M, Saba NF, Owonikoko TK, Chen AY, Beitler JJ, Curran WJ, Gillespie T, Higgins KA. Survival Advantage of Chemoradiotherapy in Anaplastic Thyroid Carcinoma: Propensity Score Matched Analysis with Multiple Subgroups. Head and Neck. 2020 Mar 18; 42: 678-687. PMID: 31845469.
  4. Fei T, Yu T. scBatch: Batch Effect Correction of RNA-seq Data through Sample Distance Matrix Adjustment. Bioinformatics. 2020 Feb 13; 36(10):3115-3123. PMID: 32053185.
  5. Fei T, Zhang T, Shi W, Yu T. Mitigating the adverse impact of batch effects in sample pattern detection. Bioinformatics. 2018 Mar 1; 34(15):2634-2641. PMID: 29506177.