Kay See Tan, PhD

Associate Attending Biostatistician

Kay See Tan, PhD

Associate Attending Biostatistician
Kay See Tan


University of Pennsylvania

Current Research Interest 

Dr. Tan’s research focuses on the joint modeling of longitudinal and recurrent-event data, particularly when an adverse event triggers outcome assessment in-between prescheduled follow-up visits. Her methods estimate covariate-outcome associations when observation times may depend on outcome values and unmeasured patient-level characteristics to ensure patients with more observations are not over-represented in the analysis. She is also interested in methodological issues pertaining to patient-reported measures such as perioperative pain and anxiety, propensity score methods in the presence of missing data, cause of death misclassifications, and statistical issues surrounding critical care outcomes. Dr. Tan serves as the biostatistician for the Department of Anesthesiology and Critical Care as well as the Thoracic Surgery Service. She is the lead statistician of preclinical and clinical studies that investigate immune-related biomarkers to predict clinical outcomes among lung cancer patients. She is also a member of the Cellular Therapeutic Center at MSK focusing on the designs and analyses of clinical trials utilizing CAR T cells and other immunotherapies for solid tumors. In addition, Dr. Tan is actively involved in the areas of statistical literacy and statistics education and co-directs the department’s Quantitative Science Undergraduate Research Experience (QSURE) summer internship program.She also serves as the associate director of the Biostatistics, Epidemiology and Research Design (BERD) Core at the Weill Cornell Clinical and Translational Science Center.


Selected peer-reviewed publications:

  1. Tan KS, Elkin EB, Satagopan JM. A model for an undergraduate research experience program in quantitative sciences. Journal of Statistics and Data Science Education. 2022 Jan 2;30(1):65-74.
  2. Tan KS. A Concern About Survival Time as an Endpoint in COVID-19 Clinical Trials. Clinical trials (London, England). 2020 Oct;17(5):505.
  3. Tan KS, Hsu M, Adusumilli PS.  Pathologic node-negative lung cancer: Adequacy of lymph node yield and a tool to assess the risk of occult nodal disease. Lung Cancer. 2022 Dec 1;174:60-6.
  4. Gross DJ, Chintala NK, Vaghjiani RG, Grosser R, Tan KS, Li X, Choe J, Li Y, Aly RG, Emoto K, Zheng H, Adusumilli PS. Tumor and tumor-associated macrophage programmed death-ligand 1 expression is associated with adjuvant chemotherapy benefit in lung adenocarcinoma. Journal of Thoracic Oncology. 2022 Jan 1;17(1):89-102.
  5. Restle D, Dux J, Li X, Byun AJ, Choe J, Li Y, Vaghjiani R, Thomas C, Misawa K, Tan KS, Jones D, Chintala N, Adusumilli P. Organ-specific heterogeneity in tumor-infiltrating immune cells and cancer antigen expression in primary and autologous metastatic lung adenocarcinoma. Journal for ImmunoTherapy of Cancer. 2023 Jun 1;11(6):e006609.

View a full listing of Kay See Tan’s journal articles.


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Kay See Tan discloses the following relationships and financial interests:

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