Papers
8
Total Citations
262
H-Index
7
About
Kay See Tan is a leading biostatistician and health outcomes researcher whose work centers on surgical quality of life, robotic-assisted procedures, and perioperative risk prediction in thoracic and head-and-neck oncology. Her major contributions include landmark studies on patient-reported outcomes after robotic-assisted minimally invasive versus open esophagectomy—her 2019 paper (78 citations) and its two-year follow-up (26 citations) provide critical evidence on recovery trajectories. She also pioneered risk stratification in HPV-related oropharyngeal cancer, with a 2014 study (69 citations) identifying predictors of treatment failure after transoral robotic surgery. Tan’s research on operative time as a predictor of complications after pulmonary lobectomy (40 citations) has informed surgical safety protocols. More recently, she has advanced the field of robotic-assisted bronchoscopy, characterizing learning curves (20 citations) and demonstrating feasibility for biomarker identification (16 citations) and histopathologic subtyping of lung adenocarcinoma. Her work consistently bridges rigorous statistical methodology with clinically actionable insights, shaping best practices in minimally invasive thoracic surgery.
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