Unsupervised clustering using multiple correspondence analysis reveals clinically-relevant demographic variables across multiple gastrointestinal cancers
Kramer R, Rhodin K, Therien A, Raman V, Eckhoff A, Thompson C, Tong B, Blazer D, Lidsky M, D’Amico T, Nussbaum D. Unsupervised clustering using multiple correspondence analysis reveals clinically-relevant demographic variables across multiple gastrointestinal cancers. Surgical Oncology Insight 2024, 1: 100009. DOI: 10.1016/j.soi.2024.100009.Peer-Reviewed Original ResearchNational Cancer DatabaseStage II/III esophageal cancerStage II/III gastric cancerStage III colon cancerIII colon cancerEsophageal cancerDemographic variablesMultiple correspondence analysisGastric cancerAt-risk populationsGastrointestinal malignanciesMultiple gastrointestinal cancersColon cancerUnsupervised clustering of patientsEducation quartileClusters of patientsIncome quartileKaplan-Meier survival methodInsurance typeCohort of patientsOutcomes researchHigher incomePrivate insuranceExamination outcomesTreatment pathways
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply