What is the best method for long-term survival analysis - BEKİROĞLU, GÜLNAZ NURAL; ÖZGÜR, EMRAH GÖKAY (My University, 2023)
In the Cox proportional hazards regression model, which is the most commonly used model in survival analysis, the effects of independent variables on survival may not be constant over time and proportionality cannot be achieved, especially when long-term follow-up is required. When this occurs, it would be better to use alternative methods that are more powerful for the evaluation of various effective independent variables, such as milestone survival analysis, restricted mean survival time analysis (RMST), area under the survival curve (AUSC) method, parametric accelerated failure time (AFT), machine learning, nomograms, and offset variable in logistic regression. The aim was to discuss the pros and cons of these methods, especially with respect to long-term follow-up survival studies.
Keywords: Tıp; Sağlık Bilimleri; Temel Tıp Bilimleri; Biyoistatistik ve Tıp Bilişimi; Medicine; Health Sciences; Fundamental Medical Sciences; Biostatistics and Medical Informatics; Klinik Tıp (MED); Klinik Tıp; TIBBİ BİLİŞİM; TIP, GENEL & DAHİLİ; Clinical Medicine (MED); CLINICAL MEDICINE; MEDICAL INFORMATICS; MEDICINE, GENERAL & INTERNAL; Genel Sağlık Meslekleri; Patofizyoloji; Temel Bilgi ve Beceriler; Değerlendirme ve Teşhis; Dahiliye; Tıbbi Bilişim; Aile Sağlığı; Tıp (çeşitli); Genel Tıp; General Health Professions; Pathophysiology; Fundamentals and Skills; Assessment and Diagnosis; Internal Medicine; Health Informatics; Family Practice; Medicine (miscellaneous); General Medicine; Cox regression; long-term follow-up; restricted mean survival time analysis; survival analysis; area under the survival curve method