Statistical issues in survival analysis (Part XVVVV)
July 31, 2024 The authors discussed applications of artificial intelligence (AI)/machine learning algorithms in survival analysis to skin cancer research publications. They reported on 16 such publications. Supervised machine learning (ML) would have used existing data for future dataset predictions. They mentioned several articles that applied supervised ML and then used Cox proportional hazard models to assess the effect of particular risk factors on survival time. It wasn’t clear from their description how the ML and the standard Cox modeling were tied together in these articles, but they said the supervised ML expanded upon previous statistical methods. They mentioned an article that used an alternative Cox loss function for melanoma survival prediction. Unsupervised machine learning has used clustering to analyze the “unlabeled” datasets and then relied on machine learning to make unknown associations. They then mentioned the survival analysis in some of the articles they referenced included Kaplan-Meier analysis. Also hazard ratios were used and allowed the authors to overcome inadequate statistical power. They concluded that unsupervised learning aided to understand survival and prognostic outcomes. To summarize in their Figure 1, the supervised learning came up with predictive models based on labeled data using classification and/or regression while the unsupervised learning came up with pattern and structure recognition of unlabeled data by using clustering, association, or dimensionality reduction or a combination of these methods.