by Usha Govindarajulu | Jan 30, 2026 | Biostatistics, Blog, Healtcare, Machine Learning, Professor, Usha Govindarajulu
January 28, 2026 Machine learning (ML) offers opportunities to overcome limitations of conventional survival analyses, which are commonly found in cancer studies. It becomes unclear whether they consistently outperform traditional statistical methods and whether one...
by Usha Govindarajulu | Jun 17, 2025 | Biostatistics, Blog, Machine Learning, Usha Govindarajulu
June 18, 2025 The goal of this paper was to bridge gaps in understanding how to use machine learning (ML) methods for survival by presenting a comprehensive study comparing various ML methods for dynamic survival analysis. They sought to provide researchers and...
by Usha Govindarajulu | Jul 31, 2024 | Biostatistics, Blog, Usha Govindarajulu
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...
by Usha Govindarajulu | Feb 28, 2024 | Blog
February 26, 2024 TABLE 1. Summary of the available methods for survival regression with competing risks (CR). Model Type Proportional hazards (PH) High dimensions (�) Missing data Approaches based on a cause-specific hazard specification Cox proportional CS hazard...
by Usha Govindarajulu | Jun 9, 2023 | Biostatistics, Blog, Machine Learning, Usha Govindarajulu
June 7, 2023 The authors reviewed methods for complex survival data in terms of frailty models, like the recent advances and R packages. They explored areas of clustered outcomes, competing risks, illness-death model. They did admit their review did not cover two...
