by Usha Govindarajulu | Aug 28, 2024 | Biostatistics
August 28, 2024 The authors have proposed using imputed data from subdistribution weights to train on machine learning methods for competing risk data. The authors focused on the random survival forest (RSF) method which it stars with a number of bootstrap samples...
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 | Jul 17, 2024 | Biostatistics, Blog, Usha Govindarajulu
July 17, 2024 The authors have written a new article about using average hazard (AH) as compared to restricted mean survival time (RMST) as a measure instead of hazard ratios which are instantaneous measures of effect generally obtained through estimation of a Cox...
by Usha Govindarajulu | Jul 3, 2024 | Biostatistics, Blog, Usha Govindarajulu
July 4, 2024 The authors discussed the use of the standardized mortality ratio (SMR) to compare survival across centers, especially in the context of U.S. kidney transplant center evaluation. The SMR is calculated based on indirect standardization methods in general...
by Usha Govindarajulu | Jun 5, 2024 | Biostatistics, Blog, Usha Govindarajulu
June 5, 2024 The authors have come up with a proportional risk model to assess treatment effect in time-to-event data. One thing they first discussed which is important to note is that relative risk (RR) and hazard ratio (HR) are definitely not the same...
by Usha Govindarajulu | May 22, 2024 | Biostatistics, Blog, Usha Govindarajulu
May 22, 2024 The authors have presented a review of current survival methods and contemporary methods that could help handle some of the issues that arise with current methods. The authors then went through description of current methods. They discussed censoring and...
