by Usha Govindarajulu | Sep 11, 2024 | Biostatistics, Blog, Usha Govindarajulu
September 11, 2024 This article focused on joint modeling in the presence of informative censoring in a retrospective analysis in palliative care research. There has been a lack of statistical models to handle longitudinal quality of life (QOL) data and...
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...