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 | Aug 13, 2025 | Biostatistics, Blog, Usha Govindarajulu
August 13, 2025 The authors looked at a method called survival average causal effect (SACE) for dealing with situations when continuous outcome measurements are truncated by death and cause problems for estimating unbiased treatment effects in randomized controlled...
by Usha Govindarajulu | Jul 2, 2025 | Biostatistics, Blog, Usha Govindarajulu
July 2, 2025 The importance of evaluating a longitudinal biomarker in survival analysis for overall or disease-free survival can be important. The authors have defined a new joint model for a longitudinal biomarker and a time-to-event endpoint, taking into account...
by Usha Govindarajulu | Apr 23, 2025 | Biostatistics, Blog, Usha Govindarajulu
April 23, 2025 The authors have discussed a sample size calculation for restricted mean survival time (RMST) in augmented tests. The RMST was developed as an alternative measure of survival that is non-parametric and does not reply on parametric constraints. It had...
by Usha Govindarajulu | Apr 9, 2025 | Biostatistics, Blog, Usha Govindarajulu
April 9, 2025 The authors have generalized the pseudo-observations approach to bivariate survival data subject to right censoring. Pseudo-observations approach was originally developed by Anderson (2003) for estimating covariates effect on time-to-event data. This...
