by Usha Govindarajulu | Sep 10, 2026 | Biostatistics, Blog, Usha Govindarajulu
September 10, 2026 The authors proposed use of an outcome-adaptive Lasso (OAL) Cox proportional hazards model as a novel variable selection framework causal inference for right censored data in light of a critical limitation in survival analysis where...
by Usha Govindarajulu | Aug 12, 2026 | Biostatistics, Blog, Machine Learning, Professor, Usha Govindarajulu
August 12, 2026 The authors, Qiang et al (2026) proposed a semiparametric modeling framework for interval-censored data using a deep generalized accelerated hazards model (DGAHM) which uses Cox proportional hazard regression or accelerated failure time (AFT)...
by Usha Govindarajulu | Jul 29, 2026 | Biostatistics, Blog, Machine Learning, Professor, Usha Govindarajulu
July 29, 2026 The authors published this second article as a two part series about exploring novel approaches to predicting survival outcomes and evaluating model performance. They also provide R code to help in the implementations of these. They then go through and...
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...
