by Usha Govindarajulu | Aug 27, 2026 | Artificial Intelligence, Biostatistics, Blog, Machine Learning, Professor, Usha Govindarajulu
August 26, 2026 In their article, Lang et al (2026) discuss the issues of assessing quality in preclinical research statistically in the age of the emerging use of artificial intelligence (AI). They aimed to show how large language models (LLMs) and more generally...
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 | Jun 4, 2025 | Biostatistics, Blog, Professor, Usha Govindarajulu
June 4, 2025 The authors discussed a weighted repeated measures correlation coefficient which could work even in the presence of missing data. The Pearson correlation coefficient cannot be used in these data due to violations of independent data. Also, some measures...
by Usha Govindarajulu | May 21, 2025 | Biostatistics, Blog, Professor, Usha Govindarajulu
May 21, 2025 The authors focused on adjustment for conditional bias in hazard ratios from overall survival (OS) in both interim and final analysis in trial where the overall hierarchical strategy was applied. They first showed a conditional bias (CB) adjusted...
