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...
by Usha Govindarajulu | May 24, 2023 | Biostatistics, Blog, Professor, Usha Govindarajulu
May 24, 2023 The authors developed a semiparametric maximum likelihood estimation procedure via a kernel smoothed-aided expectation-maximization algorithm. The variances for this were estimated through weighted bootstrap. The authors focused on this for the...
