by Usha Govindarajulu | Jul 15, 2026 | Biostatistics, Blog, Usha Govindarajulu
July 15, 2026 In the literature, there has been conflicting guidance about when to use multiple testing correction or adjustment. Also, the distinction between a confirmatory vs an exploratory analyses can make all the difference. The guidance has usually been that it...
by Usha Govindarajulu | Jul 1, 2026 | Biostatistics, Blog, Usha Govindarajulu
July 1, 2026 The authors were motivated by comparing future antibiotic resistance levels from different treatments but found some challenges as patients may only survive under one of the treatments. They approached this through a time-to-event analysis and...
by Usha Govindarajulu | Jun 17, 2026 | Biostatistics, Blog, Epidemiology, Usha Govindarajulu
June 17, 2026 In this paper, the authors have described a simple strategy for empirically assessing the plausibility of conditional unconfoundedness (i.e., whether the candidate adjustment set of covariates suffices for confounding adjustment), which does not require...
by Usha Govindarajulu | Jun 3, 2026 | Biostatistics, Blog, Usha Govindarajulu
June 3, 2026 The authors evaluated the performance of targeted maximum likelihood estimation (TMLE) for estimating the average treatment effect in missing data scenarios under varying levels of positivity violations. Directed acyclic graphs (DAGs) have been used in...
by Usha Govindarajulu | May 20, 2026 | Biostatistics, Blog, Usha Govindarajulu
May 20, 2026 Unmeasured confounding has been a long-standing methodological challenge for causal inference and these confounding mechanisms can violate the ignorability assumption that is a bedrock of causal inference. As they say, the profound implications of this...
by Usha Govindarajulu | May 6, 2026 | Biostatistics, Blog, Usha Govindarajulu
May 6, 2026 The win ratio introduced by Pocock et al (2012) has become a popular measure to summarize composite endpoints in clinical trials. It essentially prioritizes important events over lesser ones using an effect size as the relative frequency of wins (more...
