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 | 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 | Jan 2, 2025 | Biostatistics, Blog, Usha Govindarajulu
January 1, 2024 The authors were interested in the average treatment effect (ATE) which reflects how the treatment affects the potential outcome. In order to estimate ATE, propensity scores have been adapted for their estimation. such as the inverse probability...
by Usha Govindarajulu | Sep 25, 2024 | Biostatistics, Blog, Usha Govindarajulu
September 25, 2024 This article focused how changing living arrangements was associated with suicide risk using survival analysis along with causal inference. The authors admitted that traditional methods like Cox model analysis were not sufficient to handle...
