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...
by Usha Govindarajulu | Jun 20, 2024 | Blog, Usha Govindarajulu
The goals of this paper were to provide a set of conditions for inverse probability weights (IPW) to target a subpopulation of the patients who have clinical equipose, establish a relationship between matching weights and overlap weight estimators, and use the beta...
by Usha Govindarajulu | Jan 3, 2024 | Biostatistics, Blog, Usha Govindarajulu
January 3, 2023 In an article that appeared in Biometrical Journal, Le Bourdonnec et al (2023) discussed a method to address unmeasured confounding in cohort studies by an instrumental varible (IV) method for a time fixed expousre on an outcome trajectory, repeatedly...
by Usha Govindarajulu | Dec 20, 2023 | Biostatistics, Usha Govindarajulu
December 20, 2023 In article that appeared in Statistics in Medicine, Denz et al explored different methods for modeling of adjusted survival curves especially in observational studies, which tend to have issues with confounding. The authors also brought in...
