by Usha Govindarajulu | Aug 15, 2024 | Blog
August 15, 2024 In clinical trials that use time to event endpoints, a traditional measure has been using the hazard ratio derived from a Cox proportional hazard regression, but one must satisfy this assumption. Over time, measures that have relaxed this assumption...
by Usha Govindarajulu | Jan 17, 2024 | Biostatistics, Blog, Usha Govindarajulu
January 17, 2023 In an article that appeared in Biometrical Journal, Hoogland et al (2023) had aimed to combine the benefits of flexible parametric survival modeling and regularization in order to improve risk prediction modeling in the context of time-to-event data....
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 6, 2023 | Biostatistics, Blog, Usha Govindarajulu
December 6, 2023 In article that appeared in Biostatistics, Wu et al describe a joint modeling approach of longitduinal data like quality of life and survival data on a retrospective time scale and handling of informative censoring issues in a two arm clinical trial...
by Usha Govindarajulu | Oct 25, 2023 | Biostatistics, Blog, Usha Govindarajulu
October 15, 2023 The authors discussed how to handle model joint survival and longitudinal modeling in the presence of informative censoring caused by informative dropouts by participants in a study. The authors framed this in terms of palliative care studies where...