by Usha Govindarajulu | Nov 6, 2024 | Biostatistics, Blog, Usha Govindarajulu
November 6, 2024 The literature has been lacking in ways to model sufficient follow-up when analyzing survival data with a cure fraction, which means that the right extreme of the censoring time distribution is larger than that of the survival distribution of the...
by Usha Govindarajulu | Oct 24, 2024 | Biostatistics, Blog, Usha Govindarajulu
October 23, 2024 In terms of the Cox proportional hazards model, adding in a lasso feature for variable selection or other penalized method was typically done in the partial likelihood. Their method has sought to add the lasso penalty into the full likelihood. As the...
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 | 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....
