by Usha Govindarajulu | Mar 13, 2024 | Biostatistics, Blog, Usha Govindarajulu
March 13, 2024 New immunotherapies for cancer have shown to have different treatment effects often with delay compared to other cytotoxic treatments and, therefore, this has called for different ways of modeling the survival curves. The usual log-rank test has relied...
by Usha Govindarajulu | Feb 28, 2024 | Blog
February 26, 2024 TABLE 1. Summary of the available methods for survival regression with competing risks (CR). Model Type Proportional hazards (PH) High dimensions (�) Missing data Approaches based on a cause-specific hazard specification Cox proportional CS hazard...
by Usha Govindarajulu | Feb 14, 2024 | Blog
In an article in Biometrical Journal, Heinze et al discussed phases of methodological research in biostatistics. The authors of this publication are all members of an international STRATOS (STRengthening Analytical Thinking for Observational Studies) Intiative, whose...
by Usha Govindarajulu | Jan 31, 2024 | Biostatistics, Blog, COVID-19, Usha Govindarajulu
January 31, 2024 In an article that appeared in Biometrical Journal, Hu described a new random-intercept accelerated failure time model with Bayesian additive regression trees (riAFT-BART), which can be used to draw causal inferences on population treatment effectts...
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...
