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...
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...
