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 | Nov 24, 2023 | Biostatistics, Blog, Usha Govindarajulu
Statistical issues in survival analysis (Part XVIII) November 22, 2023 In this article that appeared in Journal of Probability and Statistics, the authors described a new test as an alternative to the log-rank test to compare late differences between survival curves. ...
by Usha Govindarajulu | Nov 11, 2023 | Biostatistics, Blog
November 9, 2023 In this article that appeared in a special issue in the Biometrical Journal, Morelle et al described a joint modeling approach to account for the longitudinal followup of patients at subsequent hospitalization or competing risk. While most prognostic...
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...
by Usha Govindarajulu | Oct 11, 2023 | Biostatistics, Blog, Usha Govindarajulu
October 11, 2023 In this recent article by Hoogland et al, the authors have proposed combining flexible parametric survival modeling with regularization in order to improve risk prediction modeling for time-to-event data and, thus, they made a unified regularation...
by Usha Govindarajulu | Sep 27, 2023 | Biostatistics, Blog, Usha Govindarajulu
September 25, 2023 The authors, Hanke et al, discussed variable selction methods for linear regression and that the best subset selection (BSS) is not always the best choice. After Bertsimas et al (2016) pushed BSS as a mixed-integer optimization problem (MIO) so...
