by Usha Govindarajulu | May 24, 2023 | Biostatistics, Blog, Professor, Usha Govindarajulu
May 24, 2023 The authors developed a semiparametric maximum likelihood estimation procedure via a kernel smoothed-aided expectation-maximization algorithm. The variances for this were estimated through weighted bootstrap. The authors focused on this for the...
by Usha Govindarajulu | May 10, 2023 | Biostatistics, Blog, Usha Govindarajulu
May 10, 2023 The authors Sun et al present a semi-competing risks framework using a flexible two-parameter copula-based semiparametric transformation model with interval censoring and left truncation. The semi-parametric transformation model allow both proportional...
by Usha Govindarajulu | Apr 12, 2023 | Biostatistics, Blog, Usha Govindarajulu
April 12, 2023 The authors, Ozaki and Ninomiya have presented how information criteria, like AIC, can be adapted when detecting change points in a Cox proportional hazards regression. The authors motivated the use of change-points due to onset of efficacy being...
by Usha Govindarajulu | Feb 15, 2023 | Biostatistics, Blog, New York, Usha Govindarajulu
February 15, 2023 In an article recently published in the Biometrical Journal, Magir and Jimenez discuss delayed separation of survival curves with proposals for a new weighted log-rank test to handle this through stratification. This common problem has occurred in...
by Usha Govindarajulu | Jan 19, 2023 | Biostatistics, Blog, Usha Govindarajulu
January 16, 2023 In an article recently published online in The Biometrical Journal, Handorf et al seek to understand what is the best way to model survival data in the presence of non-proportional hazards, perhaps from the effect of confounding variables. The...
