by Usha Govindarajulu | Jul 19, 2023 | Biostatistics, Usha Govindarajulu
Statistical issues in survival analysis (Part XI) July 19, 2023 The authors, Ning et al, have proposed what they refer to as a “broad class” of “so called” Cox-Aalen transformation models which have elements of both multiplicative and additive covariate effects...
by Usha Govindarajulu | Jul 6, 2023 | Biostatistics, Blog, Usha Govindarajulu
July 5, 2023 The restricted mean survival time (RMST) is a concept that has been invented in survival analysis as an alternative to the hazards ratio, which can be difficult to interpret and also, as derived from Cox model, can have difficulty fitting into the...
by Usha Govindarajulu | Jun 23, 2023 | Blog, Usha Govindarajulu
June 21, 2023 Quantile regression has been around for awhile but has not been used so extensivly in survival analysis, namely for comparisons at the population level. The authors, Williamson et al, proposed analyzing relative survival data using quantile regression...
by Usha Govindarajulu | Jun 9, 2023 | Biostatistics, Blog, Machine Learning, Usha Govindarajulu
June 7, 2023 The authors reviewed methods for complex survival data in terms of frailty models, like the recent advances and R packages. They explored areas of clustered outcomes, competing risks, illness-death model. They did admit their review did not cover two...
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...
