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 14, 2023 | Biostatistics, Usha Govindarajulu
September 13, 2023 In their paper, the authors, Wang et al focused on estimation of casual effects of a treatment on survival outcomes with censoring with propensity score (PS) adjustment. They also used alternative measures of survival like restricted average causal...
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 | 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 | Mar 2, 2023 | Biostatistics, Usha Govindarajulu
February 28, 2023 The author, Olivier Bouaziz, developed a new method to calculate pseudo-observations for the survival function and also the restricted mean survival time (RMST) that used formulas which were based on the original estimators and not on the...