by Usha Govindarajulu | Mar 16, 2023 | Biostatistics, Blog, COVID-19, Healtcare, Machine Learning, Usha Govindarajulu
March 15, 2023 In a recent article that appeared in Science News, a teach from Johns Hopkins University discussed their proof of concept study to show how their machine learning system used electronic health data to make predictions to determine who would be most...
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...
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 | Feb 2, 2023 | Biostatistics, Blog
February 1, 2023 In an article recently published in Biometics, Huang et al discuss how to adjust for publication bias in meta analyses via using inverse probability weighted adjusted measures of meta-analyses. The authors were motivated by the fact that...
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...
