Augmented estimation for t-year survival with censored regression models.

ABSTRACT: Reliable and accurate risk prediction is fundamental for successful management of clinical conditions. Estimating comprehensive risk prediction models precisely, however, is a difficult task, especially when the outcome of interest is time to a rare event and the number of candidate predictors, p, is not very small. Another challenge in developing accurate risk models arises from potential model misspecification. Time-specific generalized linear models estimated with inverse censoring probability weighting are robust to model misspecification, but may be inefficient in the rare event setting. To improve the efficiency of such robust estimation procedures, various augmentation methods have been proposed in the literature. These procedures can also leverage auxiliary variables such as interm

SUBMITTER: Zheng Y 

PROVIDER: S-EPMC5592155 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature