Inverse probability weighting methods for Cox regression with right-truncated data.

ABSTRACT: Right-truncated data arise when observations are ascertained retrospectively, and only subjects who experience the event of interest by the time of sampling are selected. Such a selection scheme, without adjustment, leads to biased estimation of covariate effects in the Cox proportional hazards model. The existing methods for fitting the Cox model to right-truncated data, which are based on the maximization of the likelihood or solving estimating equations with respect to both the baseline hazard function and the covariate effects, are numerically challenging. We consider two alternative simple methods based on inverse probability weighting (IPW) estimating equations, which allow consistent estimation of covariate effects under a positivity assumption and avoid estimation of baseline hazar

SUBMITTER: Vakulenko-Lagun B 

PROVIDER: S-EPMC7162718 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature