A pairwise likelihood augmented Cox estimator for left-truncated data.

ABSTRACT: Survival data collected from a prevalent cohort are subject to left truncation and the analysis is challenging. Conditional approaches for left-truncated data could be inefficient as they ignore the information in the marginal likelihood of the truncation times. Length-biased sampling methods may improve the estimation efficiency but only when the underlying truncation time is uniform; otherwise, they may generate biased estimates. We propose a semiparametric method for left-truncated data under the Cox model with no parametric distributional assumption about the truncation times. Our approach is to make inference based on the conditional likelihood augmented with a pairwise likelihood, which eliminates the truncation distribution, yet retains the information about the regression coefficie

SUBMITTER: Wu F 

PROVIDER: S-EPMC6402872 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature