A pairwise pseudo-likelihood approach for the additive hazards model with left-truncated and interval-censored data

Left-truncated and interval-censored data occur com-monly and some approaches have been proposed in the lit-erature for their analysis. However, most of the existing methods are based on the conditional likelihood given left -truncation times, which can be inefficient since the infor-mation in the marginal likelihood of the truncation times is ignored. To address this, in this paper, a pairwise pseudo -likelihood augmented estimation approach is proposed un-der the additive hazards model that can fully make use of all available information. The derived estimator is shown to be consistent and asymptotically normal, and simulation studies suggest that the proposed method works well and provides a substantial efficiency gain over the conditional approach. In addition, the method is applied to a set of real data arising from an AIDS cohort study.