Efficient estimation for the additive hazards model in the presence of left-truncation and interval censoring

The additive hazards model is one of the most commonly used regression models in failure time data analysis and many authors have discussed its inference under various situations (Lin and Ying, 1994; Lin et al., 1998; Zeng et al., 2006; Wang et al., 2010). In this paper, we consider it when one faces left-truncated and interval-censored data, which often occur in, for example, epidemiological and medical follow-up studies. For inference, an efficient sieve maximum likelihood estimation procedure is developed and assessed by simulation studies, which indicate that the proposed method works well in practical situations. An illustrative example is also provided.

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