A pairwise pseudo-likelihood approach for regression analysis of doubly truncated data

Double truncation commonly occurs in astronomy, epidemiology and economics. Compared to one-sided truncation, double truncation, which combines both left and right truncation, is more challenging to handle and the methods for analyzing doubly truncated data are limited. For the situation, a common approach is to perform conditional analysis conditional on truncation times, which is simple but may not be efficient. Corresponding to this, we propose a pairwise pseudo-likelihood approach that aims to recover some information missed in the conditional methods and can yield more efficient estimation. The resulting estimator is shown to be consistent and asymptotically normal. An extensive simulation study indicates that the proposed procedure works well in practice and is indeed more efficient than the conditional approach. The proposed methodology applied to an AIDS study.