Regression Analysis of Dependent Current Status Data with Left-Truncation Under Linear Transformation Model

The paper discusses the regression analysis of current status data, which is common in various fields such as tumorigenic research and demographic studies. Analyzing this type of data poses a significant challenge and has recently gained considerable interest. Furthermore, the authors consider an even more difficult scenario where, apart from censoring, one also faces left-truncation and informative censoring, meaning that there is a potential correlation between the examination time and the failure time of interest. The authors propose a sieve maximum likelihood estimation (MLE) method and in the proposed method for inference, a copula-based procedure is applied to depict the informative censoring. Also the authors utilise the splines to estimate the unknown nonparametric functions in the model, and the asymptotic properties of the proposed estimator are established. The simulation results indicate that the developed approach is effective in practice, and it has been successfully applied a set of real data.