Regression Analysis of Dependent Current Status Data with Left Truncation

Current status data are encountered in a wide range of applications, including tumorigenic experiments and demographic studies. In this case, each subject has one observation, the only information obtained is whether event interest happened at moment observation. addition to censoring, truncating also very common practice. This paper examines regression analysis current with informative censoring times, considering presence left truncation. addition, we propose an inference approach based on sieve maximum likelihood estimation (SMLE). A copula-based used describe relationship between failure time time. The spline function employed approximate unknown nonparametric function. We have established asymptotic properties proposed estimator. Simulation studies suggest that developed procedure works well applied method real dataset derived from AIDS cohort research.