Accelerated failure time model under dependent truncated data

This paper delves into the accelerated failure time model within the framework of dependent truncation data and leverages the copula model to establish correlations within the dataset. Building upon the work of Chaieb et al. (2006), who utilized the copula-graphic method to estimate survival functions and proposed an approach for estimating correlation parameters, we further extend the methodology by introducing two distinct estimation techniques for regression parameters. The first method involves parameter evaluation through the calculation of the area between survival curves, while the second method employs the weight of survival jump in conjunction with the least squares approach to estimate regression parameters. We evaluate the efficacy of these proposed estimation procedures through simulation studies and conduct a comparative analysis between the two approaches. Furthermore, we apply these methodologies to two real-world datasets, providing insights into their practical applicability. Through this analysis, we gain a deeper understanding of how these approaches can be effectively utilized in real-world scenarios.