Bootstrap Based Diagnostics for Survival Regression Model with Interval and Right-Censored Data

This research proposes a new approach based on the bias-corrected bootstrap harmonic mean and random imputation technique to obtain the adjusted residuals (Hboot) when a survival model is fit to right- and interval-censored data with covariates. Following that, the model adequacy and influence diagnostics based on these adjusted residuals, case deletion diagnostics, and the normal curvature are discussed. Simulation studies were conducted to assess the performance of the parameter estimate and compare the performances of the traditional Cox-Snell (CS), modified Cox-Snell (MCS) and Hboot at various censoring proportions (cp) and samples sizes ($n$) using the log-logistic and extreme minimum value regression models with right- and interval-censored data. The results clearly indicated that Hboot outperformed other residuals at all levels of cp and $n$, for both models. The proposed methods are then illustrated using real data set from the COM breast cancer data. The results indicate that the proposed methods work well to address model adequacy and identify potentially influential observations in the data set.