A Nonparametric Test for Interaction Effects in Regression Model for Right-Censored Survival Data

• Activity Number: 296
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Nonparametric Statistics
• Author(s): MinJae Lee*+ and Mohammad Hossein Rahbar
• Companies: University of Texas Health Science Center at Houston and University of Texas Health Science Center at Houston
• Keywords: Kaplan-Meier ; Survival Time ; Censoring ; Cox Proportional Hazard Model ; Interactions ; PROMMTT

Abstract

We propose a non-parametric test for interactions when we are concerned with investigation of the simultaneous effects of two or more factors in a regression model with right censored survival data. Through a simulation study we evaluate the impact of varying censoring rates and the presence of informative censoring on these types of estimators and power of our new proposed method with that of using the Cox Proportional Hazard (PH) model and Censored Median Regression model given the different scenarios. Finally, we illustrate application of our proposed method to real life data from Prospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study to test an interaction effect between mechanism of injury and study sites using median time for a trauma patient to receive 3 units of red blood cells. Our results indicate that when the assumption of PH is not met, our procedure performs better than both Cox model and Censored Median Regression model given moderate levels of censoring. Our simulations also indicate that our estimation is not seriously impacted by the presence of informative censoring.