Assessing Model Fit in Joint Models of Longitudinal and Survival Data with Applications to Cancer Clinical Trials

• Activity Number: 100
• Type: Invited
• Date/Time: Monday, August 1, 2016 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics in Epidemiology
• Author(s): Joseph G. Ibrahim* and Ming-Hui Chen and Mark Boye and Wei Shen and Danjie Zhang
• Companies: The University of North Carolina at Chapel Hill and University of Connecticut and Eli Lilly and Company and Eli Lilly and Company and Gilead Pharmaceuticals
• Keywords: Joint Model ; Decomposition ; Gibbs sampling ; Survival Analysis

Abstract

Joint models for longitudinal and survival data now have a long history of being used in clinical trials or other studies in which the goal is to assess a treatment effect while accounting for longitudinal measures such as patient-reported outcomes or tumor response. Compared to using survival data alone, the joint modeling of survival and longitudinal data allows for estimation of direct and indirect treatment effects thereby resulting in improved efficacy assessment. Although global fit indices such as AIC or BIC can be used to rank joint models, these measures as currently defined and used in the literature, do not provide separate assessments of each component of the joint model. In this paper, we develop a novel decomposition of AIC and BIC (i.e., AIC = AIC_Long + AIC_Surv|Long and BIC = BIC_Long + BIC_Surv|Long) that allows us to assess the fit of each component of the joint model, and in particular to assess the fit of the longitudinal component of the model and the survival component separately. Based on this decomposition, we then propose AIC and BIC to determine the importance and contribution of the longitudinal data to the model fit of the survival data.