40: What Goes Wrong in Prediction Models if you Ignore Mortality?

Music City Center 

Sudden death (SD) is a primary cause of death in the US. While clinical guidelines recommend treating patients of all ages at high risk of SD with an implantable cardiac defibrillator (ICD), questions have been raised regarding benefits for older patients where the force of mortality is high. To investigate this, the PIPER-ICD Study examines the end-of-life experience among older ICD patients to identify longitudinal markers predictive for treatment success. Based on the PIPER-ICD data and extensive simulations, we illustrate how predictions of marker trajectories go wrong if they ignore mortality. Crucially, standard methods fail to acknowledge the joint distribution of the marker and mortality: linear mixed models and joint models imply a "pretend reality" in which patients are considered immortal; marginal methods marginalize over death, treating mortality as a statistical nuisance instead of an outcome of inherent clinical value. We also discuss the clinical implications of this failure, specifically in relation to how health care decisions are made.

Mortality

Longitudinal Marker

Joint models

Marginal models

Linear mixed models

Prediction 

Section on Statistics in Epidemiology