A Proposed Method for Conducting a Comprehensive Assessment of Joint Modeling Prediction Accuracy

Music City Center 

Joint modeling of longitudinal and time-to-event data (JM) is a valuable tool in predicting outcomes. Furthermore, predictions are enhanced using super learner joint models (SLJM). Currently, little advice exists regarding comparing prediction accuracy (PA) between models as PA depends on updated biomarker information and the length of the forecast prediction timeframe. Thus, instead of one measure, multiple PA measures need to be considered. We propose an approach to compare PA that accounts for these measures. Our approach is illustrated by analyzing a cohort of 251 patients with advanced non-small cell lung cancer from the RADIOHEAD study. Guardant Reveal, an assay that extracts epigenomic data, produced the temporal biomarkers. Specifically, a JM using an aggregate epigenomic score and a SLJMs leveraging multiple epigenomic components were compared. Comparisons were made using a matrix of Brier scores-one for each model, generated using combinations of current biomarker information and forecasted prediction timeframe windows. Guided by optimism controlled bootstrapped confidence intervals, results highlight our ability to pinpoint instances where SLJM PA outperforms the JM PA and vice versa.

Joint Modeling of Longitudinal and Time-to-Event Data

Super Learner Joint Model

Model Prediction Accuracy

Genomic and Epigenomic ctDNA

Liquid Biopsy Biomarkers

Model Validation 

Section on Medical Devices and Diagnostics