Dynamic Predictions in Non-Small Cell Lung Cancer Using Joint Modeling of Longitudinal and Time-To-Event Outcomes Data.

Christopher R Pretz¹, Sara Wienke¹, Carin R Espenschied¹, Samantha I Liang², Jiemin Liao¹, Christopher R Cabanski², Jim Hayes¹, Victoria Ngo¹, Enjun Yang², Amar K Das¹

¹Guardant Health, Inc, Palo Alto, California, USA.

²Parker Institute of Cancer Immunotherapy, San Francisco, California, USA.

PubMed DOI

Abstract

Joint modeling (JM) of longitudinal and time-to-event (TTE) data is a powerful statistical technique that elucidates how temporal changes in a biomarker relate to TTE outcomes while accounting for study confounders. The growing use of next-generation sequencing (NGS) in precision oncology, particularly in capturing circulating tumor DNA (ctDNA) methylation via liquid biopsy, offers new opportuniti…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.