Abstract Number:
2255
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Siddharth Roy (1), Anindya Roy (2), Paul Albert (3), Danping Liu (4)
Institutions:
(1) UMBC, N/A, (2) University of Maryland-Baltimore County, N/A, (3) National Cancer Institute, N/A, (4) National Institutes of Health, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
We compare three common approaches to identify longitudinal biomarkers associated with survival outcomes: joint models, conditional models, and time dependent Cox models. For cancer biomarkers, associations can be acute, meaning longitudinal trajectory may change sharply just before diagnosis or have more long-term associations for risk estimation, such as differences in levels or slopes. Each of the three methods uses a different modeling framework for the joint density of the biomarkers and survival time and thus has different advantages and disadvantages for detecting local and long-term associations. The current project investigates the three approaches' power and type I error under different data generation schemes to motivate further methods development for longitudinal biomarker screening in cancer studies. We found that the conditional model can effectively disentangle the acute and long term effects. We also see the standard joint model with random intercept and slope does not identify acute effects well, but has slightly higher power than the Cox model for long term effects.
Keywords:
Time Dependent Cox Model|Joint Model|Longitudinal Biomarker Screening|Early Detection|Risk Prediction|Conditional Model
Sponsors:
Biometrics Section
Tracks:
Risk Prediction
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