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Updated: May 30, 2025
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Penalized landmark supermodels (penLM) for dynamic prediction for time-to-event outcomes in high-dimensional data
Anya H Fries1,2, Eunji Choi2,3, Summer S Han4,5
1Department of Management Science and Engineering, Stanford University, Stanford, CA, 94304, USA.
Background:
To effectively monitor long-term outcomes among cancer patients, it is critical to accurately assess patients' dynamic prognosis, which often involves utilizing multiple data sources (e.g., tumor registries, treatment histories, and patient-reported outcomes). However, challenges arise in selecting features to predict patient outcomes from high-dimensional data, aligning longitudinal measurements from multiple sources, and evaluating dynamic model performance.
Methods:
We provide a framework for dynamic risk prediction using the penalized landmark supermodel (penLM) and develop novel metrics ([Formula: see text] and [Formula: see text]) to evaluate and summarize model performance across different timepoints. Through simulations, we assess the coverage of the proposed metrics' confidence intervals under various scenarios. We applied penLM to predict the updated 5-year risk of lung cancer mortality at diagnosis and for subsequent years by combining data from SEER registries (2007-2018), Medicare claims (2007-2018), Medicare Health Outcome Survey (2006-2018), and U.S. Census (1990-2010).
Results:
The simulations confirmed valid coverage (~ 95%) of the confidence intervals of the proposed summary metrics. Of 4,670 lung cancer patients, 41.5% died from lung cancer. Using penLM, the key features to predict lung cancer mortality included long-term lung cancer treatments, minority races, regions with low education attainment or racial segregation, and various patient-reported outcomes beyond cancer staging and tumor characteristics. When evaluated using the proposed metrics, the penLM model developed using multi-source data ([Formula: see text]of 0.77 [95% confidence interval: 0.74-0.79]) outperformed those developed using single-source data ([Formula: see text]range: 0.50-0.74).
Conclusions:
The proposed penLM framework with novel evaluation metrics offers effective dynamic risk prediction when leveraging high-dimensional multi-source longitudinal data.
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