Optimizing personalized screening intervals for clinical biomarkers using extended joint models
Nobuhle Nokubonga Mchunu, Henry Mwambi, Tarylee Reddy, Nonhlanhla Yende-Zuma, Dimitris Rizopoulos
TL;DR
This paper introduces a new method for personalizing medical screening intervals for HIV and TB patients using advanced statistical models that improve accuracy and adaptability.
Contribution
The study introduces a novel use of super learning and multivariate joint models with censored longitudinal data to optimize personalized screening intervals.
Findings
The multivariate joint model using CD4 count and viral load profiles was identified as the optimal predictor of death.
Screening intervals for stable patients can be extended to 10.3 months, while those with deteriorating health require more frequent monitoring.
The approach is adaptable and can be applied to other diseases beyond HIV/TB co-infection.
Abstract
This research advances joint modeling and personalized scheduling for HIV and TB by incorporating censored longitudinal outcomes in multivariate joint models, providing a more flexible and accurate approach for complex data scenarios. Inspired by the SAPiT study, we deviate from standard model selection procedures by using super learning techniques to identify the optimal model for predicting future events in event-free subjects. Specifically, the Integrated Brier score and Expected Predictive Cross-Entropy (EPCE) identified the multivariate joint model with the parameterization of the area under the longitudinal profiles of CD4 count and viral load as optimal and strong predictors of death. Integrating this model with a risk-based screening strategy, we recommend extending intervals to 10.3 months for stable patients, with additional measurements every 12 months. For patients with…
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Taxonomy
TopicsStatistical Methods and Inference · Statistical Methods in Clinical Trials · Genetic factors in colorectal cancer