Li Wen, Li Ruosha, Feng Ziding, Ning Jing
Division of Clinical and Translational Sciences, Department of Internal Medicine, The University of Texas McGovern Medical School, Houston, TX 77030, United States.
Department of Biostatistics and Data Science, The University of Texas School of Public Health, Houston, TX 77030, United States.
Biostatistics. 2024 Dec 31;26(1). doi: 10.1093/biostatistics/kxae036.
Dynamic prediction models capable of retaining accuracy by evolving over time could play a significant role for monitoring disease progression in clinical practice. In biomedical studies with long-term follow up, participants are often monitored through periodic clinical visits with repeat measurements until an occurrence of the event of interest (e.g. disease onset) or the study end. Acknowledging the dynamic nature of disease risk and clinical information contained in the longitudinal markers, we propose an innovative concordance-assisted learning algorithm to derive a real-time risk stratification score. The proposed approach bypasses the need to fit regression models, such as joint models of the longitudinal markers and time-to-event outcome, and hence enjoys the desirable property of model robustness. Simulation studies confirmed that the proposed method has satisfactory performance in dynamically monitoring the risk of developing disease and differentiating high-risk and low-risk population over time. We apply the proposed method to the Alzheimer's Disease Neuroimaging Initiative data and develop a dynamic risk score of Alzheimer's Disease for patients with mild cognitive impairment using multiple longitudinal markers and baseline prognostic factors.
能够通过随时间演变来保持准确性的动态预测模型，在临床实践中监测疾病进展方面可能发挥重要作用。在长期随访的生物医学研究中，通常通过定期临床访视和重复测量对参与者进行监测，直到发生感兴趣的事件（如疾病发作）或研究结束。认识到纵向标志物中疾病风险和临床信息的动态性质，我们提出了一种创新的一致性辅助学习算法来得出实时风险分层分数。所提出的方法无需拟合回归模型，如纵向标志物和事件发生时间结果的联合模型，因此具有模型稳健性这一理想特性。模拟研究证实，所提出的方法在动态监测疾病发生风险以及随时间区分高风险和低风险人群方面具有令人满意的性能。我们将所提出的方法应用于阿尔茨海默病神经影像学倡议数据，并使用多个纵向标志物和基线预后因素为轻度认知障碍患者开发了阿尔茨海默病的动态风险评分。
