A comparison of landmark methods and time-dependent ROC methods to evaluate the time-varying performance of prognostic markers for survival outcomes.
A comparison of landmark methods and time-dependent ROC methods to evaluate the time-varying performance of prognostic markers for survival outcomes.
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DOI：
10.1186/s41512-019-0057-6
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发表时间：
2019-01-01
影响因子：
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通讯作者：
Heagerty, Patrick J
中科院分区：
文献类型：
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作者：
Bansal, Aasthaa;Heagerty, Patrick J
背景技术背景：预后标志物使用个体在给定时间的特征来预测未来的疾病事件，最终目标是指导医疗决策。如果可以做出准确的预测，则可以在临床上使用预后标志物来识别那些未来不良事件风险最大的受试者，并且可以用于定义适合于靶向治疗干预的人群。通常，标记物在单个基线时间点（例如疾病诊断）测量，然后用于指导多个后续时间点的决策。然而，候选标志物的性能可能会随着时间的推移而变化，作为一个人的基本临床状态changes.METHODS：我们提供了一个概述和比较现代统计方法，用于评估随时间变化的准确性的基线预后标志物。我们比较的方法，考虑累积与事件。此外，我们比较了常见的方法，使用风险比从考克斯比例风险回归到最近开发的方法，使用时间依赖性受试者工作特征（ROC）曲线。另一种统计总结使用多发性骨髓瘤研究的候选biomarkers.RESULTS：我们发现，随时间变化的HR，HR（t），使用局部线性估计显示时间趋势更清楚地直接估计在每个时间点t的关联，相比地标分析，平均跨越时间≥t。比较ROC曲线下面积（AUC）总结，AUC C/D（t，t+1）（定义1年时间间隔内累积的病例）与AUC I/D（t）（定义为事件的病例）之间非常一致。HR（t）与AUC I/D（t）更一致，因为这些测量的估计值位于每个时间点。虽然基于标志的预测在选择时间需要患者预测时可能是有用的，但是关注事件事件自然有助于评估随着时间的推移的性能趋势。
BACKGROUND: Prognostic markers use an individual's characteristics at a given time to predict future disease events, with the ultimate goal of guiding medical decision-making. If an accurate prediction can be made, then a prognostic marker could be used clinically to identify those subjects at greatest risk for future adverse events and may be used to define populations appropriate for targeted therapeutic intervention. Often, a marker is measured at a single baseline time point such as disease diagnosis, and then used to guide decisions at multiple subsequent time points. However, the performance of candidate markers may vary over time as an individual's underlying clinical status changes.METHODS: We provide an overview and comparison of modern statistical methods for evaluating the time-varying accuracy of a baseline prognostic marker. We compare approaches that consider cumulative versus incident events. Additionally, we compare the common approach of using hazard ratios obtained from Cox proportional hazards regression to more recently developed approaches using time-dependent receiver operating characteristic (ROC) curves. The alternative statistical summaries are illustrated using a multiple myeloma study of candidate biomarkers.RESULTS: We found that time-varying HRs, HR (t), using local linear estimation revealed time trends more clearly by directly estimating the association at each time point t, compared to landmark analyses, which averaged across time ≥t. Comparing area under the ROC curve (AUC) summaries, there was close agreement between AUC C/D (t,t+1) which defines cases cumulatively over 1-year intervals and AUC I/D (t) which defines cases as incident events. HR (t) was more consistent with AUC I/D (t), as estimation of these measures is localized at each time point.CONCLUSIONS: We compared alternative summaries for quantifying a prognostic marker's time-varying performance. Although landmark-based predictions may be useful when patient predictions are needed at select times, a focus on incident events naturally facilitates evaluating trends in performance over time.