Bansal Aasthaa, Heagerty Patrick J
1The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, University of Washington, H-375 Health Sciences Building, Campus Mail Stop 357630, Seattle, 98195 WA USA.
2Department of Biostatistics, University of Washington, F-600 Health Sciences Building, Campus Mail Stop 357232, Seattle, 98195 WA USA.
Diagn Progn Res. 2019 Jul 25;3:14. doi: 10.1186/s41512-019-0057-6. eCollection 2019.
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.
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.
We found that time-varying HRs, HR (), using local linear estimation revealed time trends more clearly by directly estimating the association at each time point , compared to landmark analyses, which averaged across time ≥ . Comparing area under the ROC curve (AUC) summaries, there was close agreement between AUC (,+1) which defines cases cumulatively over 1-year intervals and AUC () which defines cases as incident events. HR () was more consistent with AUC (), as estimation of these measures is localized at each time point.
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.
预后标志物利用个体在特定时间的特征来预测未来疾病事件，其最终目标是指导医疗决策。如果能够做出准确的预测，那么预后标志物可在临床上用于识别那些未来发生不良事件风险最高的受试者，并可用于确定适合进行靶向治疗干预的人群。通常，标志物在单个基线时间点（如疾病诊断时）进行测量，然后用于指导后续多个时间点的决策。然而，随着个体潜在临床状态的变化，候选标志物的性能可能随时间而变化。
我们对评估基线预后标志物随时间变化准确性的现代统计方法进行了概述和比较。我们比较了考虑累积事件与新发事件的方法。此外，我们将使用Cox比例风险回归获得的风险比的常用方法与使用时间依赖型受试者工作特征（ROC）曲线的最新方法进行了比较。使用一项关于候选生物标志物的多发性骨髓瘤研究说明了替代统计汇总。
我们发现，与在时间≥时进行平均的标志性分析相比，使用局部线性估计的随时间变化的风险比HR（）通过直接估计每个时间点的关联更清楚地揭示了时间趋势。比较ROC曲线下面积（AUC）汇总，在1年间隔内累积定义病例的AUC（，+1）与将病例定义为新发事件的AUC（）之间有密切一致性。HR（）与AUC（）更一致，因为这些测量的估计在每个时间点都是局部化的。
我们比较了用于量化预后标志物随时间变化性能的替代汇总。虽然在需要在特定时间进行患者预测时，基于标志性的预测可能有用，但关注新发事件自然有助于评估性能随时间的趋势。
