Time-dependent ROC curve analysis in medical research: current methods and applications.
Time-dependent ROC curve analysis in medical research: current methods and applications.
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DOI：
10.1186/s12874-017-0332-6
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发表时间：
2017-04-07
影响因子：
4
通讯作者：
Kolamunnage-Dona R
中科院分区：
文献类型：
--
作者：
Kamarudin AN;Cox T;Kolamunnage-Dona R
ROC(接受者操作特征)曲线分析被很好地用于评估标记物能够很好地区分经历过疾病的人和没有经历过疾病的人。经典的(标准)ROC曲线分析方法认为个体的事件(疾病)状态和标记值随着时间的推移是固定的，但在实践中，疾病状态和标记值都会随着时间的推移而变化。较早无病的个体可能会因较长时间的研究随访而较晚发生疾病，而且他们的标记值也可能在随访期间较基线发生变化。因此，ROC曲线作为时间的函数更合适。然而，许多研究人员仍然使用标准的ROC曲线方法来确定标记物的能力，忽略了疾病状态或标记物的时间依赖性。我们全面回顾了目前提出的使用单标记或纵向标记测量的时间相关ROC曲线的方法，旨在提供每种方法的清晰度，找出在实践中进行此类分析的软件工具，并说明该方法的几个应用。我们还扩展了一些方法以纳入纵向标记物，并使用梅奥临床试验中的原发性胆汁性肝硬变(PBC)序贯数据集说明了方法。从我们的方法论回顾中，我们确定了18种截尾事件时间的时变ROC曲线分析的估计方法，另外三种方法只能处理非截尾事件时间。尽管有相当数量的估计方法，但该方法在临床研究中的应用仍然很少。重新确立了随时间变化的ROC曲线方法的价值。我们使用现有的软件说明了实践中的方法，并对未来的研究提出了一些建议。本文的在线版本(doi：10.1186/s12874-0170332-6)包含补充材料，授权用户可以使用。
ROC (receiver operating characteristic) curve analysis is well established for assessing how well a marker is capable of discriminating between individuals who experience disease onset and individuals who do not. The classical (standard) approach of ROC curve analysis considers event (disease) status and marker value for an individual as fixed over time, however in practice, both the disease status and marker value change over time. Individuals who are disease-free earlier may develop the disease later due to longer study follow-up, and also their marker value may change from baseline during follow-up. Thus, an ROC curve as a function of time is more appropriate. However, many researchers still use the standard ROC curve approach to determine the marker capability ignoring the time dependency of the disease status or the marker. We comprehensively review currently proposed methodologies of time-dependent ROC curves which use single or longitudinal marker measurements, aiming to provide clarity in each methodology, identify software tools to carry out such analysis in practice and illustrate several applications of the methodology. We have also extended some methods to incorporate a longitudinal marker and illustrated the methodologies using a sequential dataset from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver. From our methodological review, we have identified 18 estimation methods of time-dependent ROC curve analyses for censored event times and three other methods can only deal with non-censored event times. Despite the considerable numbers of estimation methods, applications of the methodology in clinical studies are still lacking. The value of time-dependent ROC curve methods has been re-established. We have illustrated the methods in practice using currently available software and made some recommendations for future research. The online version of this article (doi:10.1186/s12874-017-0332-6) contains supplementary material, which is available to authorized users.