McIntosh Martin W, Urban Nicole
Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue MP-900, Seattle, WA 98109, USA.
Biostatistics. 2003 Jan;4(1):27-40. doi: 10.1093/biostatistics/4.1.27.
A revolution in molecular technology is leading to the discovery of many biomarkers of disease. Monitoring these biomarkers in a population may lead to earlier disease detection, and may prevent death from diseases like cancer that are more curable if found early. For markers whose concentration is associated with disease progression the earliest detection is achieved by monitoring the marker with an algorithm able to detect very small changes. One strategy is to monitor the biomarkers using a longitudinal algorithm that incorporates a subject's screening history into screening decisions. Longitudinal algorithms that have been proposed thus far rely on modeling the behavior of a biomarker from the moment of disease onset until its clinical presentation. Because the data needed to observe the early pre-clinical behavior of the biomarker may take years to accumulate, those algorithms are not appropriate for timely development using new biomarker discoveries. This manuscript presents a computationally simple longitudinal screening algorithm that can be implemented with data that is obtainable in a short period of time. For biomarkers meeting only a few modest assumptions our algorithm uniformly improves the sensitivity compared with simpler screening algorithms but maintains the same specificity. It is unclear what performance advantage more complex methods may have compared with our method, especially when there is doubt about the correct model for describing the behavior of the biomarker early in the disease process. Our method was specifically developed for use in screening for cancer with a new biomarker, but it is appropriate whenever the pre-clinical behavior of the disease and/or biomarker is uncertain.
分子技术的一场革命正促使人们发现许多疾病的生物标志物。在人群中监测这些生物标志物可能会实现疾病的早期检测，并可能预防因癌症等疾病导致的死亡，而这些疾病若能早期发现则更有可能治愈。对于那些浓度与疾病进展相关的标志物，通过使用能够检测到非常微小变化的算法来监测该标志物，可实现最早的检测。一种策略是使用纵向算法监测生物标志物，该算法将受试者的筛查历史纳入筛查决策中。迄今为止提出的纵向算法依赖于对生物标志物从疾病发作时刻到其临床表现的行为进行建模。由于观察生物标志物早期临床前行为所需的数据可能需要数年时间才能积累，所以那些算法不适用于利用新发现的生物标志物及时开展研究。本手稿提出了一种计算简单的纵向筛查算法，该算法可以用短时间内可获取的数据来实施。对于仅满足一些适度假设的生物标志物，与更简单的筛查算法相比，我们的算法能一致地提高灵敏度，但保持相同的特异性。与我们的方法相比，尚不清楚更复杂的方法可能具有何种性能优势，尤其是当对描述疾病过程早期生物标志物行为的正确模型存在疑问时。我们的方法是专门为使用新的生物标志物筛查癌症而开发的，但只要疾病和/或生物标志物的临床前行为不确定，它就适用。
