Singapore Clinical Research Institute PTE Ltd., 31 Biopolis Way, Nanos #02-01, Singapore 138669, Singapore.
Stat Med. 2010 Sep 30;29(22):2338-46. doi: 10.1002/sim.3993.
Biomarkers that can help identify patients who will have an early clinical benefit from a treatment are important not only for patients' survival and quality of life, but also for the cost of health care. Owing to reasons such as biological variation and limited machine precision, biomarkers are sometimes measured with large errors. Adjusting for the measurement error in calculating the proportion of the treatment effect explained by markers has been a subject of research. The proportion of information gain (PIG), a new quantity to measure the importance of a biomarker, has not yet been studied for variables measured with error. In this article, we provide methods to account for the measurement error in the calculation of PIG for continuous, binary and time-to-event outcomes. Simulation shows that the adjusted estimator has little bias and has less variability compared to the naive estimator ignoring the measurement error. Data from an osteoporosis clinical study are used to illustrate the method for a binary outcome.
能够帮助识别出哪些患者将从治疗中获得早期临床获益的生物标志物不仅对患者的生存和生活质量很重要，而且对医疗保健的成本也很重要。由于生物变异性和机器精度有限等原因，生物标志物的测量有时会存在较大误差。在计算标记物解释治疗效果的比例时，调整测量误差一直是研究的主题。尚未针对具有误差的变量研究新的信息量增益（PIG），这是一种用于衡量生物标志物重要性的新数量。在本文中，我们提供了一种方法来计算连续、二项和事件时间结果的 PIG 的测量误差。模拟结果表明，与忽略测量误差的简单估计器相比，调整后的估计器具有较小的偏差和较小的变异性。使用骨质疏松症临床研究的数据来说明二项结果的方法。
