Schmid Matthias, Kestler Hans A, Potapov Sergej
Brief Bioinform. 2015 Jan;16(1):153-68. doi: 10.1093/bib/bbt059. Epub 2013 Sep 14.
Recent developments in molecular biology have led to the massive discovery of new marker candidates for the prediction of patient survival. To evaluate the predictive value of these markers, statistical tools for measuring the performance of survival models are needed. We consider estimators of discrimination measures, which are a popular approach to evaluate survival predictions in biomarker studies. Estimators of discrimination measures are usually based on regularity assumptions such as the proportional hazards assumption. Based on two sets of molecular data and a simulation study, we show that violations of the regularity assumptions may lead to over-optimistic estimates of prediction accuracy and may therefore result in biased conclusions regarding the clinical utility of new biomarkers. In particular, we demonstrate that biased medical decision making is possible even if statistical checks indicate that all regularity assumptions are satisfied.
分子生物学的最新进展带来了大量用于预测患者生存的新候选标志物的发现。为了评估这些标志物的预测价值，需要用于衡量生存模型性能的统计工具。我们考虑鉴别度量的估计量，这是评估生物标志物研究中生存预测的一种常用方法。鉴别度量的估计量通常基于诸如比例风险假设等正则性假设。基于两组分子数据和一项模拟研究，我们表明违反正则性假设可能导致对预测准确性的过度乐观估计，从而可能导致关于新生物标志物临床效用的有偏差结论。特别是，我们证明即使统计检验表明所有正则性假设都得到满足，有偏差的医疗决策也是可能的。
