首页> 外文期刊>American Journal of Epidemiology >Further insight into the incremental value of new markers: the interpretation of performance measures and the importance of clinical context.
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Further insight into the incremental value of new markers: the interpretation of performance measures and the importance of clinical context.

机译:进一步了解新标记物的增量价值:性能指标的解释和临床环境的重要性。

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In this issue of the Journal, Pencina and et al. (Am J Epidemiol. 2012;176(6):492-494) examine the operating characteristics of measures of incremental value. Their goal is to provide benchmarks for the measures that can help identify the most promising markers among multiple candidates. They consider a setting in which new predictors are conditionally independent of established predictors. In the present article, the authors consider more general settings. Their results indicate that some of the conclusions made by Pencina et al. are limited to the specific scenarios the authors considered. For example, Pencina et al. observed that continuous net reclassification improvement was invariant to the strength of the baseline model, but the authors of the present study show this invariance does not hold generally. Further, they disagree with the suggestion that such invariance would be desirable for a measure of incremental value. They also do not see evidence to support the claim that the measures provide complementary information. In addition, they show that correlation with baseline predictors can lead to much bigger gains in performance than the conditional independence scenario studied by Pencina et al. Finally, the authors note that the motivation of providing benchmarks actually reinforces previous observations that the problem with these measures is they do not have useful clinical interpretations. If they did, researchers could use the measures directly and benchmarks would not be needed.
机译:在本期《期刊》中,彭西纳等人。 (Am J Epidemiol.2012; 176(6):492-494)研究了增值量度的操作特征。他们的目标是为可以帮助确定多个候选人中最有希望的标志的措施提供基准。他们考虑了这样一种环境,其中新的预测变量有条件地独立于既定的预测变量。在本文中,作者考虑了更常规的设置。他们的结果表明Pencina等人的一些结论。仅限于作者考虑的特定方案。例如,Pencina等。观察到连续的净重分类改进不会影响基线模型的强度,但是本研究的作者表明,这种不变性通常不成立。此外,他们不同意这样的不变性对于度量增量价值的建议。他们也没有证据支持这些措施提供补充信息的说法。此外,他们表明,与Pencina等人研究的条件独立性情景相比,与基准预测变量的相关性可以带来更大的绩效提升。最后,作者指出,提供基准的动机实际上强化了先前的观察,即这些措施的问题在于它们没有有用的临床解释。如果这样做的话,研究人员可以直接使用这些方法,并且不需要基准。

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