在生存模型中测量新的风险因素的影响。

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY
Glenn Heller, Sean M Devlin
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引用次数: 0

摘要

转移性癌症患者的生存率很低,因此检查新的生物标志物以改善患者预后并确定哪些患者将从更积极的治疗中受益至关重要。在转移性前列腺癌中,有两种新的检测方法可用:一种是量化外周血中循环的癌细胞数量,另一种是疾病侵袭性的标志。确定这些生物标志物对基于模型的风险评分的区分的影响程度是至关重要的。为了做到这一点,大多数分析师经常考虑两种独立生存模型的区别:一种既包括新因素又包括标准因素,另一种只包括标准因素。然而,对于生存中普遍存在的许多尺度转换模型来说,这种分析最终是不正确的,因为如果正确地指定了完整模型,则错误地指定了简化模型。为了避免这个问题,我们开发了一种基于预测的方法来估计两种前列腺癌生物标志物的影响。结果表明,新的生物标志物可以影响模型判别,并证明将其纳入风险模型是合理的;然而,对转移性前列腺癌患者进行风险分层的适用模型仍有待研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measuring the impact of new risk factors within survival models.

Survival is poor for patients with metastatic cancer, and it is vital to examine new biomarkers that can improve patient prognostication and identify those who would benefit from more aggressive therapy. In metastatic prostate cancer, 2 new assays have become available: one that quantifies the number of cancer cells circulating in the peripheral blood, and the other a marker of the aggressiveness of the disease. It is critical to determine the magnitude of the effect of these biomarkers on the discrimination of a model-based risk score. To do so, most analysts frequently consider the discrimination of 2 separate survival models: one that includes both the new and standard factors and a second that includes the standard factors alone. However, this analysis is ultimately incorrect for many of the scale-transformation models ubiquitous in survival, as the reduced model is misspecified if the full model is specified correctly. To circumvent this issue, we developed a projection-based approach to estimate the impact of the 2 prostate cancer biomarkers. The results indicate that the new biomarkers can influence model discrimination and justify their inclusion in the risk model; however, the hunt remains for an applicable model to risk-stratify patients with metastatic prostate cancer.

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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
76
审稿时长
>12 weeks
期刊介绍: The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.
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