Power analysis tools for biomarker discovery with heterogeneous diseases

描述(申请人提供)：在技术进步、研究积累和数据整合的推动下，癌症生物标记物的研究在学术界、工业界和政府之间蓬勃发展。尽管有大量的生物标记物发现研究，但检验发现研究的样本量要求的研究令人惊讶地很少，而且目前公开提供的工具非常少，允许研究人员进行能力分析以确定生物标记物发现研究的样本量要求。缺乏能量分析工具表明，大多数生物标志物研究都是在没有适当的能量分析的情况下进行的，这增加了样本量不足的风险。此外，目前还没有解释疾病异质性的工具，这已被证明显著增加了样本量要求，并显著改变了不同分析策略对生物标记物选择的相对能力。例如，在有100个病例和100个对照的情况下，普通t检验可以检测出99%的同质性疾病的生物标志物，但只有18%的异质性疾病的生物标志物。因此，如果疾病是异质性的，使用t检验并为同质性疾病提供支持的生物标记物研究将只有预期能力的五分之一。不太常用的方法，如部分AUC，在异质性疾病中表现良好，但在同质性疾病中表现不佳。这一研究意义重大：与同类疾病相比，识别异质性疾病早期检测的生物标记物的研究需要不同的统计选择方法和更大的样本量。因此，以前的生物标记物发现研究可能未能识别生物标记物，因为它们对异质性疾病作用不大，或者因为它们使用了不适合异质性疾病的选择方法。该项目将开发公开可用的工具，以帮助统计学家和非统计学家规划生物标记物研究。该项目已 具体目标如下：目标1：开发用于规划生物标志物发现研究的能力分析工具。这些工具将使研究人员能够确定样本量要求，并检查用于发现研究的多种分析方法，同时适当地考虑疾病的异质性。目标2：扩展工具以支持构建生物标记物签名的能量分析。

DESCRIPTION (provided by applicant): Cancer biomarker research is flourishing across academia, industry and government, buoyed by technological advancements, accumulation of research and data integration. Despite the large number biomarker discovery studies there have been surprisingly few studies that have examined sample size requirements for discovery studies and very minimal publicly available tools exist today that allow researchers to conduct power analyses to determine sample size requirements for biomarker discovery studies. The lack of power analysis tools suggests that most biomarker studies are undertaken without proper power analyses, increasing the risk of inadequate sample sizes. Further, there are no tools that account for disease heterogeneity, which has been shown to dramatically increase sample size requirements and significantly alter the relative power of different analytic strategie for biomarker selection. For example, with 100 cases and 100 controls, the ordinary t-test detects 99% of biomarkers for a homogeneous disease, but only 18% for a heterogeneous disease. Thus, a biomarker study using the t-test and powered for a homogeneous disease would have only one-fifth the anticipated power if the disease is heterogeneous. Less commonly-used methods, such as the partial AUC, perform well for heterogeneous diseases but poorly for homogeneous diseases. The implications are significant: studies to identify biomarkers for the early detection of heterogeneous diseases require different statistical selection methods and larger sample sizes than if the disease were homogeneous. Thus, previous biomarker discovery studies may have failed to identify biomarkers because they were underpowered for a heterogeneous disease or because they used selection methods that were inappropriate for heterogeneous diseases. This project will develop publicly available tools to help both statisticians and non-statisticians in the planning of biomarker studies. The project has the following specific aims: Aim 1: Develop power analysis tools for planning biomarker discovery studies. These tools will enable researchers to determine sample size requirements and examine multiple analytic methods for discovery research while properly accounting for disease heterogeneity. Aim 2: Extend the tools to support power analyses for construction of biomarker signatures.