Statistical Methods for Cancer Biomarkers

描述(申请人提供)：癌症研究中的生物标记物被认为是预防、检测、治疗和监测方面预期改进的中心组成部分。有可能在许多不同类型的研究和许多不同的目的有用。关键的问题是它们是否可以有效地使用，如何以有效和高效的方式使用它们，然后如果它们被使用，人们对所获得的结论有多大的信心。使用生物标记物来促进对癌症科学的理解具有巨大的潜力，但也存在一些风险。生物标记物在其测量中受到不确定性的影响，它们可能不能准确地测量感兴趣的数量，而且由于它们不是明确的症状测量，因此它们在临床环境中用于辅助决策或评估治疗方案受到不确定性的影响。因此，对涉及生物标志物的研究数据进行仔细分析是至关重要的。在这样的研究中出现了许多统计学上的挑战。该应用程序涉及开发、评估和应用涉及生物标志物的数据的统计方法。第一个目标是将生物标记物添加到可用于对患者进行分层或分类的预测模型中。在这一目标中，我们开发了整合来自其他来源的数据的方法来改进预测模型。本研究将具有广泛的适用性。创新方面包括使用有针对性的岭回归、多核机器建模和重要性抽样来结合文献中的信息。第二个目标与临床试验有关，在临床试验中，生物标记物将被用于评估作为替代终点的治疗。由于科学问题的性质，因果建模在这种背景下是非常自然的。我们建议开发潜在结果和结构性因果模型。我们将调查具有不同终端类型的单次试验和多次试验设置。第三个目标是关于可能只对一小部分患者有效的治疗方法，为了使这一亚组有效，这一亚组由少量的预测性生物标志物确定。对于随机临床试验的数据，我们建议使用统一的建模方法，并将研究使用变量选择和多变量偏最小二乘的单指数模型来辅助亚组识别。针对子群识别后的推论具有挑战性的问题，我们提出了一种创新的方案来模拟适当的零分布下的数据。该提案中的所有三个目标都解决了翻译肿瘤学研究中的基本和重要问题。这些目标的成功完成将对理解和利用生物标记物以及制定可更广泛地适用于其他领域的统计方法产生影响。

DESCRIPTION (provided by applicant): Biomarkers in cancer research are considered a central component of the expected improvements in prevention, detection, treatment and monitoring. There are potentially useful in many different types of studies and for many different purposes. Critical questions are whether they are valid to use, how can they be utilized in a valid and efficient way, and then if they are used how confident is one in the conclusions that are obtained. The use of biomarkers to advance understanding in cancer science has great potential, but also has some risks. Biomarkers are subject to uncertainty in their measurement, they may not be measuring exactly the quantity of interest, and since they are not explicitly measures of symptoms their use to aid in decision making or evaluation of therapies in a clinical setting is subject to uncertainty. Thus careful analysis of data from studies that involve biomarkers is crucial. There are many statistical challenges that arise in such studies. This application is concerned with developing, evaluating and applying statistical methods for data that involves biomarkers. The first aim is concerned with adding biomarkers to prediction models that may be used to stratify or classify patients. In this aim we develop approaches for integrating data from other sources to improve the prediction models. This research will have broad applicability. Innovative aspects involve the use of targeted ridge regression, multi-kernel machine modeling, and importance sampling to incorporate information from the literature. The second aim is concerned with clinical trials where the biomarker is to be used to evaluate a therapy as a surrogate endpoint. Because of the nature of the scientific question causal modeling is very natural in this context. We propose to develop both potential outcomes and structural causal models. We will investigate both single trial and multi trial settings with different endpoint types. The third aim is concerned with therapies that may be effective only for a subgroup of patients, and to be useful this subgroup is determined by a small number of predictive biomarkers. For data from randomized clinical trials we suggest a unified modeling approach, and will investigate the use of single index models with variable selection and multivariate partial least squares to aid in the subgroup identification. Inference following subgroup identification is challenging, we suggest an innovative scheme to simulate data under an appropriate null distribution. All 3 aims in this proposal address fundamental and significant problems in translational oncology research. Successful completion of the aims will have an impact both in understanding and utilizing biomarkers and also in developing statistical methodology that can be more broadly applicable to other fields.