Novel Tree-based Statistical Methods for Cancer Risk Prediction

描述(申请人提供)：早期癌症检测的矛盾之处在于，虽然一些人受益，但另一些人得到的诊断是有害的。一个明确的例子是乳房X光检查和导管原位癌(DCIS)，这是一种非浸润性乳腺癌。DCIS最常见的表现是一种不可触及的病变，在现代乳房X光检查出现之前很少被发现。自1983年以来，50岁以下妇女的DCIS发病率增加了290%，50岁以上妇女的DCIS发生率增加了500%。鉴于只有5-10%的DCIS病例进展为浸润性癌，10年死亡率为1-2%，DCIS专家建议大多数患者保留乳房。然而，这些妇女继续过度接受乳房切除术和放射治疗，其比率与那些患有浸润性癌症的人相当。无法区分低风险和高风险的部分原因是研究结果不可重现，以及风险预测和验证的统计方法不充分。我们收集了一个以人群为基础的DCIS队列，目的是描绘出最不可能复发的浸润性癌症的女性，因此，适合进行不那么激进的治疗的女性。最近，我们建立了风险指数，并发表了相应的复发类型的绝对风险估计。然而，研究设计的两个特点，即竞争风险的存在和分层病例队列设计的使用，限制了我们使用原始的经验方法进行分析，使我们无法验证我们的模型的临床实用性。这项建议的总体目标是开发一个统一的、原则性的统计框架，用于建立、选择和评估临床相关的风险指标，允许改进和验证我们DCIS研究以及其他研究中的现有风险预测模型。我们面临着多重挑战，包括如何用相关变量客观地构建风险指数；如何在各种子样本研究设计中估计相应的风险(竞争或不竞争)；以及如何验证由此产生的风险预测模型。最近，我们开发了部分DSA，这是一种基于树的方法，在建立预测模型方面提供了巨大的灵活性，并为开发临床医生友好的准确分层和风险预测工具提供了理想的基础。在目前的形式下，部分DSA无法在存在对子样本研究设计的竞争风险的情况下估计绝对风险。在这里，我们将部分DSA扩展到这样的临床相关场景(目标1)。我们还提出了风险预测的聚合学习，以提高预测精度，并随后建立更稳定但更容易解释的风险模型(目标2)。最后，我们提出了验证所得模型的必要方法(目标3)。我们的建议有两个直接的公共卫生好处：第一，这些新的统计方法将产生一个临床医生友好的、公开可用的工具，用于在许多临床环境中进行准确的风险预测、分层和验证；第二，当前的DCIS风险模型将得到改进和验证，以期更好地描述低风险者，因此强烈适合于包括积极监测在内的保守治疗。

DESCRIPTION (provided by applicant): The contradiction of early cancer detection is that while some benefit others receive a detrimental diagnosis. A definitive example is mammography and ductal carcinoma in situ (DCIS), a noninvasive breast cancer. DCIS, which most frequently presents as a non-palpable lesion, was rarely detected before the advent of modern mammography. Since 1983 there has been a 290% increase in DCIS incidence in women under 50 and 500% in those over 50. Given that only 5-10% of DCIS cases progress to invasive cancer with a 10-year mortality rate of 1-2%, DCIS experts suggest breast conservation for the majority of patients. However, these women continue to be overtreated with mastectomy and radiation, at rates comparable to those with invasive cancer. The inability to discern those at low vs. high risk is due in part to non-reproducible study results as well as inadequate statistical methods for risk prediction and validation. We have collected a population-based DCIS cohort with the goal of delineating those women least likely to recur with invasive cancer and, hence, appropriate candidates for less aggressive treatments. Recently we established risk indices and published the corresponding absolute risk estimates for type of recurrence. However, two features of the study design, namely the presence of competing risks and the use of a stratified case-cohort design, constrained us to using crude empirical methods for analysis and left us unable to validate the clinical utility of our models. The overarching goal of this proposal is to develop a unified, principled statistical framework for building, selecting, and evaluating clinically relevant risk indices, permitting refinement and validation of existing risk prediction models in our DCIS study as well as beyond. We face multiple challenges including how to objectively build risk indices with relevant variables; how to estimate the corresponding risks (competing or not) in various subsample study designs; and, how to validate the resulting risk prediction models. Recently, we developed partDSA, a tree-based method which affords tremendous flexibility in building predictive models and provides an ideal foundation for developing a clinician- friendly tool for accurate stratification and risk prediction. In its curret form, partDSA is unable to estimate absolute risk in the presence of competing risks accounting for subsample study designs. Here we extend partDSA for such clinically relevant scenarios (Aim 1). We also propose aggregate learning for risk prediction to increase prediction accuracy and subsequently to build more stable but easily interpretable risk models (Aim 2). Finally, we propose the necessary methods for validating the resulting models (Aim 3). Our proposal has two immediate public health benefits: first, these novel statistical methods will result in a clinician-friendly, publicly available tool for accurate risk prediction, stratification and validaion in numerous clinical settings; second, current DCIS risk models will be refined and validated with the expectation of better delineating those at low risk, hence strong candidates for conservative treatments including active surveillance.