Novel Tree-based Statistical Methods for Cancer Risk Prediction

描述（由申请人提供）：早期癌症检测的矛盾是，虽然一些人受益，但另一些人会接受有害的诊断。一个明确的例子是乳房X线摄影和导管原位癌（DCIS），一种非侵入性乳腺癌。DCIS最常表现为不可触及的病变，在现代乳腺X线摄影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岁以上妇女的发病率增加了500%。鉴于只有5-10%的DCIS病例进展为浸润性癌症，10年死亡率为1- 2%，DCIS专家建议大多数患者进行乳房保护。然而，这些妇女继续接受乳房切除术和放疗的过度治疗，其比率与浸润性癌症相当。无法区分低风险与高风险的部分原因是不可重现的研究结果以及用于风险预测和验证的统计方法不足。 我们收集了一个基于人群的DCIS队列，目的是描述那些最不可能复发浸润性癌症的女性，因此，合适的候选人进行不太积极的治疗。最近，我们建立了风险指数，并公布了相应的绝对风险估计类型的复发。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，限制了我们使用粗糙的经验方法进行分析，使我们无法验证我们的模型的临床效用。本提案的总体目标是制定一个统一的、原则性的统计框架，用于构建、选择和评估临床相关风险指标，允许在我们的DCIS研究中以及其他研究中对现有风险预测模型进行细化和验证。 我们面临着多重挑战，包括如何客观地建立相关变量的风险指数;如何在各种子样本研究设计中估计相应的风险（竞争或非竞争）;以及如何验证由此产生的风险预测模型。最近，我们开发了partDSA，这是一种基于树的方法，它在构建预测模型方面提供了巨大的灵活性，并为开发用于准确分层和风险预测的临床医生友好工具提供了理想的基础。在其当前形式中，partDSA无法估计存在竞争风险的绝对风险，说明子样本研究设计。在这里，我们扩展partDSA的临床相关的情况下（目的1）。我们还提出了用于风险预测的聚合学习，以提高预测准确性，并随后构建更稳定但更易于解释的风险模型（目标2）。最后，我们提出了必要的方法来验证所得到的模型（目标3）。 我们的建议有两个直接的公共卫生利益：首先，这些新的统计方法将导致临床医生友好，公开可用的工具，在许多临床环境中进行准确的风险预测，分层和验证;第二，目前的DCIS风险模型将得到完善和验证，期望更好地描绘那些低风险，因此保守治疗，包括积极监测的强有力的候选人。

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.