Statistical methods for breast cancer survival using the restricted mean survival time

摘要 乳腺癌是一种复杂的异质性疾病，许多预后因素的作用包括 雄激素受体(AR)是一种新的潜在的预后生物标志物，但其临床应用尚不清楚。 太夸张了。推动这项应用的护士健康研究(NHS)是一个很大的前瞻性队列 为调查包括乳腺癌在内的妇女主要慢性病的风险因素而进行的研究。 来自NHS的数据包含了乳腺癌研究的宝贵信息，如生活方式、荷尔蒙、 和遗传风险因素，以及乳腺癌诊断、复发和死亡等临床结果。在……里面 许多关于乳腺癌存活率的流行病学研究，包括NHS，风险比(HR)，这是估计 基于比例风险(PH)模型的配对，一直是最常用的效果衡量标准，尽管 它的局限性。在NHS的数据中，AR表达与乳腺癌之间的关联中的PH假设 生存被发现受到了侵犯。对人力资源的解释是具有挑战性的，如果 PH值假设被违反，这使得fi很难评估AR的预后价值。最近，摘要指标 基于限制的平均生存时间(Rmst)，这是defined的预期寿命到特定的fic时间 作为人力资源的有用替代品，这一点引起了极大的关注。RMST有许多优点 其直截了当的解释和健壮性等特点。特别是fi，我们可以评估预后 在绝对效应方面的因素效应，这在临床上比HR更容易解释，不需要假设 PH使用基于RMST的回归模型。在这个项目中，我们建议开发新的统计方法 以RMST为基础，充分利用NHS的丰富数据，更好地了解复杂的 AR对乳腺癌进展和生存的影响。在目标1下，我们将开发fl可扩展回归 基于RMST的方法，用于估计在一段时间内变化的协变量效应。建议数 将用回归分析的方法阐明AR在不同亚型患者生存中的临床意义。 乳腺癌。在目标2下，我们将开发一种无模型的方法来总结双变量生存 数据(即，从初始事件到中间事件以及从中间事件到故障的时间 事件)。根据目标2开发的指标将用于研究乳腺癌复发后的剩余存活率。 并便于根据不同乳腺癌亚型的AR状态在组之间进行比较。这些新奇的舞台-- 组织方法将应用于NHS的数据，以获得关于糖尿病预后价值的新知识 AR并有可能导致更好的靶向治疗。

Summary Breast cancer is a complex and heterogeneous disease where the roles of many prognostic factors, including androgen receptor (AR) which is an emerging potential prognostic biomarker, remain unclear in its clinical pro- gression. The Nurses' Health Studies (NHS), which motivated this application, are large prospective cohort studies conducted to investigate the risk factors for major chronic diseases in women including breast cancer. The data from the NHS contain invaluable information for breast cancer research such as lifestyle, hormonal, and genetic risk factors, as well as clinical outcomes such as breast cancer diagnosis, recurrence, and death. In many epidemiologic studies on breast cancer survival, including the NHS, the hazard ratio (HR), which is esti- mated based on the proportional hazards (PH) model, has been the most routinely used effect measure despite its limitations. In the NHS data, the PH assumption in the association between AR expression and breast cancer survival was found to be violated. The interpretation of HR is challenging and the result is often misleading if the PH assumption is violated, which makes it difficult to assess AR's prognostic values. Recently, summary metrics based on the restricted mean survival time (RMST), which is defined as the life expectancy up to a specific time point, have attracted substantial attention as useful alternatives to the HR. The RMST has many advantageous features such as its straightforward interpretation and robustness. Specifically, we can assess the prognostic factor's effects in terms of absolute effect, which is clinically more interpretable than the HR, without assuming PH using the RMST-based regression model. In this project, we propose to develop novel statistical methods based on RMST to fully utilize the rich data from the NHS and to gain a better understanding of the complex effect of AR on breast cancer progression and survival. Under Aim 1, we will develop a flexible regression method based on RMST that estimates the varying covariate effects across a range of time. The proposed regression method will be used to elucidate the clinical significance of AR in survival by different subtypes of breast cancers. Under Aim 2, we will develop a model-free approach to summarize the bivariate survival data (i.e., time from an initial event to an intermediate event and from the intermediate event to a failure event). The metrics developed under Aim 2 will be used to study residual survival after breast cancer recurrence and facilitate comparisons between groups by AR status for different breast cancer subtypes. These novel sta- tistical approaches will be applied to data from the NHS to obtain new knowledge about the prognostic values of AR and potentially lead to better targeted therapies.