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

总结 乳腺癌是一种复杂的异质性疾病，许多预后因素，包括 雄激素受体（AR）是一种新兴的潜在预后生物标志物，其临床前 格雷辛。护士健康研究（NHS）是一项大型前瞻性队列研究， 为调查包括乳腺癌在内的妇女主要慢性疾病的风险因素而开展的研究。 来自NHS的数据包含了乳腺癌研究的宝贵信息，如生活方式，激素， 和遗传风险因素，以及临床结果，如乳腺癌诊断，复发和死亡。在 许多关于乳腺癌生存率的流行病学研究，包括NHS，风险比（HR），这是估计的， 基于比例风险（PH）模型的配对，是最常用的效应指标， 它的局限性。在NHS数据中，AR表达与乳腺癌之间关联的PH假设 生存权被侵犯。人力资源的解释是具有挑战性的，如果 违反了PH假设，这使得难以评估AR的预后价值。最近，汇总指标 基于限制平均生存时间（RMST），其定义为达到特定时间的预期寿命 作为HR的有用替代方案，已经引起了相当大的关注。 它具有简单的解释和鲁棒性等特点。具体来说，我们可以评估预后 在绝对效应方面，因子的效应比HR更具有临床可解释性，无需假设 使用基于RRST的回归模型。在这个项目中，我们建议开发新的统计方法， 基于RMST，充分利用NHS的丰富数据，并更好地了解复杂的 AR对乳腺癌进展和生存的影响。在目标1下，我们将开发一个灵活的回归 一种基于RMST的方法，用于估计一段时间内的不同协变量效应。拟议 回归方法将用于阐明AR在不同亚型的生存中的临床意义。 乳腺癌在目标2下，我们将开发一种无模型方法来总结双变量生存率 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生存率 并有助于通过AR状态对不同乳腺癌亚型进行组间比较。这些新的阶段- 统计方法将应用于NHS的数据，以获得有关预后价值的新知识。 AR并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.