Applying statistical learning tools to personalize cardiovascular treatment

摘要 心血管疾病（CVD）治疗通常由风险分层工具指导（以决定治疗对象）， 随机对照试验（决定选择哪种治疗）。先前的CVD研究揭示了两个主要的 改善我们治疗方法的障碍：（i）纵向队列数据无法用于重新校准风险 分层工具，用于局部区域估计（按邮政编码），或用于主要CVD促进人群 合并症（例如，慢性肾脏疾病）;和（ii）随机试验中的平均治疗效果可以是 当投射到与试验中的“平均”参与者不同的个体上时，这是非常错误的。CVD风险- 如果我们克服了一个关键障碍， 进展：正确估计新的大型参与者数据库的风险和治疗效果， 与普通队列研究相比，具有更大的人群规模，并且包括患有更多共病的患者， 并且其允许从个体水平数据开发个性化风险/益处预测工具。我们事先 研究表明，我们可以通过将新的统计学习方法应用于这些数据， 解决：（一）多重测试的假阳性;（二）依赖无法解释的标准回归 因素之间的非线性、复杂的相互作用;以及（iii）在许多因素中确定最佳方法 替代统计学习方法。我们建议将我们在这些领域的工作应用于（目标1）开发CVD 常见队列研究中样本量不足患者的风险分层工具。我们将加强 CVD风险分层包括当地调整（按邮政编码）和影响的主要共病状况 CVD风险（例如，慢性肾病）。我们还将（目标2）开发个性化治疗效果 用于指导具有高潜在获益和风险的CVD治疗决策的预测工具， 可获得来自试验的个体参与者数据。我们已经从大型研究机构获得了个体参与者的数据， 显示CVD风险降低和严重不良事件风险增加的广泛差异的随机试验 从三个药物类别：非维生素K拮抗剂口服抗凝剂，强化血压治疗， 用于糖尿病的钠-葡萄糖协同转运蛋白2抑制剂。我们的初步研究表明， 回归方法无法区分哪些患者最有可能受益于这些治疗， 我们的统计学习方法可以。最后，我们将（目标3）开发开源工具，以提高 研究人员可以为他们的数据集和问题选择最佳的统计学习方法。虽然许多 统计学习方法已经在文献中提出，这是生物医学科学家的一个关键问题 在没有RCT数据的情况下，我应该使用哪种方法来估计观察数据的治疗效果？ 基于我们的创新方法来确定观测数据的最佳推理方法，我们将 构建一个开源工具来比较方法，确定哪种方法最常导致最佳结果 治疗决策，最大限度地减少错误和最大限度地提高标准化指标的性能。

Abstract Cardiovascular disease (CVD) treatment is often guided by risk stratification tools (to decide who to treat), and randomized controlled trials (to decide which treatments to select). Prior CVD research reveals two major obstacles to improving our treatment approach: (i) longitudinal cohort data are unavailable for recalibrating risk stratification tools for local-area estimation (by zip code), or for people with major CVD-promoting comorbidities (e.g., chronic kidney disease); and (ii) the average treatment effect in randomized trials can be highly erroneous when projected onto individuals that vary from the ‘average’ participant in a trial. CVD risk- stratification and treatment effect estimation can be improved and personalized if we overcome a critical barrier to progress: correctly estimating risk and treatment effect from new, large participant data repositories, which have greater population size and include patients with more co-morbid conditions than common cohort studies, and which permit personalized risk/benefit prediction tool development from individual-level data. Our prior studies show that we can critically advance the field by applying novel statistical learning methods to this data, to address: (i) false-positives from multiple testing; (ii) the reliance on standard regressions that cannot account for non-linear, complex interactions between factors; and (iii) identifying the optimal approach among many alternative statistical learning methods. We propose to apply our work in these areas to (Aim 1) Develop CVD risk stratification tools for patients with inadequate sample sizes in common cohort studies. We will enhance CVD risk stratification to include local-area adjustment (by zip code) and major co-morbid conditions affecting CVD risk (e.g., chronic kidney disease). We will additionally (Aim 2) develop personalized treatment effect prediction tools to guide decisions for CVD therapies with high potential benefit and risk, for therapies where individual participant data from trials are available. We have obtained the individual participant data from the large randomized trials that reveal wide variations in CVD risk reduction and serious adverse event risk increase from three drug classes: non-vitamin K antagonist oral anticoagulants, intensive blood pressure treatment, and sodium-glucose co-transporter 2 inhibitors for diabetes. Our preliminary research shows that traditional regression methods cannot distinguish which patients are most likely to benefit or be harmed by such therapies, but our statistical learning methods can. Finally, we will (Aim 3) develop open-source tools to improve the ability of researchers to choose an optimal statistical learning approach for their dataset and problem. While numerous statistical learning methods have been proposed in the literature, a key problem for biomedical scientists without access to RCT data is: which method should I use to estimate treatment effects from observational data? Building on our innovative approach to identify the optimal inference method for observational data, we will construct an open-source tool to compare methods, identifying which method most often results in optimal treatment decisions that minimize error and maximize performance on standardized metrics.