Methods for Comparative Effectiveness Research in Cancer

这是一项开发、改进和实施统计方法以实现比较有效性的建议 癌症研究。使用这些方法，我们将模拟复杂的个性化和随机化试验 癌症治疗和诊断的动态策略。我们将重点抓好三个方面的工作。首先， 癌症患者个体化诊疗策略的有效性比较。因为 关于诊断性工作的卫生保健分配的问题涉及战略比较 在数据中明确分配的，常规统计方法不能使用和替代 需要采用人工审查和逆概率加权等方法。在具体目标1中，我们将 开发比较癌症诊断中个性化医疗保健提供策略的方法。AS 一个鼓舞人心的例子是，我们将使用SEER-Medicare将出席的个性化策略与 根据患者和提供者的特点建立多个医疗中心。在具体目标2中，我们将发展 在存在不可测量的混淆的情况下比较个性化策略的方法。 亚群分析在确定治疗最有效的亚群方面很重要，以及 从而实现个性化治疗。因为混淆可能会改变对亚群分析的解释 并导致影响修正和交互作用参数的偏差，我们将制定评估方法 并在子组的设计、分析和解释中纠正未测量的混淆的影响 比较有效性研究中的分析。在具体目标3中，我们将制定方法，以 动态医疗服务策略在癌症治疗中的比较。因为有很多问题 癌症涉及临床决策，取决于患者不断变化的反应或 地方卫生保健系统，即它们是动态决策，常规统计方法不能 使用和替代的方法，如动态边缘结构模型和参数g公式，是 必填项。作为一个鼓舞人心的例子，我们将比较雄激素剥夺的动态策略。 CaPSURE队列中前列腺癌的治疗。在具体目标4中，我们将开发人性化、高级化 高质量和开放获取的软件将分发给癌症研究人员。该项目是对 项目2和3的描述性和推断性目标，严重依赖统计计算核心； 通过行政核心提供的组织基础设施、团队建设战略。 相关性(请参阅说明)： 本项目将为临床疗效研究提供创新和实用的统计方法。 利用大型复杂观测数据对癌症患者进行诊断和治疗的策略 学习。这些方法将导致更好地开发和使用癌症研究的观测数据， 和更好地照顾癌症患者，并将协助临床医生和患者的决策过程， 并将告知临床指南。

This is a proposal to develop, refine, and implement statistical methods for comparative effectiveness research in cancer. Using these methods, we will emulate complex randomized trials of personalized and dynamic strategies for treatment and diagnosis of cancer. We will focus our efforts on three areas. First, the comparative effectiveness of personalized strategies for the diagnostic work-up of cancer patients. Because questions about the allocation of health care for diagnostic work-up involves the comparison of strategies that are clearly assigned in the data, conventional statistical methods cannot be used and alternative methods, like artificial censoring plus inverse probability weighting, are required. In Specific Aim 1, we will develop methods for the comparison of personalized health care delivery strategies in cancer diagnosis. As a motivating example, we will use SEER-Medicare to compare personalized strategies for the attendance to multiple medical centers by patients' and providers' characteristics. In Specific Aim 2, we will develop methods for the comparison of personalized strategies in the presence of unmeasured confounding. Subgroup analyses are important in identifying subpopulations for which treatment is most effective, and thus to personalize treatment. Because confounding may change the interpretation of subgroup analyses and lead to bias in effect modification and interaction parameters, we will develop methodology to assess and correct for the impact of unmeasured confounding in the design, analysis, and interpretation of subgroup analyses in comparative effectiveness research. In Specific Aim 3, we will develop methods for the comparison of dynamic health care delivery strategies in cancer treatment. Because many questions in cancer involve clinical decisions that depend on the evolving responses of patients or the characteristics of the local health care system, i.e., they are dynamic decisions, conventional statistical methods cannot be used and alternative methods, like dynamic marginal structural models and the parametric g-formula, are required. As a motivating example, we will compare dynamic strategies regarding androgen deprivation therapy for prostate cancer in the CaPSURE cohort. In Specific Aim 4, we will develop user-friendly, high quality and open access software to be distributed to cancer researchers. This project complements the descriptive and inferential aims in Projects 2 and 3, and relies heavily on the Statistical Computing Core, and the organizational infrastructure, team building strategies, provided through the Administrative Core. RELEVANCE (See instructions): This project will provide innovative and practical statistical methods to study the effectiveness of clinical strategies for diagnosis and treatment of cancer patients using data from large and complex observational studies. These methods will lead to a better development and use of observational data for cancer research, and a better care of cancer patients, and will assist clinicians and patients in their decision making process, and will inform clinical guidelines.