Statistical Methods for Integration of Multiple Data Sources toward Precision Cancer Medicine

整合多个数据源以实现精准癌症医学的统计方法

• 批准号: 10415744
• 负责人: JING NING
• 金额: $ 34.87万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10415744
• 关键词: Agreement; Algorithmic Analysis; Algorithms; Biological; Breast Cancer Patient; Calibration; Cause of Death; Cessation of life; Characteristics; Clinical; Clinical Sciences; Comparative Effectiveness Research; Computer software; Consumption; Coupled; Cox Models; Data; Data Aggregation; Data Sources; Databases; Development; Disease; Early treatment; Eligibility Determination; Enrollment; Equation; Equilibrium; Evidence based treatment; Gold; Heterogeneity; Individual; Interdisciplinary Study; Isotonic Exercise; Knowledge; Learning; Link; Malignant Neoplasms; Measures; Methodology; Methods; Modeling; Modification; Nature; Outcome; Patient-Focused Outcomes; Patients; Population; Population Study; Population-Based Registry; Practice Guidelines; Probability; Randomized Controlled Trials; Rare Diseases; Recommendation; Reproducibility; Research; Resources; Selection Bias; Selection for Treatments; Source; Statistical Methods; Statistical Models; Subgroup; Testing; Time; Treatment Efficacy; Treatment Protocols; Tumor Subtype; Variant; Weight; anticancer research; base; cancer care; cancer therapy; clinical care; clinical decision-making; clinical practice; clinical subtypes; cohort; comparative effectiveness; computerized tools; data registry; evidence base; flexibility; hazard; heterogenous data; improved; individual patient; insight; malignant breast neoplasm; method development; multidisciplinary; multiple data sources; neoplasm registry; novel; optimal treatments; patient population; patient subsets; population based; precision medicine; precision oncology; prediction algorithm; public health relevance; semiparametric; standard care; stem; survival outcome; systematic review; tool; treatment arm; treatment effect; treatment guidelines; tumor; user friendly software

Project Summary: The primary objective of this research is to develop novel statistical and computational tools to evaluate new and existing cancer therapies for precision cancer medicine, with a principal focus on integrating multiple data sources including randomized controlled trials (RCT) and real world data (RWD). All of the aims are motivated by multidisciplinary collaboration. Evidence-based clinical decision making involves synthesizing available research evidence from multiple resources, including RCT and RWD. Pivotal RCTs are the primary evidence that established the oncologic equivalence or efficacy of local and systemic treatments. However, a recent systematic review found little agreement between population-based RWD and RCTs when comparing the same oncologic treatment regimens. This difference is thought to stem from the highly selective criteria used for trial enrollment coupled with the rapidly changing nature of multidisciplinary cancer care. Moreover, heterogeneous treatment effects by disease biologic tumor subtype on survival outcomes has not been examined sufficiently in early RCTs. We will develop statistical tools and software to evaluate the agreement of findings from RCTs and the real-world patient population, reassessing standard treatment guidelines on local- regional therapies for early-stage breast cancer by patients’ clinical and tumor subtypes. While the proposed methodology is agnostic to disease type, we will use breast cancer patients as proof of principle for the approaches proposed. The specific aims are: (1) to estimate and assess the agreement of treatment efficacy on survival outcomes across multiple studies (e.g., RCT and RWD) using nonparametric calibration weights to adjust for treatment selection bias and heterogeneity between studies; (2) to test the existence of a subgroup of patients with enhanced treatment effect and predict subgroup membership of a treatment using a semi-parametric isotonic- Cox model, and to develop a concordance-assisted learning tool for threshold identification to guide patient treatment selection; (3) to infer the treatment effects on breast cancer-specific survival when the cause of death is unknown in RWD by integrating data from RCT and RWD; (4) to estimate treatment effect for rare subtypes of breast cancer by combining external aggregate data with individual-level data to improve inference efficiency; and (5) to develop and disseminate publicly available, user-friendly software and facilitate the reproducibility and applications of our methods to multiple existing databases, including large-population-level data and RCT data for breast cancer research. The proposed research will advance general methodologic development in comparative effectiveness and precision medicine research by efficiently integrating multiple data sources. More importantly, the study findings could improve evidence-based treatment recommendations, better informing clinicians to select optimal treatments according to patients’ tumor subtypes and other characteristics, thus furthering clinical care via better integration of clinical science.

项目概要： 这项研究的主要目标是开发新的统计和计算工具，以评估新的 以及现有的癌症治疗方法，用于精准癌症医学，主要关注整合多种数据 来源包括随机对照试验（RCT）和真实的世界数据（RWD）。所有的目标都是 多学科合作。循证临床决策包括综合现有的 来自多个资源的研究证据，包括RCT和RWD。随机对照试验是主要证据 确定局部和全身治疗的肿瘤等效性或疗效。但最近的一项 系统性综述发现，当比较 相同的肿瘤治疗方案。这种差异被认为是源于所使用的高度选择性的标准 对于试验招募，再加上多学科癌症护理的快速变化的性质。此外，委员会认为， 疾病生物学肿瘤亚型对生存结局的异质性治疗效果尚未被 在早期的RCT中得到了充分的检验。我们将开发统计工具和软件，以评估 来自RCT和真实世界患者人群的结果，重新评估当地标准治疗指南， 根据患者的临床和肿瘤亚型对早期乳腺癌进行区域治疗。虽然拟议的 方法学对疾病类型是不可知的，我们将使用乳腺癌患者作为原则的证明， 提出的办法。 具体目标是：（1）估计和评估治疗效果对生存结局的一致性 在多个研究中（例如，RCT和RWD）使用非参数校准权重调整治疗 研究之间的选择偏倚和异质性;（2）检验是否存在一个亚组的患者， 增强的治疗效果，并使用半参数等渗- 考克斯模型，并开发一个一致性辅助学习工具的阈值识别，以指导患者 治疗选择;（3）当乳腺癌的病因 通过整合RCT和RWD的数据，RWD中的死亡未知;（4）估计罕见的治疗效果 通过将外部聚集数据与个体水平数据相结合来改进推断， （5）开发和传播方便用户的公开软件， 我们的方法在多个现有数据库中的重现性和应用，包括大人口水平 乳腺癌研究的随机对照试验数据。该研究将推进一般方法学 通过有效整合多种药物，发展比较有效性和精准医学研究 数据源更重要的是，研究结果可以改善循证治疗建议， 更好地告知临床医生根据患者的肿瘤亚型和其他 因此，通过更好地整合临床科学来促进临床护理。