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: 10632124
• 负责人: JING NING
• 金额: $ 34.18万
• 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/10632124
• 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; Complement; Computer software; Consumption; Coupled; Cox Models; Data; Data Aggregation; Data Sources; Databases; Development; Disease; Early treatment; Eligibility Determination; Equation; Equilibrium; Evidence based treatment; Heterogeneity; Individual; Interdisciplinary Study; Isotonic Exercise; Knowledge; Learning; Link; Malignant Neoplasms; Measures; Methodology; Methods; Modeling; Modification; Nature; Oncology; Outcome; Patient-Focused Outcomes; Patients; Population; Population Study; Population-Based Registry; Practice Guidelines; Prediction of Response to Therapy; 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; cancer care; cancer therapy; clinical care; clinical decision-making; clinical practice; clinical subtypes; cohort; comparative effectiveness; computerized tools; data integration; data registry; evidence base; flexibility; hazard; 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; trial enrollment; 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.

项目总结： 这项研究的主要目标是开发新的统计和计算工具来评估新的 以及现有的癌症治疗方法，用于精确癌症医学，主要侧重于集成多个数据 资料来源包括随机对照试验(RCT)和真实世界数据(RWD)。所有的目标都是有动机的 通过多学科协作。基于证据的临床决策涉及综合现有的 来自多种资源的研究证据，包括RCT和RWD。Pivotal RCT是主要证据 这确立了局部和系统治疗在肿瘤学上的等价性或有效性。然而，最近的一次 系统审查发现，当比较基于人口的RWD和RCT时，几乎没有一致之处 同样的肿瘤治疗方案。这种差异被认为源于所使用的高度选择性的标准 与快速变化的多学科癌症护理的性质相结合的试验登记。此外， 疾病生物肿瘤亚型对生存结果的异质性治疗效果尚未得到证实 在早期随机对照试验中进行了充分的检查。我们将开发统计工具和软件来评估 来自随机对照试验和现实世界患者群体的研究结果，重新评估了关于局部- 根据患者的临床和肿瘤亚型进行早期乳腺癌的区域治疗。虽然建议的 方法学与疾病类型无关，我们将使用乳腺癌患者作为原则证据 建议的方法。 具体目标是：(1)估计和评估治疗效果对生存结果的一致性 跨多项研究(例如，随机对照试验和随机对照试验)，使用非参数校准权重调整以进行治疗 研究之间的选择偏差和异质性；(2)测试患者亚组的存在 使用半参数等张曲线增强治疗效果并预测治疗的亚组成员。 COX模型，并开发一种用于阈值识别的一致性辅助学习工具来指导患者 治疗选择；(3)推断治疗效果时对乳腺癌特异性生存率的原因 整合RCT和RWD的数据，RWD的死亡是未知的；(4)评估罕见的治疗效果 通过结合外部聚集数据和个人水平数据来改进推断乳腺癌亚型 效率；及。(5)开发和传播公众可得、方便使用的软件，并促进 我们的方法在多个现有数据库中的重现性和应用，包括大人口级别 用于乳腺癌研究的数据和随机对照试验数据。拟议的研究将推动一般方法论的进步 通过有效地整合多个 数据源。更重要的是，研究结果可能会改进循证治疗建议， 更好地通知临床医生根据患者的肿瘤亚型和其他 通过更好地整合临床科学，从而进一步促进临床护理。

项目成果

Reassessing Estrogen Receptor Expression Thresholds for Breast Cancer Prognosis in HER2-negative Patients Using Shape Restricted Modeling.

使用形状限制模型重新评估 HER2 阴性患者乳腺癌预后的雌激素受体表达阈值。

• DOI: 10.21203/rs.3.rs-3466989/v1
• 发表时间: 2023
• 期刊: Research square
• 作者: Dong,Wenli;Fujii,Takeo;Ning,Jing;Iwase,Toshiaki;Qin,Jing;Ueno,NaotoT;Shen,Yu
• 通讯作者: Shen,Yu

Effectiveness Without Efficacy: Cautionary Tale from a Landmark Breast Cancer Randomized Controlled Trial.

• DOI: 10.7150/jca.79797
• 发表时间: 2023
• 期刊: Journal of Cancer
• 影响因子: 3.9
• 作者: Shen Y;Ning J;Lin HY;Shaitelman SF;Kuerer HM;Bedrosian I
• 通讯作者: Bedrosian I