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.

项目概要： 这项研究的主要目标是开发新颖的统计和计算工具来评估新的 以及用于精准癌症医学的现有癌症疗法，主要侧重于整合多个数据 来源包括随机对照试验（RCT）和真实世界数据（RWD）。所有的目标都有动力 通过多学科合作。基于证据的临床决策涉及综合可用的 来自多种资源的研究证据，包括 RCT 和 RWD。关键随机对照试验是主要证据 建立了局部和全身治疗的肿瘤学等效性或功效。然而，最近的一则 系统评价发现，在比较基于人群的 RWD 和 RCT 之间几乎没有一致性。 相同的肿瘤治疗方案。这种差异被认为源于所使用的高度选择性标准 试验入组以及多学科癌症护理快速变化的性质。而且， 疾病生物肿瘤亚型对生存结果的异质治疗效果尚未得到证实 在早期的随机对照试验中得到了充分的研究。我们将开发统计工具和软件来评估协议的一致性 随机对照试验和现实世界患者群体的研究结果，重新评估当地的标准治疗指南 根据患者的临床和肿瘤亚型对早期乳腺癌进行区域治疗。虽然提议的 方法与疾病类型无关，我们将使用乳腺癌患者作为原理证明 提出的方法。 具体目标是：（1）估计和评估治疗效果与生存结果的一致性 跨多项研究（例如 RCT 和 RWD），使用非参数校准权重来调整治疗 研究之间的选择偏差和异质性； (2) 检验是否存在以下患者亚组 使用半参数等渗增强治疗效果并预测治疗的亚组成员资格 Cox模型，并开发一种用于阈值识别的索引辅助学习工具来指导患者 治疗选择； (3) 当病因如下时，推断治疗对乳腺癌特异性生存的影响： 通过整合 RCT 和 RWD 的数据，RWD 中的死亡未知； (4) 估计罕见病的治疗效果 通过将外部聚合数据与个体数据相结合以改进推断来确定乳腺癌的亚型 效率; (5) 开发和传播公开可用的、用户友好的软件并促进 我们的方法对多个现有数据库（包括大群体水平）的再现性和应用 乳腺癌研究的数据和随机对照试验数据。拟议的研究将推进一般方法论 通过有效整合多种方法，发展比较有效性和精准医学研究 数据来源。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情况选择最佳治疗方法 特点，从而通过更好地整合临床科学来促进临床护理。

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