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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要：这项研究的主要目的是开发新颖的统计和计算工具来评估新的以及现有的精密癌症医学癌症疗法，主要重点是整合多个数据包括随机对照试验（RCT）和现实世界数据（RWD）在内的来源。所有目标都是成熟的由多学科合作。循证临床决策涉及合成可用来自RCT和RWD在内的多个资源的研究证据。关键RCT是主要证据这确立了局部和全身治疗的肿瘤学等效性或效率。但是，最近系统评价在比较时发现了基于人群的RWD和RCT之间的一致性相同的肿瘤治疗方案。这种差异被认为源于高度选择性的标准用于试验的入学率以及多学科癌症护理的迅速变化的性质。而且，疾病生物肿瘤亚型对生存结果的异质治疗效果尚未在早期的RCT中进行了适当的检查。我们将开发统计工具和软件来评估 RCT和现实世界患者人群的发现，对局部的标准治疗指南进行了重新评估患者的临床和肿瘤亚型针对早期乳腺癌的区域疗法。而提议方法论对疾病类型是不可知的，我们将使用乳腺癌患者作为原理证明提出的方法。具体目的是：（1）估计和评估治疗效率与生存结果的一致在多项研究（例如RCT和RWD）中，使用非参数校准权重进行调整以进行治疗研究之间的选择偏见和异质性；（2）测试存在增强治疗效果并使用半参数等元 - 预测治疗的亚组成员身份 COX模型，并开发一种与一致性的学习工具，用于指导患者治疗选择；（3）推断出对乳腺癌特异性生存的影响通过整合RCT和RWD的数据，RWD中的死亡是未知的；（4）估计罕见的治疗效果乳腺癌的亚型通过将外部骨料数据与单个级别的数据相结合以改善推理效率; （5）开发和传播公开可用的，用户友好的软件，并促进我们方法在多个现有数据库中的可重复性和应用，包括大型人口级别乳腺癌研究的数据和RCT数据。拟议的研究将提高一般方法论通过有效整合多重的比较有效性和精确医学研究的发展数据源。更重要的是，研究结果可以改善基于证据的治疗建议，更好地通知临床医生根据患者的肿瘤亚型和其他特征，从而通过更好地整合临床科学来进一步临床护理。