人口临床试验多样性评估方法:使用现实世界的数据
Hua Chen1, Nnadozie Emechebe2, Sudeep Karve2
1Department of Pharmaceutical Health Outcomes and Policy, College of Pharmacy, University of Houston, USA.
Contemporary clinical trials communications
|February 24, 2025
概括
现实世界数据 (RWD) 可以通过提供疾病特定的人口统计估计来提高临床试验的多样性. 一个新的框架将RWD分析标准化,以创建患者个人资料,以便更具代表性的招生.
科学领域:
- 临床研究 临床研究
- 健康 公平 卫生 公平
- 现实世界的数据分析.
背景情况:
- 临床试验的多样性对于评估不同人群的治疗安全性和有效性至关重要.
- 目前对美国人口普查数据的比较可能不准确地反映疾病特定的人口统计数据.
- 现实世界数据 (RWD) 提供了更具代表性的患者人口统计估计的潜力.
研究的目的:
- 开发和介绍一个标准化的框架,用于从RWD.生成疾病特定的人口统计估计.
- 为潜在的临床试验提供多样化的入学目标.
- 解决开发和传播RWD衍生的人口统计估计的挑战.
主要方法:
- 确定选择合适的RWD数据库的标准 (例如,数据可访问性,人口变量可用性,样本大小).
- 通过针对特定疾病条件的有针对性的文献审查收集并发数据.
- 使用RWD和人口估计开发了特定疾病的人口统计估计资料.
主要成果:
- 该框架允许在使用RWD的特定疾病人群中评估患者人口统计数据.
- 创建了疾病特定的人口资料,以指导多样化的招生.
- 该框架的应用是使用类风湿性关节炎和中风患者数据来说明的.
结论:
- 开发的框架提供了一个标准化的方法来利用RWD用于临床试验多样性规划.
- 从RWD获得的疾病特异性人口统计资料提高了临床试验参与者选择的代表性.
- 该方法支持制定有针对性的招生策略,以改善临床研究中的健康公平.
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