概念化
步伐
过程(计算)
数据科学
人口
集合(抽象数据类型)
医疗保健
医学
专业
计算机科学
人口健康
知识管理
风险分析(工程)
家庭医学
环境卫生
大地测量学
人工智能
经济增长
经济
程序设计语言
地理
操作系统
作者
Lynne Penberthy,Donna R. Rivera,Jennifer L. Lund,Melissa A. Bruno,Anne‐Marie Meyer
摘要
Generating evidence on the use, effectiveness, and safety of new cancer therapies is a priority for researchers, health care providers, payers, and regulators given the rapid pace of change in cancer diagnosis and treatments. The use of real-world data (RWD) is integral to understanding the utilization patterns and outcomes of these new treatments among patients with cancer who are treated in clinical practice and community settings. An initial step in the use of RWD is careful study design to assess the suitability of an RWD source. This pivotal process can be guided by using a conceptual model that encourages predesign conceptualization. The primary types of RWD included are electronic health records, administrative claims data, cancer registries, and specialty data providers and networks. Careful consideration of each data type is necessary because they are collected for a specific purpose, capturing a set of data elements within a certain population for that purpose, and they vary by population coverage and longitudinality. In this review, the authors provide a high-level assessment of the strengths and limitations of each data category to inform data source selection appropriate to the study question. Overall, the development and accessibility of RWD sources for cancer research are rapidly increasing, and the use of these data requires careful consideration of composition and utility to assess important questions in understanding the use and effectiveness of new therapies.
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