Development and Validation of a High-Quality Composite Real-World Mortality Endpoint.
Development and Validation of a High-Quality Composite Real-World Mortality Endpoint.
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DOI：
10.1111/1475-6773.12872
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发表时间：
2018-12
影响因子：
3.4
通讯作者：
Abernethy AP
中科院分区：
文献类型：
--
作者：
Curtis MD;Griffith SD;Tucker M;Taylor MD;Capra WB;Carrigan G;Holzman B;Torres AZ;You P;Arnieri B;Abernethy AP
创建高质量的电子健康记录(EHR)派生的死亡率数据集，用于前瞻性和前瞻性的真实世界证据生成。肿瘤学EHR数据，辅以外部商业和美国社会保障死亡指数数据，以国家死亡指数(NDI)为基准。我们开发了一个最新的、可链接的、高质量的死亡率变量，该变量从多个数据来源合并，以补充EHR数据，并以美国最完整的死亡率数据NDI为基准。这里报告了死亡率变量2.0版的数据质量。对于晚期非小细胞肺癌，死亡率信息的敏感性从EHR结构化数据中的66%提高到复合数据集中的91%，与NDI相比具有更高的日期一致性。对于晚期黑色素瘤、转移性结直肠癌和转移性乳腺癌，最终变量的敏感度为85%至88%。Kaplan-Meier生存分析表明，与基于NDI的估计相比，提高死亡率数据的完整性最大限度地减少了对生存的高估。要使EHR得出的数据产生可靠的真实证据，它需要具有已知的和足够高的质量。考虑到死亡率数据的完整性对生存终点的影响，我们强调了数据质量评估的重要性，并倡导以NDI为基准。
To create a high‐quality electronic health record (EHR)–derived mortality dataset for retrospective and prospective real‐world evidence generation. Oncology EHR data, supplemented with external commercial and US Social Security Death Index data, benchmarked to the National Death Index (NDI). We developed a recent, linkable, high‐quality mortality variable amalgamated from multiple data sources to supplement EHR data, benchmarked against the highest completeness U.S. mortality data, the NDI. Data quality of the mortality variable version 2.0 is reported here. For advanced non‐small‐cell lung cancer, sensitivity of mortality information improved from 66 percent in EHR structured data to 91 percent in the composite dataset, with high date agreement compared to the NDI. For advanced melanoma, metastatic colorectal cancer, and metastatic breast cancer, sensitivity of the final variable was 85 to 88 percent. Kaplan–Meier survival analyses showed that improving mortality data completeness minimized overestimation of survival relative to NDI‐based estimates. For EHR‐derived data to yield reliable real‐world evidence, it needs to be of known and sufficiently high quality. Considering the impact of mortality data completeness on survival endpoints, we highlight the importance of data quality assessment and advocate benchmarking to the NDI.
影响因子：
5
作者：
CALLE, EE;TERRELL, DD
通讯作者：
TERRELL, DD
影响因子：
10.3
作者：
Khozin, Sean;Blumenthal, Gideon M.;Pazdur, Richard
通讯作者：
Pazdur, Richard