Methodology progress and challenges on assessing the appropriateness of real-world data.
10.3760/cma.j.cn112338-20210402-00271
- Author:
Yue Lin YU
1
;
Lin ZHUO
2
;
Ruo Gu MENG
3
;
Si Yan ZHAN
4
;
Sheng Feng WANG
1
Author Information
1. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
2. Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing 100191, China.
3. National Institute of Health Data Science, Peking University, Beijing 100191, China.
4. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing 100191, China.
- Publication Type:Review
- MeSH:
Big Data;
Delivery of Health Care;
Humans;
Reproducibility of Results
- From:Chinese Journal of Epidemiology
2022;43(4):578-585
- CountryChina
- Language:Chinese
-
Abstract:
From the perspective of data users, ensuring the relevance and reliability of big data in healthcare and medicine via assessments on data appropriateness is a prerequisite for generating high-quality real-world evidence, which could guarantee good representativeness and generalizability of real-world studies. This review summarized the quality dimensions, definitions, evaluation indexes and calculating methods of assessment on the appropriateness of real-world data (RWD) according to guidance from different countries and international organizations, as well as exploring the opportunities and challenges for better assessing RWD appropriateness.
