Observational studies based on real-world data are providing increasing amount of evidence for evaluating therapeutic outcomes, which is important for timely decision-making. Although time and costs for data collection could be saved using real-world data, it is significantly more complex to design real world researches with lower risk of bias. In order to enhance the validity of causal inference and to reduce potential risk of bias in real world studies, the Working Group of China Real world data and studies Alliance (China REAL) has formulated recommendations for designing observational studies to evaluate therapeutic outcomes based on real-world data. This guidance introduces design types commonly used in real world research; recommends key elements to consider in observational studies, including sample selection, specifying and allocating exposures, defining study entry and endpoints, and pre-designing statistical analysis protocols; and summarizes potential biases and corresponding control measures in real-world studies. These recommendations introduces key elements in designing observational studies using real-world data, for the purpose of improving the validity of causal inference. However, the application scope of these recommendations may be limited and warrant constant improvement.
Citation：
PENG Xiaoxia, SHU Xiaochen, TAN Jing, WANG Li, NIE Xiaolu, WANG Wen, WEN Zehuai, SUN Xin, On behalf of China Real world data and studies Alliance (ChinaREAL). Technical guidance for designing observational studies to assess therapeutic outcomes using real-world data. Chinese Journal of Evidence-Based Medicine, 2019, 19(7): 779-786. doi: 10.7507/1672-2531.201904164
Copy
Copyright © the editorial department of Chinese Journal of Evidence-Based Medicine of West China Medical Publisher. All rights reserved
| 1. |
U.S. Food & Drug Administration. Use of real-world evidence to support regulatory decision-making for medical devices. Available at: https://www.fda.gov/media/99447/download.
|
| 2. |
Cattell J, Chilukuri S, Levy M. How big data can revolutionize pharmaceutical R&D. Available at: https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/how-big-data-can-revolutionize-pharmaceutical-r-and-d.
|
| 3. |
Anglemyer A, Horvath HT, Bero L. Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials. Cochrane database of Syst Rev, 2014, (4): Mr000034.
|
| 4. |
Hernan MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol, 2016, 183(8): 758-764.
|
| 5. |
Corrao G, Cantarutti A. Building reliable evidence from real-world data: Needs, methods, cautiousness and recommendations. Pul Pharmacol Ther, 2018, 53: 61-67.
|
| 6. |
聂晓路, 彭晓霞. 使用常规收集卫生数据开展观察性研究的报告规范-RECORD 规范. 中国循证医学杂志, 2017, 17(4): 475-487.
|
| 7. |
聂晓璐, 武泽昊, 赵厚宇, 等. 使用常规收集医疗卫生数据开展观察性研究的报告规范(药物流行病学版) RECORD-PE 规范中文版(上). 药物流行病学杂志, 2019, (3): 190-198, 212.
|
| 8. |
聂晓璐, 武泽昊, 赵厚宇, 等. 使用常规收集医疗卫生数据开展观察性研究的报告规范(药物流行病学版) RECORD-PE 规范中文版(下). 药物流行病学杂志, 2019, (4): 57-66.
|
| 9. |
Douglas IJ, Langham J, Bhaskaran K, et al. Orlistat and the risk of acute liver injury: self controlled case series study in UK Clinical Practice Research Datalink. BMJ, 2013, 346: f1936.
|
| 10. |
Nielsen PB, Skjoth F, Sogaard M. Causal Inference From Real-World Data: A Request for Asking the Proper Research Question. J Am Coll Cardiol, 2018, 72(5): 486-488.
|
| 11. |
Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-world evidence-what is it and what can it tell us? N Engl J Med, 2016, 375(23): 2293-2297.
|
- 1. U.S. Food & Drug Administration. Use of real-world evidence to support regulatory decision-making for medical devices. Available at: https://www.fda.gov/media/99447/download.
- 2. Cattell J, Chilukuri S, Levy M. How big data can revolutionize pharmaceutical R&D. Available at: https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/how-big-data-can-revolutionize-pharmaceutical-r-and-d.
- 3. Anglemyer A, Horvath HT, Bero L. Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials. Cochrane database of Syst Rev, 2014, (4): Mr000034.
- 4. Hernan MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol, 2016, 183(8): 758-764.
- 5. Corrao G, Cantarutti A. Building reliable evidence from real-world data: Needs, methods, cautiousness and recommendations. Pul Pharmacol Ther, 2018, 53: 61-67.
- 6. 聂晓路, 彭晓霞. 使用常规收集卫生数据开展观察性研究的报告规范-RECORD 规范. 中国循证医学杂志, 2017, 17(4): 475-487.
- 7. 聂晓璐, 武泽昊, 赵厚宇, 等. 使用常规收集医疗卫生数据开展观察性研究的报告规范(药物流行病学版) RECORD-PE 规范中文版(上). 药物流行病学杂志, 2019, (3): 190-198, 212.
- 8. 聂晓璐, 武泽昊, 赵厚宇, 等. 使用常规收集医疗卫生数据开展观察性研究的报告规范(药物流行病学版) RECORD-PE 规范中文版(下). 药物流行病学杂志, 2019, (4): 57-66.
- 9. Douglas IJ, Langham J, Bhaskaran K, et al. Orlistat and the risk of acute liver injury: self controlled case series study in UK Clinical Practice Research Datalink. BMJ, 2013, 346: f1936.
- 10. Nielsen PB, Skjoth F, Sogaard M. Causal Inference From Real-World Data: A Request for Asking the Proper Research Question. J Am Coll Cardiol, 2018, 72(5): 486-488.
- 11. Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-world evidence-what is it and what can it tell us? N Engl J Med, 2016, 375(23): 2293-2297.
