Statistical consideration about causal inference to obtain real-world evidence |
Received:August 31, 2021 Revised:October 19, 2021 Click here to download the full text |
Citation of this paper:HUANG Li-hong,ZHAO Yang,WANG Ling,LI Chen,CHEN Feng.Statistical consideration about causal inference to obtain real-world evidence[J].Chinese Journal of Clinical Medicine,2021,28(5):738-743 |
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Author Name | Affiliation | E-mail | HUANG Li-hong | Department of Biostatistics, Zhongshan Hospital, Fudan University, Shanghai 200032, China CSCO Biostatistics Expert Committee RWE Working Group | | ZHAO Yang | Public Health School, Nanjing Medical University, Nanjing 211166, Jiangsu, China CSCO Biostatistics Expert Committee RWE Working Group | Yzhao@njmu.edu.cn | WANG Ling | Department of Health Statistics, Air Military Medical University, Xi'an 710032, Shaanxi, China CSCO Biostatistics Expert Committee RWE Working Group | | LI Chen | Department of Health Statistics, Air Military Medical University, Xi'an 710032, Shaanxi, China CSCO Biostatistics Expert Committee RWE Working Group | | CHEN Feng | Public Health School, Nanjing Medical University, Nanjing 211166, Jiangsu, China | |
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Abstract:Association is often observed in medical research, but causal inference is the ultimate goal of clinical study. The criteria for determining causality include association temporality, strength, consistency, specificity, coherence, dose-response relationship, biologic plausibility and experimental evidence. In order to obtain causality, there are many causal inference elements in clinical study design and analysis. This study analyzes the influence of confounders on causality, and discusses the causal inference elements in three important topics:randomization, analysis of data sets and subgroup analysis. Medical researches should be fully aware of the causal elements in clinical study, so as to currently understand the level of evidence that can be provided by study, and try to produce high-level medical evidence in practice work. |
keywords:clinical study study design statistical analysis causal inference |
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