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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 NameAffiliationE-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  
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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