Quantifying causal effects from observed data using quasi-intervention
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Published:2022-12-21
Issue:1
Volume:22
Page:
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ISSN:1472-6947
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Container-title:BMC Medical Informatics and Decision Making
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language:en
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Short-container-title:BMC Med Inform Decis Mak
Author:
Yang Jinghua,Wan Yaping,Ni Qianxi,Zuo Jianhong,Wang Jin,Zhang Xiapeng,Zhou Lifang
Abstract
Abstract
Background
Causal inference is a crucial element within medical decision-making. There have been many methods for investigating potential causal relationships between disease and treatment options developed in recent years, which can be categorized into two main types: observational studies and experimental studies. However, due to the nature of experimental studies, financial resources, human resources, and patients' ethical considerations, researchers cannot fully control the exposure of the research participants. Furthermore, most existing observational research designs are limited to determining causal relationships and cannot handle observational data, let alone determine the dosages needed for medical research.
Results
This paper presents a new experimental strategy called quasi-intervention for quantifying the causal effect between disease and treatment options in observed data by using a causal inference method, which converts the potential effect of different treatment options on disease into computing differences in the conditional probability. We evaluated the accuracy of the quasi-intervention by quantifying the impact of adjusting Chinese patients’ neutrophil-to-lymphocyte ratio (NLR) on their overall survival (OS) (169 lung cancer patients and 79 controls).The results agree with the literature in this study, consisting of nine papers on cohort studies on the NLR and the prognosis of lung cancer patients, proving that our method is correct.
Conclusion
Taken together, the results imply that quasi-intervention is a promising method for quantifying the causal effect between disease and treatment options without clinical trials, and it could improve confidence about treatment options' efficacy and safety.
Funder
Postgraduate Scientific Research Innovation Project of Hunan Province
Innovation Special Zone Project
Hunan Province’s 2020 Innovative Province Construction Special Project to Fight the New Coronary Pneumonia Epidemic Response Support
Hunan Provincial Education Department Key Project
Hunan Province Graduate Student Research and Innovation Project Funding
Publisher
Springer Science and Business Media LLC
Subject
Health Informatics,Health Policy,Computer Science Applications
Cited by
1 articles.
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