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Data Imputation for Clinical Trial Emulation: A Case Study on Impact of Intracranial Pressure Monitoring for Traumatic Brain Injury
Randomized clinical trial emulation using real-world data is significant for treatment effect evaluation. Missing values are common in the observational data. Handling missing data improperly would cause biased estimations and invalid conclusions. However, discussions on how to address this issue in...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9915810/ https://www.ncbi.nlm.nih.gov/pubmed/36778272 http://dx.doi.org/10.1101/2023.01.29.23285172 |
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author | Zhao, Zhizhen Liu, Ruoqi Groner, Jonathan I. Xiang, Henry Zhang, Ping |
author_facet | Zhao, Zhizhen Liu, Ruoqi Groner, Jonathan I. Xiang, Henry Zhang, Ping |
author_sort | Zhao, Zhizhen |
collection | PubMed |
description | Randomized clinical trial emulation using real-world data is significant for treatment effect evaluation. Missing values are common in the observational data. Handling missing data improperly would cause biased estimations and invalid conclusions. However, discussions on how to address this issue in causal analysis using observational data are still limited. Multiple imputation by chained equations (MICE) is a popular approach to fill in missing data. In this study, we combined multiple imputation with propensity score weighted model to estimate the average treatment effect (ATE). We compared various multiple imputation (MI) strategies and a complete data analysis on two benchmark datasets. The experiments showed that data imputations had better performances than completely ignoring the missing data, and using different imputation models for different covariates gave a high precision of estimation. Furthermore, we applied the optimal strategy on a medical records data to evaluate the impact of ICP monitoring on inpatient mortality of traumatic brain injury (TBI). The experiment details and code are available at https://github.com/Zhizhen-Zhao/IPTW-TBI. |
format | Online Article Text |
id | pubmed-9915810 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-99158102023-02-11 Data Imputation for Clinical Trial Emulation: A Case Study on Impact of Intracranial Pressure Monitoring for Traumatic Brain Injury Zhao, Zhizhen Liu, Ruoqi Groner, Jonathan I. Xiang, Henry Zhang, Ping medRxiv Article Randomized clinical trial emulation using real-world data is significant for treatment effect evaluation. Missing values are common in the observational data. Handling missing data improperly would cause biased estimations and invalid conclusions. However, discussions on how to address this issue in causal analysis using observational data are still limited. Multiple imputation by chained equations (MICE) is a popular approach to fill in missing data. In this study, we combined multiple imputation with propensity score weighted model to estimate the average treatment effect (ATE). We compared various multiple imputation (MI) strategies and a complete data analysis on two benchmark datasets. The experiments showed that data imputations had better performances than completely ignoring the missing data, and using different imputation models for different covariates gave a high precision of estimation. Furthermore, we applied the optimal strategy on a medical records data to evaluate the impact of ICP monitoring on inpatient mortality of traumatic brain injury (TBI). The experiment details and code are available at https://github.com/Zhizhen-Zhao/IPTW-TBI. Cold Spring Harbor Laboratory 2023-02-02 /pmc/articles/PMC9915810/ /pubmed/36778272 http://dx.doi.org/10.1101/2023.01.29.23285172 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Zhao, Zhizhen Liu, Ruoqi Groner, Jonathan I. Xiang, Henry Zhang, Ping Data Imputation for Clinical Trial Emulation: A Case Study on Impact of Intracranial Pressure Monitoring for Traumatic Brain Injury |
title | Data Imputation for Clinical Trial Emulation: A Case Study on Impact of Intracranial Pressure Monitoring for Traumatic Brain Injury |
title_full | Data Imputation for Clinical Trial Emulation: A Case Study on Impact of Intracranial Pressure Monitoring for Traumatic Brain Injury |
title_fullStr | Data Imputation for Clinical Trial Emulation: A Case Study on Impact of Intracranial Pressure Monitoring for Traumatic Brain Injury |
title_full_unstemmed | Data Imputation for Clinical Trial Emulation: A Case Study on Impact of Intracranial Pressure Monitoring for Traumatic Brain Injury |
title_short | Data Imputation for Clinical Trial Emulation: A Case Study on Impact of Intracranial Pressure Monitoring for Traumatic Brain Injury |
title_sort | data imputation for clinical trial emulation: a case study on impact of intracranial pressure monitoring for traumatic brain injury |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9915810/ https://www.ncbi.nlm.nih.gov/pubmed/36778272 http://dx.doi.org/10.1101/2023.01.29.23285172 |
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