Bias Adjustment Methods for Analysis of a Non-randomized Controlled Trials of Right Heart Catheterization for Patients in ICU

Yi Xu; Yeqian Liu

doi:doi:10.11648/j.bsi.20210602.12

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Bias Adjustment Methods for Analysis of a Non-randomized Controlled Trials of Right Heart Catheterization for Patients in ICU

Yi Xu, Yeqian Liu

Published in Biomedical Statistics and Informatics (Volume 6, Issue 2)

Received: 21 June 2021 Accepted: 7 July 2021 Published: 19 July 2021

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Abstract

Kaplan-Meier estimate or proportional hazards regression is commonly used directly to estimate the effect of treatment on survival time in randomized clinical studies. However, such methods usually lead to biased estimate of treatment effect in non-randomized or observational studies because the treated and untreated groups cannot be compared directly due to potential systematical difference in baseline characteristics. Researchers have developed various methods for adjusting biased estimates by balancing out confounding covariates such as matching or stratification on propensity score, inverse probability treatment weighting. However, very few studies have compared the performance of these methods. In this paper, we conducted an intensive case study to compare the performance of various bias correction methods for non-randomized studies and applied these methods to the right-heart catheterization (RHC) study to investigate the impact of RHC on the survival time of critically ill patients in the intensive care unit. Our findings suggest that, after bias adjustment procedures, RHC was associated with increased mortality. The inverse probability treatment weighting outperforms other bias adjustment methods in terms of bias, mean-squared error of the hazard ratio estimators, type I error and power. In general, a combination of these bias adjustment methods could be applied to make the estimation of the treatment effect more efficient.

Published in	Biomedical Statistics and Informatics (Volume 6, Issue 2)
DOI	10.11648/j.bsi.20210602.12
Page(s)	32-41
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Confounder, Right Heart Catheterization, Propensity Score, Proportional Hazards Model, Kaplan-Meier Estimate, Non-randomized Study

References

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[4]	Granger, E., Watkins, T., Sergeant, J. C. (2020). A review of the use of propensity score diagnostics in papers published in high-ranking medical journals. BMC Medical Research Methodolgy. 20, 132.
[5]	Connors, A. F., Speroff, T. & Dawson, N. (1996). The effectiveness of right heart catheterization in the initial care of critically ill patients. Journal of American Medical Association. 18, 294-1295.
[6]	Thavaneswaran, A., Lix, L. (2008). Propensity score matching in observational studies. Manitoba Centre for Health Policy.
[7]	Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research. 46 (3): 399-424.
[8]	Austin, P. C. (2009). Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Statistics in Medicine. 28 (25): 3083-107.
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[13]	Rosenbaum, P. R., Rubin, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association. 79, 516–524.
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[15]	Lunceford, J. K., Davidian, M. (2004). Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative. Statistics in Medicine 23, 2937-2960.

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Yi Xu, Yeqian Liu. (2021). Bias Adjustment Methods for Analysis of a Non-randomized Controlled Trials of Right Heart Catheterization for Patients in ICU. Biomedical Statistics and Informatics, 6(2), 32-41. https://doi.org/10.11648/j.bsi.20210602.12

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ACS Style

Yi Xu; Yeqian Liu. Bias Adjustment Methods for Analysis of a Non-randomized Controlled Trials of Right Heart Catheterization for Patients in ICU. Biomed. Stat. Inform. 2021, 6(2), 32-41. doi: 10.11648/j.bsi.20210602.12

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AMA Style

Yi Xu, Yeqian Liu. Bias Adjustment Methods for Analysis of a Non-randomized Controlled Trials of Right Heart Catheterization for Patients in ICU. Biomed Stat Inform. 2021;6(2):32-41. doi: 10.11648/j.bsi.20210602.12

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@article{10.11648/j.bsi.20210602.12,
  author = {Yi Xu and Yeqian Liu},
  title = {Bias Adjustment Methods for Analysis of a Non-randomized Controlled Trials of Right Heart Catheterization for Patients in ICU},
  journal = {Biomedical Statistics and Informatics},
  volume = {6},
  number = {2},
  pages = {32-41},
  doi = {10.11648/j.bsi.20210602.12},
  url = {https://doi.org/10.11648/j.bsi.20210602.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20210602.12},
  abstract = {Kaplan-Meier estimate or proportional hazards regression is commonly used directly to estimate the effect of treatment on survival time in randomized clinical studies. However, such methods usually lead to biased estimate of treatment effect in non-randomized or observational studies because the treated and untreated groups cannot be compared directly due to potential systematical difference in baseline characteristics. Researchers have developed various methods for adjusting biased estimates by balancing out confounding covariates such as matching or stratification on propensity score, inverse probability treatment weighting. However, very few studies have compared the performance of these methods. In this paper, we conducted an intensive case study to compare the performance of various bias correction methods for non-randomized studies and applied these methods to the right-heart catheterization (RHC) study to investigate the impact of RHC on the survival time of critically ill patients in the intensive care unit. Our findings suggest that, after bias adjustment procedures, RHC was associated with increased mortality. The inverse probability treatment weighting outperforms other bias adjustment methods in terms of bias, mean-squared error of the hazard ratio estimators, type I error and power. In general, a combination of these bias adjustment methods could be applied to make the estimation of the treatment effect more efficient.},
 year = {2021}
}

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TY  - JOUR
T1  - Bias Adjustment Methods for Analysis of a Non-randomized Controlled Trials of Right Heart Catheterization for Patients in ICU
AU  - Yi Xu
AU  - Yeqian Liu
Y1  - 2021/07/19
PY  - 2021
N1  - https://doi.org/10.11648/j.bsi.20210602.12
DO  - 10.11648/j.bsi.20210602.12
T2  - Biomedical Statistics and Informatics
JF  - Biomedical Statistics and Informatics
JO  - Biomedical Statistics and Informatics
SP  - 32
EP  - 41
PB  - Science Publishing Group
SN  - 2578-8728
UR  - https://doi.org/10.11648/j.bsi.20210602.12
AB  - Kaplan-Meier estimate or proportional hazards regression is commonly used directly to estimate the effect of treatment on survival time in randomized clinical studies. However, such methods usually lead to biased estimate of treatment effect in non-randomized or observational studies because the treated and untreated groups cannot be compared directly due to potential systematical difference in baseline characteristics. Researchers have developed various methods for adjusting biased estimates by balancing out confounding covariates such as matching or stratification on propensity score, inverse probability treatment weighting. However, very few studies have compared the performance of these methods. In this paper, we conducted an intensive case study to compare the performance of various bias correction methods for non-randomized studies and applied these methods to the right-heart catheterization (RHC) study to investigate the impact of RHC on the survival time of critically ill patients in the intensive care unit. Our findings suggest that, after bias adjustment procedures, RHC was associated with increased mortality. The inverse probability treatment weighting outperforms other bias adjustment methods in terms of bias, mean-squared error of the hazard ratio estimators, type I error and power. In general, a combination of these bias adjustment methods could be applied to make the estimation of the treatment effect more efficient.
VL  - 6
IS  - 2
ER  -

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Author Information

Yi Xu

Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, USA
Yeqian Liu

Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, USA

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APA Style

Yi Xu, Yeqian Liu. (2021). Bias Adjustment Methods for Analysis of a Non-randomized Controlled Trials of Right Heart Catheterization for Patients in ICU. Biomedical Statistics and Informatics, 6(2), 32-41. https://doi.org/10.11648/j.bsi.20210602.12

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ACS Style

Yi Xu; Yeqian Liu. Bias Adjustment Methods for Analysis of a Non-randomized Controlled Trials of Right Heart Catheterization for Patients in ICU. Biomed. Stat. Inform. 2021, 6(2), 32-41. doi: 10.11648/j.bsi.20210602.12

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AMA Style

Yi Xu, Yeqian Liu. Bias Adjustment Methods for Analysis of a Non-randomized Controlled Trials of Right Heart Catheterization for Patients in ICU. Biomed Stat Inform. 2021;6(2):32-41. doi: 10.11648/j.bsi.20210602.12

Copy | Download

@article{10.11648/j.bsi.20210602.12,
  author = {Yi Xu and Yeqian Liu},
  title = {Bias Adjustment Methods for Analysis of a Non-randomized Controlled Trials of Right Heart Catheterization for Patients in ICU},
  journal = {Biomedical Statistics and Informatics},
  volume = {6},
  number = {2},
  pages = {32-41},
  doi = {10.11648/j.bsi.20210602.12},
  url = {https://doi.org/10.11648/j.bsi.20210602.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.bsi.20210602.12},
  abstract = {Kaplan-Meier estimate or proportional hazards regression is commonly used directly to estimate the effect of treatment on survival time in randomized clinical studies. However, such methods usually lead to biased estimate of treatment effect in non-randomized or observational studies because the treated and untreated groups cannot be compared directly due to potential systematical difference in baseline characteristics. Researchers have developed various methods for adjusting biased estimates by balancing out confounding covariates such as matching or stratification on propensity score, inverse probability treatment weighting. However, very few studies have compared the performance of these methods. In this paper, we conducted an intensive case study to compare the performance of various bias correction methods for non-randomized studies and applied these methods to the right-heart catheterization (RHC) study to investigate the impact of RHC on the survival time of critically ill patients in the intensive care unit. Our findings suggest that, after bias adjustment procedures, RHC was associated with increased mortality. The inverse probability treatment weighting outperforms other bias adjustment methods in terms of bias, mean-squared error of the hazard ratio estimators, type I error and power. In general, a combination of these bias adjustment methods could be applied to make the estimation of the treatment effect more efficient.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Bias Adjustment Methods for Analysis of a Non-randomized Controlled Trials of Right Heart Catheterization for Patients in ICU
AU  - Yi Xu
AU  - Yeqian Liu
Y1  - 2021/07/19
PY  - 2021
N1  - https://doi.org/10.11648/j.bsi.20210602.12
DO  - 10.11648/j.bsi.20210602.12
T2  - Biomedical Statistics and Informatics
JF  - Biomedical Statistics and Informatics
JO  - Biomedical Statistics and Informatics
SP  - 32
EP  - 41
PB  - Science Publishing Group
SN  - 2578-8728
UR  - https://doi.org/10.11648/j.bsi.20210602.12
AB  - Kaplan-Meier estimate or proportional hazards regression is commonly used directly to estimate the effect of treatment on survival time in randomized clinical studies. However, such methods usually lead to biased estimate of treatment effect in non-randomized or observational studies because the treated and untreated groups cannot be compared directly due to potential systematical difference in baseline characteristics. Researchers have developed various methods for adjusting biased estimates by balancing out confounding covariates such as matching or stratification on propensity score, inverse probability treatment weighting. However, very few studies have compared the performance of these methods. In this paper, we conducted an intensive case study to compare the performance of various bias correction methods for non-randomized studies and applied these methods to the right-heart catheterization (RHC) study to investigate the impact of RHC on the survival time of critically ill patients in the intensive care unit. Our findings suggest that, after bias adjustment procedures, RHC was associated with increased mortality. The inverse probability treatment weighting outperforms other bias adjustment methods in terms of bias, mean-squared error of the hazard ratio estimators, type I error and power. In general, a combination of these bias adjustment methods could be applied to make the estimation of the treatment effect more efficient.
VL  - 6
IS  - 2
ER  -

Copy | Download