Addressing confounders in observational comparative effectiveness research: Methods, software, and reporting standards

Yingying Wang; Wenwen Li; Ling Wang; Yongyue Wei; Yang Zhao; Guoshuang Feng; Lihong Huang

doi:10.59717/j.xinn-med.2026.100187

The Innovation Medicine > 2026 Vol. 4 > No. 1 > 100187

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Addressing confounders in observational comparative effectiveness research: Methods, software, and reporting standards

1.
Department of Biostatistics, Zhongshan Hospital, Fudan University, Shanghai 200032, China
2.
Department of Health Statistics, Air Force Medical University, Xi'an 710032, China
3.
Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing 100191, China
4.
Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
5.
Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
6.
Department of Biostatistics, Nanjing Medical University, Nanjing 211166, China
7.
Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China
8.
China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing 211166, China
9.
Ministry of Education Key Laboratory for Modern Toxicology, School of Public Health, Nanjing Medical University, Nanjing 211166, China
10.
Big Data Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
11.
These authors contributed equally

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Corresponding authors: huang.lihong@zs-hospital.sh.cn (L.H.); glxfgsh@163.com (G.F.)
Received Date: August 22, 2025

Accepted Date: December 31, 2025

Published Online: January 03, 2026

The Innovation Medicine 4(1), https://doi.org/10.59717/j.xinn-med.2026.100187 | Cite this article

GRAPHICAL ABSTRACT

GRAPHICAL ABSTRACT

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PUBLIC SUMMARY

PUBLIC SUMMARY
1. Confounding is the primary threat to validity in observational comparative effectiveness research (CER).
  
  Multiple statistical methods have been adopted to address measured, unmeasured, and time-varying confounders.
  
  Sensitivity analyses are essential to evaluate the robustness of findings in the presence of confounders.
  
  Writing guidelines and reporting standards are fundamental to ensure transparency and reproducibility.

ABSTRACT

ABSTRACT

Confounding poses a critical threat to the validity of observational comparative effectiveness research (CER) by distorting the estimated associations between treatments/exposures and outcomes. This challenge is particularly pronounced in real-world data, given the absence of randomization and the prevalence of unmeasured and time-varying confounders. Thus, the observed associations may be attributable to differences other than the treatments/exposures of interest and causality cannot be assumed. By synthesizing theoretical foundations, practical applications, software implementations, and reporting standards, this study provides insights into statistical approaches for identifying and handling confounders in observational CER, in order to strengthen the validity of causal inference in real-world evidence, and ultimately enhance the quality and reliability of observational CER.

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Wang Y., Li W., Wang L., et al. (2026). Addressing confounders in observational comparative effectiveness research: Methods, software, and reporting standards. The Innovation Medicine 4:100187. https://doi.org/10.59717/j.xinn-med.2026.100187

Wang Y., Li W., Wang L., et al. (2026). Addressing confounders in observational comparative effectiveness research: Methods, software, and reporting standards. The Innovation Medicine 4:100187. https://doi.org/10.59717/j.xinn-med.2026.100187

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