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

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.