Strategies to Avoid Confounders and Bias in Observational Studies

Abstract

Observational studies play a crucial role in understanding the etiology, prognosis, and treatment outcomes of rheumatic diseases. Unlike randomized controlled trials (RCTs), observational studies reflect real-world clinical settings, making their findings more generalizable. However, they are inherently susceptible to confounding and bias, which can compromise the validity of their conclusions. Addressing these challenges is critical for advancing evidence-based rheumatology.

This editorial outlines key strategies to identify, mitigate, and report confounding and bias, focusing on practical approaches relevant to rheumatologists, researchers, and editors.

A confounder is an extraneous variable that influences both the independent variable (exposure) and the dependent variable (outcome), distorting the observed relationship. For example, in studies of biologic therapies for psoriatic arthritis, age and disease severity often act as confounders, affecting both treatment allocation and patient outcomes.

Bias occurs when there is a systematic deviation from the true association. It can arise at any stage of the research process, from study design to data collection and analysis. Selection bias, information bias, and measurement bias are the most commonly encountered forms in rheumatic disease studies [1].

At the study design stage, several fundamental methods can help avoid confounding. Randomization and matching, though true randomization is unfeasible in observational studies, can be approximated using techniques like propensity score matching (PSM) to balance groups concerning confounders. Matching based on variables such as age, sex, and disease duration is particularly critical in fields like rheumatology research. Restriction is another approach, where study participants are limited to a homogenous group, such as those with a specific disease severity, which helps reduce confounding but may affect the generalizability of findings. Additionally, techniques such as propensity score weighting or stratification further enhance baseline comparability by mimicking the effects of randomization, ensuring a balanced distribution of confounders across exposure groups [2].

At the statistical stage, several methods are available to address confounding effectively. Stratification involves pre-specifying subgroup analyses based on potential confounders, such as age, sex, race, or comorbidities, to reveal heterogeneity in effects. Multivariable regression includes confounders as covariates in models, for instance, adjusting for age, sex, BMI, and disease activity when examining the relationship between medication use and disease remission [3]. Sensitivity analyses simulate the potential impact of unmeasured confounders using different operational definitions, allowing researchers to assess their influence on study conclusions. Instrumental variable analysis employs a valid instrument, such as a positive or negative outcome control, to minimize unmeasured confounding. Additionally, E-value calculation quantifies the strength of association required between an unmeasured confounder and both exposure and outcome to explain away observed associations, with larger E-values indicating a robust result unlikely to be explained by residual confounding [4].

To reduce selection bias, researchers should carefully define inclusion and exclusion criteria to avoid over-selecting specific subpopulations; for instance, excluding patients with severe comorbidities can bias survival analysis results. Minimizing loss to follow-up is essential in cohort studies, as high attrition rates can lead to bias; this can be mitigated by tracking dropouts and applying methods such as multiple imputation for handling missing data [5]. Additionally, using validated datasets, such as population-based registries like the National Health Insurance Database of Taiwan, or cross-validating findings with other datasets ensures a representative and reliable study sample [6].

To minimize information bias, investigators can adopt several strategies. Using objective, quantitative measures, such as laboratory data and other biomarkers, instead of claim-based codes or subjective self-reports, enhances data accuracy. Sensitivity tests, which apply multiple operational definitions of exposure or outcomes, help reduce measurement bias. To avoid recall bias, especially when collecting data on past exposures like prior medication use, electronic health records are preferable over patient-reported data. Advanced causal inference techniques, such as inverse probability weighting and marginal structural models, can further address time-varying confounders that may change throughout the study period, thereby improving the reliability of study findings [7].

To strengthen the quality and credibility of observational studies in rheumatic diseases, researchers and editors must prioritize strategies to reduce confounding and bias. Adopting advanced statistical methods, improving transparency in reporting, and employing causal inference techniques are essential. As editors and reviewers of IJRD, it is our responsibility to encourage authors to provide a clear methodological description of how they address these issues (Table 1).

The credibility of evidence in rheumatology depends on our collective commitment to methodological rigor. By embedding these principles into research and editorial review, we can produce robust, actionable insights that improve clinical practice and patient outcomes.

All authors were involved in drafting the article or revising it, and all authors approved the final version to be published. Conception and design: P.K., J.C.-C.W. Accessed and verified the underlying data: P.K., R.C., J.C.-C.W. Analysis and interpretation of data: P.K., R.C., J.C.-C.W. Writing (original draft preparation): P.K. Writing (review and editing): R.C., J.C.-C.W.

The authors declare no conflicts of interest.