Best Practices for Preventing Statistical Pitfalls in Observational Research

By Charmi Patel

July 5, 2024

Introduction

Statistics form the base of Health Economics and Outcomes Research (HEOR), enabling researchers to draw meaningful conclusions from complex data. While experimental studies like randomised controlled trials minimise bias and ensure valid inferences through variable manipulation and randomisation, observational studies, common in fields like epidemiology, lack such control but can still provide valuable insights with careful design. Minimising biases in observational studies is crucial for robust statistical inferences. It is pertinent to outline common errors in classical observational studies, emphasising the importance of focusing on study types, addressing biases, using checklists, transparent reporting, best practices in statistical methods, and rigorous analysis and interpretation.

Study Design Foundation

Observational studies, such as case-controls, cross-sectional, and cohorts, form the backbone of research. Each design offers unique insights into population dynamics and health outcomes. Therefore, careful planning, from defining research questions to sampling strategies, is essential to ensure robust results. Moreover, attention to detail in study design minimises biases and, consequently, enhances the study’s internal and external validity.

Figure 1: Schematic representation of the processes of selection and measurement in cohort, case–control, and cross-sectional studies

Biases in Observational Studies

One of the primary roles of statistics in HEOR is to control for confounding variables and biases, which can distort study results. Biases, such as selection bias, information bias, and confounding variables, can skew results and mislead interpretations. Therefore, addressing biases early on, through proper study design and statistical analysis, is critical. By understanding and mitigating biases, researchers can ensure the accuracy and reliability of study findings. Consequently, this leads to more trustworthy and valid conclusions.

Transparency and reproducibility

Transparency and reproducibility are fundamental principles in HEOR. This practice not only enhances the credibility of the research but also allows other researchers to replicate the study, thereby validating the findings. For example, when analysing data from a national health survey, researchers should clearly outline the steps taken to handle missing data, such as whether imputation methods were used or if missing observations were discarded. Furthermore, specifying the rationale behind categorising covariates and selecting reference levels helps ensure that the analysis is transparent and reproducible.

Avoiding Common Pitfalls

Effective communication of statistical findings is vital in HEOR. The article highlights the need for clear and concise writing, using short sentences, active voice, and strong verbs. This approach improves readability and ensures that the research is accessible to a broad audience, including policymakers, clinicians, and other stakeholders. Moreover, another pitfall is “cherry-picking,” where researchers selectively report data that supports their hypothesis while ignoring contradictory evidence. This practice undermines the objectivity of the analysis and can lead to faulty conclusions.

Conclusion

In conclusion, robust statistical analysis is indispensable in HEOR, underpinning the validity and reliability of research findings. By adhering to best practices in statistical reporting, such as identifying biases, ensuring transparency, avoiding cherry-picking, and communicating clearly, researchers can enhance the credibility and impact of their work. As the article aptly demonstrates, meticulous attention to statistical methods is essential for driving evidence-based interventions and improving public health outcomes.
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