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.
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.
📊 New research has highlighted the alarming issue of antibiotic misuse among children under 5 in sub-Saharan Africa. The study reveals that most antibiotics are sourced from unqualified providers, with maternal education playing a crucial role in accessing proper care.
This analysis calls for urgent interventions to address healthcare disparities and combat antimicrobial resistance (AMR). Ensuring safe antibiotic use is essential for safeguarding the health of our youngest populations.
Read more about the findings and implications in the latest issue of *International Health*!
Olympus Corporation has partnered with Proximie to improve operating room technology, enhancing collaboration significantly among healthcare professionals. This partnership is set to improve patient outcomes and pave the way for global equity in healthcare. Discover how this innovative health tech platform transforms surgical practices.
🩺 The UK’s NICE has recommended Elranatamab for use with managed access to treat relapsed and refractory multiple myeloma in adults.
This decision allows eligible patients to access this innovative treatment while further data is collected to address uncertainties about its long-term effectiveness.
Key features of the Managed Access Agreement (MAA) include:
– Continued data collection from the ongoing MagnetisMM-3 clinical trial and other sources, including the Systemic Anti-Cancer Therapy (SACT) dataset.
– A focus on gathering long-term data on overall survival (OS), progression-free survival (PFS), and the use of intravenous immunoglobulin (IVIg).
– Pfizer Ltd.’s responsibility to submit an evidence submission to NICE for a guidance update by July 2026.
When you partner with Syenza, it’s like a Nuclear Fusion.
Our expertise are combined with yours, and we contribute clinical expertise and advanced degrees in
health policy, health economics, systems analysis, public finance, business, and project management.
You’ll also feel our high-impact global and local perspectives with cultural intelligence.
