Introduction
Missing data is a common challenge in observational studies, often leading to biased results and reduced statistical power. The recently developed TARMOS (Treatment And Reporting of Missing data in Observational Studies) framework offers a structured approach to handling and reporting missing data, enhancing the reliability and reproducibility of research findings. This blog explores how practitioners can implement this framework to improve their research methodologies.
Understanding the TARMOS Framework
The TARMOS framework consists of three key steps designed to guide researchers in systematically addressing missing data in their studies:
- Step 1: Plan the Analysis - Develop a comprehensive analysis plan that specifies the model and how missing data will be addressed. Consider whether a complete records analysis is valid or if multiple imputation (MI) or another method offers benefits. Conduct a sensitivity analysis if needed.
- Step 2: Examine the Data - Evaluate the data to ensure the planned methods are appropriate, and conduct the preplanned analysis.
- Step 3: Report the Results - Transparently report the analysis results, detailing how missing data were addressed and interpreting the findings in light of the missing data.
Implementing the Framework
To effectively implement the TARMOS framework, practitioners should begin by identifying the substantive research questions and planning the statistical analysis without initially considering the missing data. This involves specifying the exposure, outcome, confounders, and analysis model. The next step is to determine how missing data will be addressed, which involves understanding the missing data mechanism—whether it's missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR).
Multiple imputation is highlighted as a flexible approach for handling missing data, allowing researchers to incorporate auxiliary variables that can reduce bias and improve efficiency. However, it’s crucial to ensure that the imputation model is compatible with the substantive model to avoid fundamental mistakes.
Conducting Sensitivity Analyses
Given the assumptions made about the missing data mechanism, sensitivity analyses are essential to explore the robustness of the conclusions. For instance, in the Avon Longitudinal Study of Parents and Children (ALSPAC) case study, a sensitivity analysis was conducted to test the assumption that missingness in smoking data was associated with smoking itself.
Reporting and Transparency
Transparent reporting is vital to enhance the reproducibility of research findings. Researchers should detail how missing data were addressed in the methods section, including any sensitivity analyses conducted. The results should be interpreted considering the missing data, and any discrepancies should be explained, highlighting the most accurate results based on clinical knowledge.
Conclusion
The TARMOS framework provides a practical guide for researchers to systematically handle and report missing data in observational studies. By adopting this framework, researchers can increase the reliability and reproducibility of their findings, ultimately contributing to more robust scientific knowledge.
To read the original research paper, please follow this link: Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework.
