A review on the utility of sensitivity analysis in trauma research: Methods and challenges from recent trauma studies

In trauma research, the complexity of patient presentations, variability across clinical environments, and diversity in outcomes often introduce substantial uncertainty into the interpretation of findings. Sensitivity analysis (SA) is a vital methodological tool for examining the stability and reliability of research conclusions by testing how they respond to changes in analytical methods, model structures, unmeasured confounding, and foundational assumptions.

A literature review was conducted to provide an overview of current knowledge and updates on the application of sensitivity analysis in trauma research. A search was performed in PubMed and Google Scholar for studies conducted in humans and published in English between May 2000 and May 2025.

This review explored the crucial role that SA plays in trauma studies, outlining key techniques, including addressing missing data through multiple or single imputation, evaluating protocol noncompliance, adjusting for baseline imbalances, validating statistical assumptions, and conducting subgroup analyses. These strategies are particularly relevant in trauma research, where data fragmentation, wide population variability, and ethical challenges are common. Drawing on landmark trauma studies including PROMPTT, PROPPR, PATCH-Trauma, PAMPer, and recent neural network applications, we demonstrated how SA can be utilized to evaluate model accuracy, determine variable importance, and assess the consistency of treatment effects.

Although the implementation of SA in trauma research remains uneven across the field, establishing SA as a standard element of study design and reporting is vital. Doing so not only bolsters the transparency and trustworthiness of analytical results but also enhances the reproducibility and applicability of findings, ultimately supporting more robust and clinically meaningful advancements in trauma care.