Practical Considerations on Selecting Statistical Methods to Mitigate Unmeasured Confounding

Music City Center 

Non-randomized studies are increasingly used to support decisions on the comparative effectiveness of interventions. However, identifying valid causal effects relies on certain assumptions. The assumption of no unmeasured confounding cannot be statistically tested, but a range of methods for sensitivity analysis have been developed. This research aimed to guide the selection of suitable methods to assess the robustness of study results to unmeasured confounding. Current recommendations on sensitivity analysis from regulatory and health technology assessment (HTA) guidelines were summarized. Commonly used methods were evaluated, along with recent approaches designed to overcome the limitations of established techniques. Methods were further categorized based on confounder measurability in external data sources and ease of implementation. In this presentation, key findings will be summarized; practical considerations, such as the assessment goals, nature of the unmeasured confounders, and availability of information on confounders, will be discussed to facilitate the selection of methods and promote transparency in reporting; and methods will be illustrated through examples.

Unmeasured confounding

Sensitivity analysis

Non-randomized studies 

Biopharmaceutical Section