Missing Data Sensitivity Analyses for Alcohol Research

In alcohol research studies, nonresponse is plausibly related to unobserved drinking scores. These so-called missing not at random (MNAR) processes can reflect situations in which missingness is associated with the underlying level of drinking or changes in drinking over time. Examples include participants experiencing heavy drinking, increasing use or symptoms who may be less likely to report their behavior, and participants whose drinking improves substantially who disengage after achieving their goals or no longer perceiving a need for continued participation.

Calls for missing data sensitivity analyses have become increasingly common. To implement this approach, researchers compare models that make different assumptions about the missing data process and examine the stability of substantive conclusions across these alternatives. Sensitivity analyses are particularly valuable because assumptions about missing data are inherently untestable. However, clear guidance on how to implement them in practice remains limited. To address these recommendations and advance the application of sophisticated missing data methods in alcohol research, this paper provides a practical, step-by-step account of how to conduct such analyses. We focus on selection and shared parameter models because they align with the intuitive idea that a person's unobserved drinking behavior may influence the likelihood of nonresponse, either through individual change trajectories or recent episodic shifts in drinking behavior. Annotated scripts and output are provided in supplemental materials to support implementation. Although missing data assumptions did not appear to have a substantial impact, they easily could under slightly different conditions.

Our sensitivity analysis strategy is applicable to a broad range of alcohol research settings. However, data-specific features can meaningfully alter certain steps. We describe how aspects of our clinical trial illustration may or may not generalize to other alcohol research contexts, and we propose several recommendations for reporting the results of a sensitivity analysis.