Tutorial in Biostatistics: The use of generalized additive models to evaluate alcohol consumption as an exposure variable. (1st April 2020) - White, Laura F; Jiang, Wenqing; Ma, Yicheng; So-Armah, Kaku; Samet, Jeffrey H.; Cheng, Debbie M
Highlights: Analytics to quantify alcohol consumption can oversimplify the original measure. Generalized additive models (GAMs) are a useful tool for this problem. GAMs permit thorough exploration of the relationship between alcohol and outcomes. To make GAMs more accessible to researchers, we provide code and an example. Abstract: Alcohol consumption is a commonly studied risk factor for many poor health outcomes. Various instruments exist to measure alcohol consumption, including the AUDIT-C, Single Alcohol Screening Questionnaire (SASQ) and Timeline Followback. The information gathered by these instruments is often simplified and analyzed as a dichotomous measure, risking the loss of information of potentially prognostic value. We discuss generalized additive models (GAM) as a useful tool to understand the association between alcohol consumption and a health outcome. We demonstrate how this analytic strategy can guide the development of a regression model that retains maximal information about alcohol consumption. We illustrate these approaches using data from the Russia ARCH (Alcohol Research Collaboration on HIV/AIDS) study to analyze the association between alcohol consumption and biomarker of systemic inflammation, interleukin-6 (IL-6). We provide SAS and R code to implement these methods. GAMs have the potential to increase statistical power and allow for better elucidation of more nuanced and non-linear associations between alcohol consumption and important health outcomes.