A Comparison of Mathematical and Statistical Modeling with Longitudinal Data: An Application to Ecological Momentary Assessment of Behavior Change in Individuals with Alcohol Use Disorder.

Ecological momentary assessment (EMA) has been broadly used to collect real-time longitudinal data in behavioral research. Several analytic methods have been applied to EMA data to understand the changes of motivation, behavior, and emotions on a daily or within-day basis. One challenge when utilizing those methods on intensive datasets in the behavioral field is to understand when and why the methods are appropriate to investigate particular research questions. In this manuscript, we compared two widely used methods (generalized estimating equations and generalized linear mixed models) in behavioral research with three other less frequently used methods (Markov models, generalized linear mixed-effects Markov models, and differential equations) in behavioral research but widely used in other fields. The purpose of this manuscript is to illustrate the application of five distinct analytic methods to one dataset of intensive longitudinal data on drinking behavior, highlighting the utility of each method.

Enhancing Diversity in Biomedical Data Science.

The gap in educational attainment separating underrepresented minorities from Whites and Asians remains wide. Such a gap has significant impact on workforce diversity and inclusion among cross-cutting Biomedical Data Science (BDS) research, which presents great opportunities as well as major challenges for addressing health disparities. This article provides a brief description of the newly established National Institutes of Health Big Data to Knowledge (BD2K) diversity initiatives at four universities: California State University, Monterey Bay; Fisk University; University of Puerto Rico, Río Piedras Campus; and California State University, Fullerton. We emphasize three main barriers to BDS careers (ie, preparation, exposure, and access to resources) experienced among those pioneer programs and recommendations for possible solutions (ie, early and proactive mentoring, enriched research experience, and data science curriculum development). The diversity disparities in BDS demonstrate the need for educators, researchers, and funding agencies to support evidence-based practices that will lead to the diversification of the BDS workforce.