Statistics Core

点击翻译按钮获取中文摘要

ABSTRACT The Statistics Core (SC) serves as the statistical support hub of the Einstein Aging Study (EAS). A primary scientific goal of this Core is to advance our understanding of how individual in-depth characterization of health behaviors, biomarkers, physical and psychosocial health, and environmental factors impact cognitive performance and decline prior to onset of Alzheimer’s disease and related dementias and assessing the role of multiple exposures that influence cognition on a short-term and long-term basis. To achieve this goal, the EAS collects data across multiple timescales ranging from real-time to annual assessments, combining conventional in-person interviews, self-reports and clinical assessments with data streams from wearables (e.g., actigraphy for measurement of sleep and physical activity, air quality monitors, continuous glucose monitors – CGM) and collected via mobile devices (e.g., ecological momentary assessment (EMA) surveys and ambulatory cognitive assessments). The SC will continue in its long history of developing and applying cutting-edge analytic approaches to address the research questions described in the four scientific Projects and to assist in service to the other Cores. It serves two functions that are essential for the success of EAS. First, the SC will provide collaborative and consultative support to Project investigators for data analyses and interpretation of results. Second, the SC will engage in methodological development to meet the challenges posed from complications encountered in analyses and study design. The SC team plays a key role in assisting EAS investigators with all stages of data analysis, such as tailoring analysis plans to specific scientific hypotheses, conducting data analysis, interpreting results, and addressing challenges and potential threats to validity. In particular, the SC will work closely with the Technology and Data Management Core to insure data quality. In short, the SC will continue its long history of contributions to aging research through developing new statistical methodology and through innovative application of existing methodologies.