M4: Modules on advanced Methodology when Modeling Multinational data

Northwestern University At Chicago, Evanston IL

Investigators

Abstract

Project Summary/Abstract The prevalence of Alzheimer's disease and AD-Related Dementias (AD/ADRD) is increasing globally, highlighting an urgent need to identify factors influencing healthy aging and cognitive change. The Health and Retirement Study (HRS) and its sub-study—the Harmonized Cognitive Assessment Protocol (HCAP)—together with their international sister studies, provide invaluable resources for exploring the epidemiology of AD/ADRD. However, there are methodological hurdles and challenges that create barriers for newer researchers to engage with these rich data in a meaningful way. These challenges include issues related to data quality, varying cultural contexts, and harmonizing analyses across datasets. To equip the next generation of researchers with the necessary methodological skills, we proposed to develop M4: Modules on advanced Methodology when Modeling Multinational data. Required modules will focus on the responsible conduct of secondary data analysis, social and behavioral factors related to AD/ADRD, and reproducibility and open science principles. Trainees will choose one or more optional advanced methodology modules appropriate for multinational analyses: item response theory (IRT), coordinated data analyses (CDA), or latent variable alignment methods. Each module will be taught across multiple sessions, allowing for didactic and hands-on training. We have four specific aims: Aim 1: Develop and refine a module-based training program in large-scale data analysis. This is a necessary initial step to create the modules, leveraging and adapting our existing training and tutorials. Aim 2: Instruct trainees in protective behavioral and social factors aimed at reducing AD/ADRD in cross- national contexts. While many different research questions could be addressed with the HRS and HCAP, a primary intention of the proposed training is to address AD/ADRD. Aim 3: Build open science resources to support the identification of factors affecting the trajectory of healthy aging and AD/ADRD. We believe in open science as a primary way to expand knowledge and ensure reproducibility across projects, especially when using widely-available data like HRS and HCAP. Aim 4: Provide ongoing mentorship in advance methodologies. By engaging trainees across modules, we can develop mentoring relationships whereby we can provide ongoing support in their mastery of these advanced methodologies. M4 will be offered primarily by synchronous virtual learning. Then it will be adapted for asynchronous self-paced learning, allowing participants to engage with it at their convenience. This is of vital importance, insofar as these are multinational data, and international researchers need to be equipped to analyze their own data. Finally, abbreviated versions of the modules will be offered as in-person pre-conference workshops to raise interest and enhance trainee recruitment, ultimately aiming to foster a robust community of researchers dedicated to advancing the understanding and reduction of AD/ADRD.