Longitudinal Integrative Data Analysis (IDA) for Life Course and Aging Research

Project Title: “Life Course Process of Alzheimer’s Disease: Sex Differences and Biosocial Mechanisms” (Project Website)

National Institute on Aging R01AG057800 (Principal Investigator: Yang C. Yang)

Introduction

The project addresses major gaps in existing research on cognitive aging by 1) characterizing age-related cognitive change over the full life span, 2) assessing social disparities in cognitive aging by sex/gender, race/ethnicity, and socioeconomic status as well as other social stressors, and 3) exploring biological mechanisms by which social factors are linked to cognitive health and dementia risk. The foundation for this project is the construction of longitudinal cohort data that spans the adult life course. We address challenges in previous research using single panel data with an extensive longitudinal life course research design and a novel application of integrative data analysis (IDA) to determine for the first time the trajectory of cognitive aging throughout life in Americans aged 12 and older, and main demographic and socioeconomic differentials therein.

We integrated four U.S. population-based panel studies of over 50,000 individuals from most 20th century birth cohorts followed for up to 25 years, including the National Longitudinal Study of Adolescent to Adult Health (1994–2018), the National Survey of Midlife Development in the U.S. (2004–2017), Health and Retirement Study (1996–2018), and Americans’ Changing Lives study (1986–2011). The methodology is applicable to a wide variety of outcomes for life course and aging research.

Integrated Dataset Creation and Public Code Documentation

The subproject website provides open-source Stata code for users to create the dataset that combines data from these four individual population-based longitudinal surveys. This resource enables new analytic and modeling approaches for longitudinal IDA (Curran & Hussong, 2009) across a variety of outcomes, and is meant to support data sharing and help build a more cumulative science. Key publications utilizing this dataset include Yang et al. (2021) and Yang et al. (2023).

 

References:

  • Curran, P. J., & Hussong, A. M. (2009). Integrative data analysis: the simultaneous analysis of multiple data sets. Psychological methods, 14(2), 81.
  • Yang, Y. C., Walsh, C. E., Johnson, M. P., Belsky, D. W., Reason, M., Curran, P., Aiello, A.E., Chanti-Ketterl, M. & Harris, K. M. (2021). Life-course trajectories of body mass index from adolescence to old age: Racial and educational disparities. Proceedings of the National Academy of Sciences, 118(17), e2020167118.
  • Yang, Y.C., Walsh, C. E., Shartle, K., Stebbins, R. C., Aiello, A. E., Belsky, D. W., Harris, K. M., Chanti-Ketterl, M., & Plassman, B. L. (2023). An early and unequal decline: life course trajectories of cognitive aging in the United States.” Journal of Aging and Health. https://doi.org/10.1177/08982643231184593. PMCID: 37335551.

 

(Last updated in June, 2023)