The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
A Guide for Population-based Analysis of the Adolescent Brain Cognitive Development (ABCD) Study Baseline Data
Open accessAbstract
ABCD is a longitudinal, observational study of U.S. children, ages 9-10 at baseline, recruited at random from the household populations in defined catchment areas for each of 21 study sites. The 21 geographic locations that comprise the ABCD research sites are nationally distributed and generally represent the range of demographic and socio-economic diversity of the U.S. birth cohorts that comprise the ABCD study population. The clustering of participants and the potential for selection bias in study site selection and enrollment are features of the ABCD observational study design that are informative for statistical estimation and inference. Both multi-level modeling and robust survey design-based methods can be used to account for clustering of sampled ABCD children in the 21 study sites. Covariate controls in analytical models and propensity weighting methods that calibrate ABCD weighted distributions to nationally-representative controls from the American Community Survey (ACS) can be employed in analysis to account for known informative sample design features or to attenuate potential demographic and socio-economic selection bias in the national sampling and recruitment of eligible children. This guide will present results of an empirical investigation of the ABCD baseline data that compares the statistical efficiency of multi-level modeling and distribution-free design-based approaches—both weighted and unweighted--to analyses of the ABCD baseline data. Specific recommendations will be provided for researchers on robust, efficient approaches to both descriptive and multivariate analyses of the ABCD baseline data.
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“Data analyses were performed using Stata 18.0 (College Station, TX) and applied propensity weights based on the American Community Survey [104].”
From Screen time and mental health: a prospective analysis of the Adolescent Brain Cognitive Development (ABCD) Study (2024) · cites this paper as [104]
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Cites
Austin, P. C., & Stuart, E. A. (2015) · Statistics in Medicine · doi:10.1002/sim.6607 · Cited by 4,495 · cited 2×
“…population estimation and inference, ABCD has used a second weighting method that is closely related to the inverse propensity score weighting methodology (IPSW) that is employed to reduce confounding and estimate average treatment effects (ATE) from observational data (Austin and Stuart, 2015).”
MethodMultiple imputation after 18+ years
Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476908 · Cited by 4,072
Multiple Imputation After 18+ Years
Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.2307/2291635 · Cited by 2,686 · cited 1×
“…of sample inclusion or differential nonresponse, some authors have suggested that the weight variable itself be included as a covariate in the model specification as further protection against sample selectivity that may not be captured in the observed covariate controls (Rubin, 1996).”
BackgroundRecruiting the ABCD sample: Design considerations and procedures
Garavan, H., Bartsch, H., Conway, K., Decastro, A., Goldstein, R. Z., Heeringa, S., Jernigan, T., Potter, A., Thompson, W., & Zahs, D. (2018) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2018.04.004 · Cited by 1,393
Self-citationComplex Surveys: A Guide to Analysis Using R
Lumley, T. (2010) · doi:10.1002/9780470580066 · Cited by 647
Multilevel Modelling of Complex Survey Data
Rabe‐Hesketh, S., & Skrondal, A. (2006) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2006.00426.x · Cited by 642 · cited 2×
“Weighted estimation of multi-level models requires a disaggregation and scaling of the individual propensity or population weights for each level of the model (Rabe-Hesketh and Skrondal, 2006).”
BackgroundGeneralizing Observational Study Results: Applying Propensity Score Methods to Complex Surveys
DuGoff, E. H., Schuler, M., & Stuart, E. A. (2014) · Health Services Research · doi:10.1111/1475-6773.12090 · Cited by 605
Complex Surveys: A Guide to Analysis Using R
Lumley, T. (2010) · Cited by 571
West, B. T., Welch, K. B., & Gałecki, A. T. (2014) · Chapman & Hall/CRC eBooks · doi:10.1201/b17198 · Cited by 485
Weighting for Unequal Selection Probabilities in Multilevel Models
Pfeffermann, D., Skinner, C. J., Holmes, D. J., Goldstein, H., & Rasbash, J. (1998) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/1467-9868.00106 · Cited by 438
Research works printed but not linked: 1
- 10.1038/s411467-017-00908-7 · DOI
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The ABCD study: understanding the development of risk for mental and physical health outcomes
Karcher, N. R., & Barch, D. M. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-020-0736-6 · Cited by 351
“, nesting ABCD Study site and family) [76].”
BackgroundMeaningful associations in the adolescent brain cognitive development study
Dick, A. S., Lopez, D. A., Watts, A. L., Heeringa, S. G., Reuter, C., Bartsch, H., Fan, C. C., Kennedy, D. N., Palmer, C. E., Marshall, A. T., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118262 · Cited by 240
“The construction of the population weights for the ABCD Study is described in detail in Heeringa and Berglund (2020) ( Heeringa and Berglund, 2020 ).”
InfluentialSelf-citationSaragosa‐Harris, N. M., Chaku, N., MacSweeney, N., Williamson, V. G., Scheuplein, M., Feola, B., Cardenas‐Iniguez, C., Demir-Lira, E., McNeilly, E. A., Huffman, L. G., et al. (2022) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2022.101115 · Cited by 172
“The mathematical bases and assumptions of these models have been established (Etz and Vandekerckhove, 2018; Raudenbush and Bryk, 2002; Sterba, 2014) and detailed primers and statistical packages are available elsewhere (see Heeringa & Berglund, 2020 for ABCD-relevant guidelines).”
MethodOwens, M. M., Potter, A., Hyatt, C. S., Albaugh, M. D., Thompson, W. K., Jernigan, T. L., Yuan, D., Hahn, S., Allgaier, N. A., & Garavan, H. (2021) · PLoS ONE · doi:10.1371/journal.pone.0257535 · Cited by 157
Sociodemographic Correlates of Contemporary Screen Time Use among 9- and 10-Year-Old Children
Nagata, J. M., Ganson, K. T., Iyer, P., Chu, J., Baker, F. C., Pettee Gabriel, K., Garber, A. K., Murray, S. B., & Bibbins-Domingo, K. (2022) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2021.08.077 · Cited by 117
Metadata
- Authors
- Heeringa, S. G., & Berglund, P. A.
- Affiliations
- University of Michigan (US)
- Corresponding author
- Heeringa, Steven G.
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2020-02-10
- OpenAlex
- W3005123580
- Cited by
- 247
- References
- 17
- Citations by year
- 2026: 23 · 2025: 50 · 2024: 54 · 2023: 44 · 2022: 38 · 2021: 30 · 2020: 8
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- Adolescent Brain Cognitive Development Study, population-based study, survey design, multilevel modeling, design-based estimation, survey weights, selection bias, socioeconomic diversity, American Community Survey, statistical efficiency, adolescent brain development, cognitive development
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