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A Guide for Population-based Analysis of the Adolescent Brain Cognitive Development (ABCD) Study Baseline Data

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Heeringa, Steven G., Berglund, Patricia A.·2020·bioRxiv (Cold Spring Harbor Laboratory)

DOI 10.1101/2020.02.10.942011

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Abstract

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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Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies
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).”
Method
Multiple imputation after 18+ years
Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476908 · Cited by 4,072
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“…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).”
Background
Recruiting the ABCD sample: Design considerations and procedures
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Self-citation
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Lumley, T. (2010) · doi:10.1002/9780470580066 · Cited by 647
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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).”
Background
Generalizing Observational Study Results: Applying Propensity Score Methods to Complex Surveys
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Lumley, T. (2010) · Cited by 571
Linear Mixed Models
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Weighting for Unequal Selection Probabilities in Multilevel Models
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Background
How Does Parental Monitoring Reduce Adolescent Substance Use? Preliminary Tests of Two Potential Mechanisms
Pelham, W. E., Tapert, S. F., Gonzalez, M. R., Ahiarakwe, U., Patel, H., Davis, I. S., Meruelo, A. D., Rinsveld, A. M. V., Marshall, A. T., Dick, A. S., et al. (2024) · Journal of Studies on Alcohol and Drugs · doi:10.15288/jsad.23-00297 · Cited by 33
“The ABCD Study has followed a cohort of adolescents, recruited primarily from schools, at 21 study sites across the United States (Garavan et al., 2018).”
MethodInfluential
Parental Arrest and Child Behavior: Differential Role of Executive Functioning among Racial Subgroups
Johnson, E. I., Planalp, E. M., & Poehlmann, J. (2022) · Journal of Child and Family Studies · doi:10.1007/s10826-022-02251-y · Cited by 28
“…monitored as the sample accumulated, and, when necessary, recruitment strategies were adjusted to ensure adherence to the desired sociodemographic diversity and representativeness of the birth cohorts in the U.S. that comprise the study population (Garavan et al., 2018; Heeringa & Berglund, 2020).”
Method
Examining the Bidirectional Associations between Sleep Duration, Screen Time, and Internalizing Symptoms in the ABCD Study
Zink, J., O'Connor, S., Blachman-Demner, D., Wolff-Hughes, D. L., & Berrigan, D. (2023) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2023.09.001 · Cited by 24
Maternal Tobacco Use During Pregnancy and Child Neurocognitive Development
Puga, T. B., Dai, H. D., Wang, Y., & Theye, E. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.55952 · Cited by 31
Association between adverse childhood experiences and diet, exercise, and sleep in pre-adolescents.
Angeles, W. W. L.-D. L. (2022) · Academic pediatrics · doi:10.1016/j.acap.2022.06.007 · Cited by 25
Hippocampal Subfield Volumes Mediate the Association between Socioeconomic Disadvantage and Episodic Memory in the Abcd Sample
Botdorf, M., Dunstan, J., Sorcher, L. K., Dougherty, L. R., & Riggins, T. (2022) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2022.101138 · Cited by 26
Associations between brain imaging and polygenic scores of mental health and educational attainment in children aged 9–11
Fernández‐Cabello, S., Alnæs, D., van der Meer, D., Dahl, A., Holm, M. C., Kjelkenes, R., Maximov, I. I., Norbom, L. B., Pedersen, M. L., Voldsbekk, I., et al. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119611 · Cited by 26
“Despite the efforts to minimize sample bias, the ABCD cohort differs from general population estimates (e.g., higher family income, Heeringa and Berglund, 2020) and it is uncertain how this impacts the distributions of the PRS of mental disorders.”
Method
Longitudinal trajectories of childhood and adolescent attention deficit hyperactivity disorder diagnoses in three cohorts
Norman, L. J., Price, J., Ahn, K., Sudre, G. P., Sharp, W. S., & Shaw, P. (2023) · EClinicalMedicine · doi:10.1016/j.eclinm.2023.102021 · Cited by 29
Psychiatric comorbidity of eating disorders in children between the ages of 9 and 10
Convertino, A. D., & Blashill, A. J. (2021) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13484 · Cited by 29
Screen Time and Obsessive-Compulsive Disorder Among Children 9–10 Years Old: A Prospective Cohort Study
Nagata, J. M., Chu, J., Zamora, G., Ganson, K., Testa, A., Jackson, D. B., Costello, C. R., Murray, S. B., & Baker, F. (2022) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2022.10.023 · Cited by 21
“Propensity weights were applied to yield estimates based on the American Community Survey from the US Census.”
Method
Air pollution from biomass burning disrupts early adolescent cortical microarchitecture development
Bottenhorn, K. L., Sukumaran, K., Cardenas‐Iniguez, C., Habre, R., Schwartz, J. D., Chen, J., & Herting, M. M. (2024) · Environment International · doi:10.1016/j.envint.2024.108769 · Cited by 28
Schooling substantially improves intelligence, but neither lessens nor widens the impacts of socioeconomics and genetics
Judd, N., Sauce, B., & Klingberg, T. (2022) · npj Science of Learning · doi:10.1038/s41539-022-00148-5 · Cited by 27
“Our endogenous variables were working memory (WM), ﬂ uid ( f IQ), and crystallized intelligence ( c IQ) from the NIH toolbox cognition battery 75 .”
Social epidemiology of Fitbit daily steps in early adolescence
Nagata, J. M., Alsamman, S., Smith, N., Yu, J., Ganson, K., Dooley, E. E., Wing, D., Baker, F., & Gabriel, K. P. (2023) · Pediatric Research · doi:10.1038/s41390-023-02700-4 · Cited by 22
A Mega-analytic Study of White Matter Microstructural Differences Across 5 Cohorts of Youths With Attention-Deficit/Hyperactivity Disorder
Sudre, G. P., Norman, L. J., Bouyssi-Kobar, M., Price, J., Shastri, G. G., & Shaw, P. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.09.021 · Cited by 21
COVID-19 Policies, Pandemic Disruptions, and Changes in Child Mental Health and Sleep in the United States
Xiao, Y., Brown, T. T., Snowden, L. R., Chow, J. C., & Mann, J. J. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.2716 · Cited by 24
Direct and Indirect Associations of Widespread Individual Differences in Brain White Matter Microstructure With Executive Functioning and General and Specific Dimensions of Psychopathology in Children
Cardenas‐Iniguez, C., Moore, T. M., Kaczkurkin, A. N., Meyer, F. A. C., Satterthwaite, T. D., Fair, D. A., White, T. J. H., Blok, E., Applegate, B. E., Thompson, L. M., et al. (2020) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2020.11.007 · Cited by 25
Mental Well-Being Among Adversity-Exposed Adolescents During the COVID-19 Pandemic
Raney, J. H., Weinstein, S., Ganson, K. T., Testa, A. M., Jackson, D. B., Pantell, M. S., Glidden, D. V., Brindis, C. D., & Nagata, J. M. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.2076 · Cited by 24
Psychiatric neuroimaging designs for individualised, cohort, and population studies
Gell, M., Noble, S., Laumann, T. O., Nelson, S. M., & Tervo‐Clemmens, B. (2024) · Neuropsychopharmacology · doi:10.1038/s41386-024-01918-y · Cited by 24
“Ideally, population studies accurately represent a complex target population (e.g., nationally representative of adolescents in the United States) through inclusive and well-planned sampling (cf., [34, 61]).”
Background
A multidimensional approach to understanding the emergence of sex differences in internalizing symptoms in adolescence
Serio, B., Köhler, R., Ye, F., Lichenstein, S. D., & Yip, S. W. (2022) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2022.101182 · Cited by 21
Treatment of US Children With Attention-Deficit/Hyperactivity Disorder in the Adolescent Brain Cognitive Development Study
Olfson, M., Wall, M. M., Wang, S., Laje, G., & Blanco, C. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.10999 · Cited by 20
Sports participation & childhood neurocognitive development
Tan, F.-M., Yu, J., & Goodwill, A. M. (2024) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2024.101492 · Cited by 26
“This allowed the model to concurrently account for the oversampling of twins in the ABCD study, along with the clustering of data within the 21 testing sites (Heeringa and Berglund, 2020).”
Sleep disorders predict the 1-year onset, persistence, but not remission of psychotic experiences in preadolescence: a longitudinal analysis of the ABCD cohort data
Reeve, S., & Bell, V. (2022) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-022-01966-z · Cited by 20
“In addition, we altered our analysis plan across all regression analyses based on ABCD analysis guidance by (i) including clustering by site and family to create multilevel models (otherwise known as mixed-effect models) that included site and family as random intercept factors [41], and (ii) repeating the analyses wi…”
Method
Correction to: Multimethod investigation of the neurobiological basis of ADHD symptomatology in children aged 9-10: baseline data from the ABCD study
Owens, M. M., Allgaier, N. A., Hahn, S., Yuan, D., Albaugh, M. D., Adise, S., Chaarani, B., Ortigara, J., Juliano, A., Potter, A., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01320-y · Cited by 19
Parent-Adolescent Discrepancies in Adolescent Recreational Screen Time Reporting During the Coronavirus Disease 2019 Pandemic
Nagata, J. M., Cortez, C. A., Iyer, P., Ganson, K., Chu, J., & Conroy, A. A. (2021) · Academic pediatrics · doi:10.1016/j.acap.2021.12.008 · Cited by 16
“All data analyses were conducted in 2021 using Stata 15.1 (StataCorp, College Station, TX) utilized a statistical significance threshold of P = .05, and applied propensity weights to match key sociodemographic variables in ABCD Study to the American Community Survey by the US Census.30 Propensity weights from the Adol…”
Influential
Problematic social media use and alcohol expectancies in early adolescents
Nagata, J. M., Smith, N., Zamora, G., Sajjad, O. M., Ganson, K., Testa, A., & Jackson, D. B. (2023) · BMC Public Health · doi:10.1186/s12889-023-15298-3 · Cited by 23
Psychiatric Diagnoses and Treatment in Nine- to Ten-Year-Old Participants in the ABCD Study
Duffy, K. A., Gandhi, R., Falke, C. A., Wiglesworth, A., Mueller, B. A., Fiecas, M. J. A., Klimes‐Dougan, B., Luciana, M. M., & Cullen, K. R. (2023) · JAACAP Open · doi:10.1016/j.jaacop.2023.03.001 · Cited by 18
Cyberbullying and eating disorder symptoms in US early adolescents
Cheng, C. M., Chu, J., Ganson, K. T., Trompeter, N., Testa, A. M., Jackson, D. B., He, J., Glidden, D. V., Baker, F. C., & Nagata, J. M. (2023) · International Journal of Eating Disorders · doi:10.1002/eat.24034 · Cited by 22
“Finally, propensity weights were applied based on the US Census American Community Survey to yield representative estimates (Heeringa & Berglund, 2020).”
Prevalence of Disordered Eating and Associations With Sex, Pubertal Maturation, and Weight in Children in the US
Murray, S. B., Blashill, A. J., & Calzo, J. P. (2022) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2022.2490 · Cited by 17
Adverse childhood experiences and sipping alcohol in U.S. children: Findings from the Adolescent Brain Cognitive Development Study
Nagata, J. M., Smith, N., Sajjad, O. M., Zamora, G., Raney, J. H., Ganson, K. T., Testa, A. M., Vittinghoff, E., & Jackson, D. B. (2023) · Preventive Medicine Reports · doi:10.1016/j.pmedr.2023.102153 · Cited by 18
“ABCD propensity weights were applied based on the American Community Survey from the U.S. Census.”
Method
Recalibrating expectations about effect size: A multi-method survey of effect sizes in the ABCD studyPreprint
Owens, 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. (2020) · doi:10.31234/osf.io/tn9u4 · Cited by 17
Physical and mental health in adolescence: novel insights from a transdiagnostic examination of FitBit data in the ABCD study
Damme, K. S. F., Vargas, T., Walther, S., Shankman, S. A., & Mittal, V. A. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02794-2 · Cited by 21
Identification and validation of distinct latent neurodevelopmental profiles in the Adolescent Brain and Cognitive Development study
Lichenstein, S., Roos, C. R., Kohler, R., Kiluk, B. D., Carroll, K., Worhunsky, P., Witkiewitz, K., & Yip, S. W. (2021) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2021.02.013 · Cited by 18
Pandemic-Related Changes in the Prevalence of Early Adolescent Alcohol and Drug Use, 2020-2021: Data from a Multisite Cohort StudyPreprint
Pelham, W. E., Tapert, S. F., Zúñiga, M. L., Thompson, W. K., Wade, N. E., Gonzalez, M. R., Patel, H., Baker, F. C., Dowling, G. J., Rinsveld, A. M. V., et al. (2022) · PsyArXiv (OSF Preprints) · doi:10.1016/j.jadohealth.2023.02.040 · Cited by 15
Cyberbullying, mental health, and substance use experimentation among early adolescents: a prospective cohort study
Nagata, J. M., Shim, J. E., Balasubramanian, P., Leong, A. W., Smith-Russack, Z., Shao, I. Y., Al-Shoaibi, A. A. A., Helmer, C. K., Ganson, K. T., Testa, A. M., et al. (2025) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2025.101002 · Cited by 28
Severity and Transition of Suicidal Behaviors in Childhood: Sex, Racial, and Ethnic Differences in the Adolescent Brain Cognitive Development (ABCD) Study
Ortin‐Peralta, A., Sheftall, A. H., Osborn, A., & Miranda, R. (2023) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2023.05.026 · Cited by 16
The Association between Family Environment and Subsequent Risk of Cyberbullying Victimization in Adolescents
Shao, I. Y., Al-Shoaibi, A. A. A., Testa, A. M., Ganson, K. T., Baker, F. C., & Nagata, J. M. (2023) · Academic Pediatrics · doi:10.1016/j.acap.2023.11.019 · Cited by 21
Facets of impulsivity and reward in relation to binge-eating disorder course of illness among children: Findings from the Adolescent Brain Cognitive Development (ABCD) study
Smith, K. E., Wang, W.-L., & Mason, T. B. (2023) · Journal of Child Psychology and Psychiatry and Allied Disciplines · doi:10.1111/jcpp.13789 · Cited by 12
Can we diagnose mental disorders in children? A large‐scale assessment of machine learning on structural neuroimaging of 6916 children in the adolescent brain cognitive development study
Gaus, R., Pölsterl, S., Greimel, E., Schulte‐Körne, G., & Wachinger, C. (2023) · JCPP Advances · doi:10.1002/jcv2.12184 · Cited by 15
“Both family income and the proportion of participants with married parents, who are both employed, are higher in the ABCD cohort (Heeringa & Berglund, 2020).”
Background
Preliminary analysis of low-level alcohol use and suicidality with children in the adolescent brain and cognitive development (ABCD) baseline cohort
Aguinaldo, L. D., Goldstone, A., Hasler, B. P., Brent, D. A., Coronado, C., & Jacobus, J. (2021) · Psychiatry Research · doi:10.1016/j.psychres.2021.113825 · Cited by 14
Missing data approaches for longitudinal neuroimaging research: Examples from the Adolescent Brain and Cognitive Development (ABCD) Study®
Li, L., Bayat, M., Hayes, T. B., Thompson, W. K., Neale, M. C., Gard, A. M., & Dick, A. S. (2025) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2025.101563 · Cited by 9
“To mitigate the influence of extreme weights (Heeringa and Berglund, 2020; Stürmer et al., 2021), the final weight was trimmed by 1% on both sides (Chesnaye et al., 2021).”
Sexual orientation and mental health in a US cohort of children: a longitudinal mediation study
Feinstein, B. A., van der Star, A., Dorrell, K. D., & Blashill, A. J. (2023) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13873 · Cited by 13
Cognitive deficits and enhancements in youth from adverse conditions: An integrative assessment using Drift Diffusion Modeling in the ABCD study
Vermeent, S., Young, E. S., DeJoseph, M. L., Schubert, A., & Frankenhuis, W. E. (2024) · Developmental Science · doi:10.1111/desc.13478 · Cited by 17
Associations of Contemporary Screen Time Modalities With Early Adolescent Nutrition
Nagata, J. M., Weinstein, S., Bashir, A., Lee, S., Al-shoaibi, A. A. A., Shao, I. Y., Ganson, K. T., Testa, A., He, J., & Garber, A. K. (2024) · Academic Pediatrics · doi:10.1016/j.acap.2024.01.023 · Cited by 12
Associations between potentially traumatic events and psychopathology among preadolescents in the Adolescent Brain and Cognitive Development Study®
Thompson, E., Lever, N., Connors, K., Cloak, C., Reeves, G., & Chang, L. (2022) · Journal of Traumatic Stress · doi:10.1002/jts.22793 · Cited by 11
Associations of polygenic risk for attention-deficit/hyperactivity disorder with general and specific dimensions of childhood psychological problems and facets of impulsivity
Lahey, B. B., Lin, T., Pierce, B. L., Hedeker, D., Berman, M. G., Cardenas‐Iniguez, C., Moore, T. M., Applegate, B. E., Tiemeier, H. W., & Kaczkurkin, A. N. (2022) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2022.06.019 · Cited by 16
Sex, gender diversity, and brain structure in early adolescence
Torgerson, C. M., Ahmadi, H., Choupan, J., Fan, C. C., Blosnich, J. R., & Herting, M. M. (2024) · Human Brain Mapping · doi:10.1002/hbm.26671 · Cited by 12
“The heterogeneity of sites captured geographic, demographic, and socioeconomic diversity of participants (Garavan et al., 2018; Heeringa & Berglund, 2020).”
Background
Sexual identity is associated with adverse childhood experiences (ACEs) in US early adolescents
Raney, J. H., Weinstein, S., Testa, A. M., Ganson, K., Memon, Z., Glidden, D. V., Baker, F. C., Brindis, C. D., & Nagata, J. M. (2024) · Academic pediatrics · doi:10.1016/j.acap.2024.07.022 · Cited by 12
Cyberbullying Victimization Among Transgender and Gender-Questioning Early Adolescents
Nagata, J. M., Balasubramanian, P., Diep, T., Ganson, K. T., Testa, A. M., He, J., & Baker, F. C. (2024) · Academic Pediatrics · doi:10.1016/j.acap.2024.102624 · Cited by 12

Authors: Heeringa, S. G., & Berglund, P. A.
Affiliations: University of Michigan (US)
Corresponding author: Heeringa, Steven G.
ORCID: Heeringa, Steven G.
Berglund, Patricia A.
Venue: bioRxiv (Cold Spring Harbor Laboratory)
Publisher: openRxiv
Published: 2020-02-10
DOI: 10.1101/2020.02.10.942011
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: cc-by-nd
creativecommons.org/licenses/by-nd/4.0/ (vor)
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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A Guide for Population-based Analysis of the Adolescent Brain Cognitive Development (ABCD) Study Baseline Data

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