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Group-based Trajectory Modeling Extended to Account for Nonrandom Participant Attrition

Author

Listed:
  • Amelia M. Haviland

    (RAND Corporation, Pittsburgh, PA, USA)

  • Bobby L. Jones

    (University of Pittsburgh, Pittsburgh, PA, USA)

  • Daniel S. Nagin

    (Carnegie Mellon University, Pittsburgh, PA, USA, dn03@andrew.cmu.edu)

Abstract

This article reports on an extension of group-based trajectory modeling to address nonrandom participant attrition or truncation due to death that varies across trajectory groups. The effects of the model extension are explored in both simulated and real data. The analyses of simulated data establish that estimates of trajectory group size as measured by group membership probabilities can be badly biased by differential attrition rates across groups if the groups are initially not well separated. Differential attrition rates also imply that group sizes will change over time, which in turn has important implications for using the model parameter estimates to make population-level projections. Analyses of longitudinal data on disability levels in a sample of very elderly individuals support both of these conclusions.

Suggested Citation

  • Amelia M. Haviland & Bobby L. Jones & Daniel S. Nagin, 2011. "Group-based Trajectory Modeling Extended to Account for Nonrandom Participant Attrition," Sociological Methods & Research, , vol. 40(2), pages 367-390, May.
    • Handle: RePEc:sae:somere:v:40:y:2011:i:2:p:367-390
    DOI: 10.1177/0049124111400041
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    References listed on IDEAS

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    1. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    2. P. Diggle & M. G. Kenward, 1994. "Informative Drop‐Out in Longitudinal Data Analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 43(1), pages 49-73, March.
    3. Constantine E. Frangakis & Donald B. Rubin, 2002. "Principal Stratification in Causal Inference," Biometrics, The International Biometric Society, vol. 58(1), pages 21-29, March.
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    Cited by:

    1. Hu, Bo, 2020. "Trajectories of informal care intensity among the oldest-old Chinese," Social Science & Medicine, Elsevier, vol. 266(C).
    2. Sridharan, Sanjeev & Jones, Bobby & Caudill, Barry & Nakaima, April, 2016. "Steps towards incorporating heterogeneities into program theory: A case study of a data-driven approach," Evaluation and Program Planning, Elsevier, vol. 58(C), pages 88-97.
    3. Zachary Zimmer & Luoman Bao & Nanette L. Mayol & Feinian Chen & Tita Lorna L. Perez & Paulita L. Duazo, 2017. "Functional limitation trajectories and their determinants among women in the Philippines," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 36(30), pages 863-892.
    4. Zachary Zimmer & Heidi Hanson & Ken Smith, 2016. "Childhood socioeconomic status, adult socioeconomic status, and old-age health trajectories," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 34(10), pages 285-320.
    5. Heidi Hanson & Ken Smith & Zachary Zimmer, 2015. "Reproductive History and Later-Life Comorbidity Trajectories: A Medicare-Linked Cohort Study From the Utah Population Database," Demography, Springer;Population Association of America (PAA), vol. 52(6), pages 2021-2049, December.
    6. Bo Hu & Javiera Cartagena-Farias & Nicola Brimblecombe, 2022. "Functional disability and utilisation of long-term care in the older population in England: a dual trajectory analysis," European Journal of Ageing, Springer, vol. 19(4), pages 1363-1373, December.
    7. Anne Clark, 2018. "The role of residential mobility in reproducing socioeconomic stratification during the transition to adulthood," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 38(7), pages 169-196.
    8. Laura Serra & Kristin Farrants & Kristina Alexanderson & Mónica Ubalde & Tea Lallukka, 2022. "Trajectory analyses in insurance medicine studies: Examples and key methodological aspects and pitfalls," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-12, February.
    9. Zachary Zimmer & Linda Martin & Daniel Nagin & Bobby Jones, 2012. "Modeling Disability Trajectories and Mortality of the Oldest-Old in China," Demography, Springer;Population Association of America (PAA), vol. 49(1), pages 291-314, February.

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