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A Bayesian semiparametric approach for inference on the population partly conditional mean from longitudinal data with dropout
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR).ORCID iD: 0000-0002-1812-3581
Department of Statistics, University of Florida, USA.
Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB). Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI).ORCID iD: 0000-0001-9512-3289
2023 (English)In: Biostatistics, ISSN 1465-4644, E-ISSN 1468-4357, Vol. 24, no 2, p. 372-387Article in journal (Refereed) Published
Abstract [en]

Studies of memory trajectories using longitudinal data often result in highly non-representative samples due to selective study enrollment and attrition. An additional bias comes from practice effects that result in improved or maintained performance due to familiarity with test content or context. These challenges may bias study findings and severely distort the ability to generalize to the target population. In this study we propose an approach for estimating the finite population mean of a longitudinal outcome conditioning on being alive at a specific time point. We develop a flexible Bayesian semi-parametric predictive estimator for population inference when longitudinal auxiliary information is known for the target population. We evaluate sensitivity of the results to untestable assumptions and further compare our approach to other methods used for population inference in a simulation study. The proposed approach is motivated by 15-year longitudinal data from the Betula longitudinal cohort study. We apply our approach to estimate lifespan trajectories in episodic memory, with the aim to generalize findings to a target population.

Place, publisher, year, edition, pages
Oxford University Press, 2023. Vol. 24, no 2, p. 372-387
Keywords [en]
BART, Memory, MNAR, Nonignorable dropout, Population inference, Sensitivity analysis, Truncationby death
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:umu:diva-181637DOI: 10.1093/biostatistics/kxab012ISI: 000755883800001PubMedID: 33880509Scopus ID: 2-s2.0-85139431191OAI: oai:DiVA.org:umu-181637DiVA, id: diva2:1538810
Part of project
Attrition and Generalizability of Cognitive Aging Studies - A Population-Based Perspective, Riksbankens JubileumsfondAvailable from: 2021-03-22 Created: 2021-03-22 Last updated: 2023-06-16Bibliographically approved

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Josefsson, MariaPudas, Sara

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Josefsson, MariaPudas, Sara
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StatisticsCentre for Demographic and Ageing Research (CEDAR)Department of Integrative Medical Biology (IMB)Umeå Centre for Functional Brain Imaging (UFBI)
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Biostatistics
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