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  • 1.
    Ecker, Kreske
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Schelin, Lina
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Causal inference with a functional outcome2024In: The Journal of the Royal Statistical Society, Series C: Applied Statistics, ISSN 0035-9254, E-ISSN 1467-9876, Vol. 73, no 1, p. 221-240Article in journal (Refereed)
    Abstract [en]

    This article presents methods to study the causal effect of a binary treatment on a functional outcome with observational data. We define a Functional Average Treatment Effect (FATE) and develop an outcome regression estimator. We show how to obtain valid inference on the FATE using simultaneous confidence bands, which cover the FATE with a given probability over the entire domain. Simulation experiments illustrate how the simultaneous confidence bands take the multiple comparison problem into account. Finally, we use the methods to infer the effect of early adult location on subsequent income development for one Swedish birth cohort.

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  • 2.
    Josefsson, Maria
    et al.
    Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR). Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE).
    Daniels, Michael J.
    Bayesian semi-parametric G-computation for causal inference in a cohort study with MNAR dropout and death2021In: The Journal of the Royal Statistical Society, Series C: Applied Statistics, ISSN 0035-9254, E-ISSN 1467-9876, Vol. 70, no 2, p. 398-414Article in journal (Refereed)
    Abstract [en]

    Causal inference with observational longitudinal data and time-varying exposures is often complicated by time-dependent confounding and attrition. The G-computation formula is one approach for estimating a causal effect in this setting. The parametric modelling approach typically used in practice relies on strong modelling assumptions for valid inference, and moreover depends on an assumption of missing at random, which is not appropriate when the missingness is missing not at random (MNAR) or due to death. In this work we develop a flexible Bayesian semi-parametric G-computation approach for assessing the causal effect on the subpopulation that would survive irrespective of exposure, in a setting with MNAR dropout. The approach is to specify models for the observed data using Bayesian additive regression trees, and then use assumptions with embedded sensitivity parameters to identify and estimate the causal effect. The proposed approach is motivated by a longitudinal cohort study on cognition, health, and aging and we apply our approach to study the effect of becoming a widow on memory. We also compare our approach to several standard methods.

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  • 3.
    Josefsson, Maria
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Daniels, Michael
    University of Texas at Austin.
    Nyberg, Lars
    Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI). Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology. Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB), Physiology.
    Causal inference with longitudinal outcomes and non-ignorable drop-out: Estimating the effect of living alone on cognitive decline2016In: The Journal of the Royal Statistical Society, Series C: Applied Statistics, ISSN 0035-9254, E-ISSN 1467-9876, Vol. 65, no 1, p. 131-144Article in journal (Refereed)
    Abstract [en]

    We develop a model to estimate the causal effect of living arrangement (living alone versus living with someone) on cognitive decline based on a 15-year prospective cohort study, where episodic memory function is measured every 5 years. One key feature of the model is the combination of propensity score matching to balance confounding variables between the two living arrangement groups—to reduce bias due to unbalanced covariates at baseline, with a pattern–mixture model for longitudinal data—to deal with non-ignorable dropout. A fully Bayesian approach allows us to convey the uncertainty in the estimation of the propensity score and subsequent matching in the inference of the causal effect of interest. The analysis conducted adds to previous studies in the literature concerning the protective effect of living with someone, by proposing a modelling approach treating living arrangement as an exposure.

  • 4.
    Wegmann, Bertil
    et al.
    Division of Statistics and Machine Learning, Department of Computer and Information Science, Linköping University , Linköping , Sweden;Center for Medical Image Science and Visualization (CMIV), Linköping University , Linköping , Sweden.
    Lundquist, Anders
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics. Department of Statistics, Umeå School of Business, Economics and Statistics (USBE), Umeå University , Umeå , Sweden.
    Eklund, Anders
    Center for Medical Image Science and Visualization (CMIV), Linköping University , Linköping , Sweden;Division of Medical Informatics, Department of Biomedical Engineering, Linköping University , Linköping , Sweden.
    Villani, Mattias
    Department of Statistics, Stockholm University , Stockholm , Sweden.
    Bayesian modelling of effective and functional brain connectivity using hierarchical vector autoregressions2024In: The Journal of the Royal Statistical Society, Series C: Applied Statistics, ISSN 0035-9254, E-ISSN 1467-9876Article in journal (Refereed)
    Abstract [en]

    Analysis of brain connectivity is important for understanding how information is processed by the brain. We propose a novel Bayesian vector autoregression hierarchical model for analysing brain connectivity within resting-state functional magnetic resonance imaging, and apply it to simulated data and a real data set with subjects in different groups. Our approach models functional and effective connectivity simultaneously and allows for both group- and single-subject inference. We combine analytical marginalization with Hamiltonian Monte Carlo to obtain highly efficient posterior sampling. We show that our model gives similar inference for effective connectivity compared to models with a common covariance matrix to all subjects, but more accurate inference for functional connectivity between regions compared to models with more restrictive covariance structures. A Stan implementation of our model is available on GitHub.

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