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Assessing systematic effects of stroke on motor control by using hierarchical function-on-scalar regression

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  • Jeff Goldsmith
  • Tomoko Kitago

Abstract

type="main" xml:id="rssc12115-abs-0001"> This work is concerned with understanding common population level effects of stroke on motor control while accounting for possible subject level idiosyncratic effects. Upper extremity motor control for each subject is assessed through repeated planar reaching motions from a central point to eight prespecified targets arranged on a circle. We observe the kinematic data for hand position as a bivariate function of time for each reach. Our goal is to estimate the bivariate function-on-scalar regression with subject level random functional effects while accounting for potential correlation in residual curves; covariates of interest are severity of motor impairment and target number. We express fixed effects and random effects by using penalized splines, and we allow for residual correlation by using a Wishart prior distribution. Parameters are jointly estimated in a Bayesian framework, and we implement a computationally efficient approximation algorithm using variational Bayes methods. Simulations indicate that the method proposed yields accurate estimation and inference, and application results suggest that the effect of stroke on motor control has a systematic component observed across subjects.

Suggested Citation

  • Jeff Goldsmith & Tomoko Kitago, 2016. "Assessing systematic effects of stroke on motor control by using hierarchical function-on-scalar regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(2), pages 215-236, February.
    • Handle: RePEc:bla:jorssc:v:65:y:2016:i:2:p:215-236
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    File URL: http://hdl.handle.net/10.1111/rssc.2016.65.issue-2
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    Cited by:

    1. Cui Guo & Jian Kang & Timothy D. Johnson, 2022. "A spatial Bayesian latent factor model for image‐on‐image regression," Biometrics, The International Biometric Society, vol. 78(1), pages 72-84, March.
    2. Mark J. Meyer & Haobo Cheng & Katherine Hobbs Knutson, 2023. "Bayesian Analysis of Multivariate Matched Proportions with Sparse Response," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 490-509, July.
    3. Daniel R. Kowal & Antonio Canale, 2021. "Semiparametric Functional Factor Models with Bayesian Rank Selection," Papers 2108.02151, arXiv.org, revised May 2022.
    4. Emiliano Ceccarelli & Giada Minelli & Viviana Egidi & Giovanna Jona Lasinio, 2023. "Assessment of Excess Mortality in Italy in 2020–2021 as a Function of Selected Macro-Factors," IJERPH, MDPI, vol. 20(4), pages 1-14, February.
    5. Luca Aiello & Matteo Fontana & Alessandra Guglielmi, 2022. "Bayesian Functional Emulation of CO2 Emissions on Future Climate Change Scenarios," Papers 2209.05767, arXiv.org.
    6. Park, So Young & Xiao, Luo & Willbur, Jayson D. & Staicu, Ana-Maria & Jumbe, N. L’ntshotsholé, 2018. "A joint design for functional data with application to scheduling ultrasound scans," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 101-114.
    7. Rahul Ghosal & Arnab Maity, 2023. "Variable selection in nonlinear function‐on‐scalar regression," Biometrics, The International Biometric Society, vol. 79(1), pages 292-303, March.
    8. Luca Aiello & Matteo Fontana & Alessandra Guglielmi, 2023. "Bayesian functional emulation of CO2 emissions on future climate change scenarios," Environmetrics, John Wiley & Sons, Ltd., vol. 34(8), December.
    9. Li, Kan & Luo, Sheng, 2019. "Bayesian functional joint models for multivariate longitudinal and time-to-event data," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 14-29.

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