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A high‐dimensional mediation model for a neuroimaging mediator: Integrating clinical, neuroimaging, and neurocognitive data to mitigate late effects in pediatric cancer

Author

Listed:
  • Jade Xiaoqing Wang
  • Yimei Li
  • Wilburn E. Reddick
  • Heather M. Conklin
  • John O. Glass
  • Arzu Onar‐Thomas
  • Amar Gajjar
  • Cheng Cheng
  • Zhao‐Hua Lu

Abstract

Pediatric cancer treatment, especially for brain tumors, can have profound and complicated late effects. With the survival rates increasing because of improved detection and treatment, a more comprehensive understanding of the impact of current treatments on neurocognitive function and brain structure is critically needed. A frontline medulloblastoma clinical trial (SJMB03) has collected data, including treatment, clinical, neuroimaging, and cognitive variables. Advanced methods for modeling and integrating these data are critically needed to understand the mediation pathway from the treatment through brain structure to neurocognitive outcomes. We propose an integrative Bayesian mediation analysis approach to model jointly a treatment exposure, a high‐dimensional structural neuroimaging mediator, and a neurocognitive outcome and to uncover the mediation pathway. The high‐dimensional imaging‐related coefficients are modeled via a binary Ising–Gaussian Markov random field prior (BI‐GMRF), addressing the sparsity, spatial dependency, and smoothness and increasing the power to detect brain regions with mediation effects. Numerical simulations demonstrate the estimation accuracy, power, and robustness. For the SJMB03 study, the BI‐GMRF method has identified white matter microstructure that is damaged by cancer‐directed treatment and impacts late neurocognitive outcomes. The results provide guidance on improving treatment planning to minimize long‐term cognitive sequela for pediatric brain tumor patients.

Suggested Citation

  • Jade Xiaoqing Wang & Yimei Li & Wilburn E. Reddick & Heather M. Conklin & John O. Glass & Arzu Onar‐Thomas & Amar Gajjar & Cheng Cheng & Zhao‐Hua Lu, 2023. "A high‐dimensional mediation model for a neuroimaging mediator: Integrating clinical, neuroimaging, and neurocognitive data to mitigate late effects in pediatric cancer," Biometrics, The International Biometric Society, vol. 79(3), pages 2430-2443, September.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:3:p:2430-2443
    DOI: 10.1111/biom.13729
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    References listed on IDEAS

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    1. Smith, Michael & Fahrmeir, Ludwig, 2007. "Spatial Bayesian Variable Selection With Application to Functional Magnetic Resonance Imaging," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 417-431, June.
    2. Hua Zhou & Lexin Li & Hongtu Zhu, 2013. "Tensor Regression with Applications in Neuroimaging Data Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 540-552, June.
    3. Yanyi Song & Xiang Zhou & Min Zhang & Wei Zhao & Yongmei Liu & Sharon L. R. Kardia & Ana V. Diez Roux & Belinda L. Needham & Jennifer A. Smith & Bhramar Mukherjee, 2020. "Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies," Biometrics, The International Biometric Society, vol. 76(3), pages 700-710, September.
    4. Zhao, Yi & Lindquist, Martin A. & Caffo, Brian S., 2020. "Sparse principal component based high-dimensional mediation analysis," Computational Statistics & Data Analysis, Elsevier, vol. 142(C).
    5. Li, Fan & Zhang, Nancy R., 2010. "Bayesian Variable Selection in Structured High-Dimensional Covariate Spaces With Applications in Genomics," Journal of the American Statistical Association, American Statistical Association, vol. 105(491), pages 1202-1214.
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