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Statistics and Its Interface
Volume 17 (2024)
Number 3
Imaging mediation analysis for longitudinal outcomes: a case study of childhood brain tumor survivorship
Pages: 533 – 548
DOI: https://dx.doi.org/10.4310/23-SII815
Authors
Abstract
Aggressive cancer treatments that affect the central nervous system are associated with an increased risk of cognitive deficits. As treatment for pediatric brain tumors has become more effective, there has been a heightened focus on improving cognitive outcomes, which can significantly affect the quality of life for pediatric cancer survivors. This paper is motivated by and applied to a clinical trial for medulloblastoma, the most common malignant brain tumor in children. The trial collects comprehensive data including treatment-related clinical information, neuroimaging, and longitudinal neurocognitive outcomes to enhance our understanding of the responses to treatment and the enduring impacts of radiation therapy on the survivors of medulloblastoma. To this end, we have developed a new mediation model tailored for longitudinal outcomes with high-dimensional imaging mediators. Specifically, we adopt a joint binary Ising-Gaussian Markov random field prior distribution to account for spatial dependency and smoothness of ultra-high-dimensional neuroimaging mediators for enhancing detection power of informative voxels. By exploiting the proposed approach, we identify causal pathways and the corresponding white matter microstructures mediating the negative impact of irradiation on neurodevelopment. The results provide guidance on sparing the brain regions and improving long-term neurodevelopment for pediatric cancer survivors. Simulation studies also confirm the validity of the proposed method.
Keywords
Bayesian mediation analysis, DTI, high-dimensional data, neurodevelopment, neuroimaging, longitudinal outcomes
2010 Mathematics Subject Classification
Primary 62J05, 62M40. Secondary 62F15.
Received 2 December 2022
Accepted 15 August 2023
Published 19 July 2024