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Semiparametric mixed model for longitudinal image data. (English) Zbl 1175.62039

Summary: This paper is concerned with analyzing image data with repeated measurements. Modeling the complex spatial variability in medical images can enhance the inference for comparing treatments and examining the time evolution. We propose a semiparametric mixed regression model that combines the robustness of a low-rank spline method with the efficiency of likelihood-based parameter estimation. In the convenient framework of a linear mixed model, we decompose pattern variation by using the Karhunen-Loève expansion of a spatial random process. Additional spatial covariates, the time variable and other experimental factors are easily accommodated in the linear predictor, which enables comprehensive analyses of the underlying spatial patterns. The backfitting algorithm and the smoothing parameter selections are investigated by simulations for our semiparametric model. We illustrate our method through application to data from a clinical study of neuromuscular electrical stimulation effects on the seating interface pressures for patients with spinal cord injury.

MSC:

62G08 Nonparametric regression and quantile regression
62M40 Random fields; image analysis
92C55 Biomedical imaging and signal processing
65C60 Computational problems in statistics (MSC2010)

Software:

SemiPar; gss
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References:

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