ANOPOW FOR REPLICATED NONSTATIONARY TIME SERIES IN EXPERIMENTS.

We propose a novel analysis of power (ANOPOW) model for analyzing replicated nonstationary time series commonly encountered in experimental studies. Based on a locally stationary ANOPOW Cramér spectral representation, the proposed model can be used to compare the second-order time-varying frequency patterns among different groups of time series and to estimate group effects as functions of both time and frequency. Formulated in a Bayesian framework, independent two-dimensional second-order random walk (RW2D) priors are assumed on each of the time-varying functional effects for flexible and adaptive smoothing. A piecewise stationary approximation of the nonstationary time series is used to obtain localized estimates of time-varying spectra. Posterior distributions of the time-varying functional group effects are then obtained via integrated nested Laplace approximations (INLA) at a low computational cost. The large-sample distribution of local periodograms can be appropriately utilized to improve estimation accuracy since INLA allows modeling of data with various types of distributions. The usefulness of the proposed model is illustrated through two real data applications: analyses of seismic signals and pupil diameter time series in children with attention deficit hyperactivity disorder. Simulation studies, Supplementary Materials (Li, Yue and Bruce, 2023a), and R code (Li, Yue and Bruce, 2023b) for this article are also available.

Adaptive Bayesian sum of trees model for covariate-dependent spectral analysis.

This paper introduces a flexible and adaptive nonparametric method for estimating the association between multiple covariates and power spectra of multiple time series. The proposed approach uses a Bayesian sum of trees model to capture complex dependencies and interactions between covariates and the power spectrum, which are often observed in studies of biomedical time series. Local power spectra corresponding to terminal nodes within trees are estimated nonparametrically using Bayesian penalized linear splines. The trees are considered to be random and fit using a Bayesian backfitting Markov chain Monte Carlo (MCMC) algorithm that sequentially considers tree modifications via reversible-jump MCMC techniques. For high-dimensional covariates, a sparsity-inducing Dirichlet hyperprior on tree splitting proportions is considered, which provides sparse estimation of covariate effects and efficient variable selection. By averaging over the posterior distribution of trees, the proposed method can recover both smooth and abrupt changes in the power spectrum across multiple covariates. Empirical performance is evaluated via simulations to demonstrate the proposed method's ability to accurately recover complex relationships and interactions. The proposed methodology is used to study gait maturation in young children by evaluating age-related changes in power spectra of stride interval time series in the presence of other covariates.

The Application Value of MRI T2∗WI Radiomics Nomogram in Discriminating Hepatocellular Carcinoma from Intrahepatic Cholangiocarcinoma.

Objective: To establish and validate an MRI T2∗WI-based radiomics nomogram model and to discriminate hepatocellular carcinoma (HCC) from intrahepatic cholangiocarcinoma (ICCA). Methods: 174 patients were retrospectively collected, who were diagnosed with primary hepatic carcinoma by surgery or puncture pathology and received preoperative MRI scans including T2∗WI scans. There were 113 cases of HCC and 61 cases of mass-type ICCA. T2∗WI was used for feature extraction, the extent of the lesions was manually outlined at the largest lesions layer of the T2∗WI, and the feature dimension reduction was performed by the mRMR and LASSO to obtain the optimal feature set. The radiomics features and clinical risk factors were combined to establish the radiomics nomogram model. In both training and validation groups, calibration curves and ROC curves were applied to validate the efficacy of the established model. Finally, calibration curves were applied to assess the degree of fitting and DCA to assess the clinical utility of the established model. Results: The radiomics model had the AUC of 0.90 (95% CI, 0.85-0.96) and 0.91 (95% CI, 0.83-0.99) in the training and validation groups, respectively; the AUC of the radiomics nomogram was 0.97 (95% CI, 0.94-0.99) in the training group and 0.95 (95% CI, 0.95-0.99) in the validation group. DCA suggested the clinical application value of the nomogram model. Conclusion: Radiomics nomogram model based on MRI T2∗WI scan without enhancement can be used to discriminate HCC from ICCA.

Interpretable Classification of Categorical Time Series Using the Spectral Envelope and Optimal Scalings.

This article introduces a novel approach to the classification of categorical time series under the supervised learning paradigm. To construct meaningful features for categorical time series classification, we consider two relevant quantities: the spectral envelope and its corresponding set of optimal scalings. These quantities characterize oscillatory patterns in a categorical time series as the largest possible power at each frequency, or spectral envelope, obtained by assigning numerical values, or scalings, to categories that optimally emphasize oscillations at each frequency. Our procedure combines these two quantities to produce an interpretable and parsimonious feature-based classifier that can be used to accurately determine group membership for categorical time series. Classification consistency of the proposed method is investigated, and simulation studies are used to demonstrate accuracy in classifying categorical time series with various underlying group structures. Finally, we use the proposed method to explore key differences in oscillatory patterns of sleep stage time series for patients with different sleep disorders and accurately classify patients accordingly. The code for implementing the proposed method is available at https://github.com/zedali16/envsca.

Conditional adaptive Bayesian spectral analysis of replicated multivariate time series.

This article introduces a flexible nonparametric approach for analyzing the association between covariates and power spectra of multivariate time series observed across multiple subjects, which we refer to as multivariate conditional adaptive Bayesian power spectrum analysis (MultiCABS). The proposed procedure adaptively collects time series with similar covariate values into an unknown number of groups and nonparametrically estimates group-specific power spectra through penalized splines. A fully Bayesian framework is developed in which the number of groups and the covariate partition defining the groups are random and fit using Markov chain Monte Carlo techniques. MultiCABS offers accurate estimation and inference on power spectra of multivariate time series with both smooth and abrupt dynamics across covariate by averaging over the distribution of covariate partitions. Performance of the proposed method compared with existing methods is evaluated in simulation studies. The proposed methodology is used to analyze the association between fear of falling and power spectra of center-of-pressure trajectories of postural control while standing in people with Parkinson's disease.

Adaptive Bayesian Spectral Analysis of High-dimensional Nonstationary Time Series.

This article introduces a nonparametric approach to spectral analysis of a high-dimensional multivariate nonstationary time series. The procedure is based on a novel frequency-domain factor model that provides a flexible yet parsimonious representation of spectral matrices from a large number of simultaneously observed time series. Real and imaginary parts of the factor loading matrices are modeled independently using a prior that is formulated from the tensor product of penalized splines and multiplicative gamma process shrinkage priors, allowing for infinitely many factors with loadings increasingly shrunk towards zero as the column index increases. Formulated in a fully Bayesian framework, the time series is adaptively partitioned into approximately stationary segments, where both the number and locations of partition points are assumed unknown. Stochastic approximation Monte Carlo (SAMC) techniques are used to accommodate the unknown number of segments, and a conditional Whittle likelihood-based Gibbs sampler is developed for efficient sampling within segments. By averaging over the distribution of partitions, the proposed method can approximate both abrupt and slowly varying changes in spectral matrices. Performance of the proposed model is evaluated by extensive simulations and demonstrated through the analysis of high-density electroencephalography.

Adaptive Bayesian Time-Frequency Analysis of Multivariate Time Series.

This article introduces a nonparametric approach to multivariate time-varying power spectrum analysis. The procedure adaptively partitions a time series into an unknown number of approximately stationary segments, where some spectral components may remain unchanged across segments, allowing components to evolve differently over time. Local spectra within segments are fit through Whittle likelihood based penalized spline models of modified Cholesky components, which provide flexible nonparametric estimates that preserve positive definite structures of spectral matrices. The approach is formulated in a Bayesian framework, in which the number and location of partitions are random, and relies on reversible jump Markov chain and Hamiltonian Monte Carlo methods that can adapt to the unknown number of segments and parameters. By averaging over the distribution of partitions, the approach can approximate both abrupt and slow-varying changes in spectral matrices. Empirical performance is evaluated in simulation studies and illustrated through analyses of electroencephalography during sleep and of the El Niño-Southern Oscillation.

Screening for vitamin D insufficiency in pediatric cancer survivors.

BACKGROUND: Corticosteroids increase risk for decreased bone mineral density, which can be worsened by vitamin D insufficiency (VDI) or deficiency (VDD). PROCEDURE: In the Vanderbilt cancer survivorship clinic, we obtained screening total 25-hydroxy vitamin D levels (VDL) in 171 cancer survivors <23 years old who were treated with prolonged corticosteroids for their cancer, and compared this group to a control group of 97 healthy pediatric patients. RESULTS: VDD was diagnosed in 15.8% and VDI in 34.5% of cancer survivors and VDD/VDI combined was associated with body mass index (BMI) >85th percentile (Odds ratio [OR] = 5.4; P < 0.001), older age (OR = 2.2; P = 0.012), non-Caucasian or Hispanic race (OR = 4.5; P = 0.008) and summer versus winter season (OR = 0.12; P < 0.001). In multivariable analysis, VDI/VDD prevalence did not differ from the control group (VDI/VDD (43.3%)). In the combined survivor/control group multivariable analysis, cancer diagnosis did not increase VDI/VDD risk, but significant associations persisted with elevated BMI (P < 0.001), age (P = 0.004), non-Caucasian or Hispanic race (P < 0.001), and seasonality (P < 0.001). CONCLUSION: VDD/VDI is equally common in pediatric cancer survivors treated with corticosteroids and healthy children. The impact of VDD/VDI in cancer survivors may be greater due to risk for impaired bone health superimposed on that conferred from corticosteroid exposure. Thus, screening VDLs should be obtained in pediatric cancer survivors treated with corticosteroids, particularly in those with elevated BMI, older age, or non-Caucasian race. Prospective studies evaluating the impact of interventions to minimize VDD/VDI on long-term bone health in survivors are required.