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Modern Applied Statistics With S

On robust estimation of the location parameter

Modeling susceptibility to forest fires in the Central Corridor of the Atlantic Forest using the frequency ratio method.

Functional data clustering is to identify heterogeneous morphological patterns in the continuous functions underlying the discrete measurements/observations. Application of functional data clustering has appeared in many publications across various fields of sciences, including but not limited to biology, (bio)chemistry, engineering, environmental science, medical science, psychology, social science, and so on. The phenomenal growth of the application of functional data clustering indicates the urgent need for a systematic approach to develop efficient clustering methods and scalable algorithmic implementations. On the other hand, there is abundant literature on the cluster analysis of time series, trajectory data, spatio-temporal data, and so on, which are all related to functional data. Therefore, an overarching structure of existing functional data clustering methods will enable the cross-pollination of ideas across various research fields. We here conduct a comprehensive review of original clustering methods for functional data. We propose a systematic taxonomy that explores the connections and differences among the existing functional data clustering methods and relates them to the conventional multivariate clustering methods. The structure of the taxonomy is built on three main attributes of a functional data clustering method and therefore is more reliable than existing categorizations. The review aims to bridge the gap between the functional data analysis community and the clustering community and to generate new principles for functional data clustering.

/pdf/review-of-clustering-methods-for-functional-data-3bqbyb1b.pdf

Review of Clustering Methods for Functional Data

Stargardt disease, the most common inherited macular dystrophy, is characterized by vision loss due to central retinal atrophy. Although clinical trials for Stargardt are currently underway, the disease is typically slowly progressive, and objective, imaging-based biomarkers are critically needed. In this retrospective, observational study, we characterize the thicknesses of individual retinal sublayers by macular optical coherence tomography (OCT) in a large cohort of patients with molecularly-confirmed, ABCA4-associated Stargardt disease (STGD1) relative to normal controls. Automated segmentation of retinal sublayers was performed with manual correction as needed, and thicknesses in various macular regions were compared using mixed effects models. Relative to controls (42 eyes, 40 patients), STGD1 patients (107 eyes, 63 patients) had slight thickening of the nerve fiber layer and retinal pigment epithelium-Bruch’s membrane, with thinning in other sublayers, especially the outer nuclear layer (ONL) (p < 0.0015). When comparing the rate of retinal sublayer thickness change over time (mean follow-up 3.9 years for STGD1, 2.5 years for controls), STGD1 retinas thinned faster than controls in the outer retina (ONL to photoreceptor outer segments). OCT-based retinal sublayer thickness measurements are feasible in STGD1 patients and may provide objective measures of disease progression or treatment response.

/pdf/analysis-of-retinal-sublayer-thicknesses-and-rates-of-change-vw2lviko4g.pdf

Analysis of retinal sublayer thicknesses and rates of change in ABCA4-associated Stargardt disease

In this review, we present an overview of the main aspects related to the statistical evaluation of medical tests for diagnosis and prognosis. Measures of diagnostic performance for binary tests, s...

Statistical Evaluation of Medical Tests

The extraordinary advancements in neuroscientific technology for brain recordings over the last decades have led to increasingly complex spatiotemporal data sets. To reduce oversimplifications, new models have been developed to be able to identify meaningful patterns and new insights within a highly demanding data environment. To this extent, we propose a new model called parameter clustering functional principal component analysis (PCl-fPCA) that merges ideas from functional data analysis and Bayesian nonparametrics to obtain a flexible and computationally feasible signal reconstruction and exploration of spatiotemporal neuroscientific data. In particular, we use a Dirichlet process Gaussian mixture model to cluster functional principal component scores within the standard Bayesian functional PCA framework. This approach captures the spatial dependence structure among smoothed time series (curves) and its interaction with the time domain without imposing a prior spatial structure on the data. Moreover, by moving the mixture from data to functional principal component scores, we obtain a more general clustering procedure, thus allowing a higher level of intricate insight and understanding of the data. We present results from a simulation study showing improvements in curve and correlation reconstruction compared with different Bayesian and frequentist fPCA models and we apply our method to functional magnetic resonance imaging and electroencephalogram data analyses providing a rich exploration of the spatiotemporal dependence in brain time series.

Parameter clustering in Bayesian functional principal component analysis of neuroscientific data.

In this review, we present an overview of the main aspects related to the statistical evaluation of medical tests for diagnosis and prognosis. Measures of diagnostic performance for binary tests, such as sensitivity, specificity, and predictive values, are introduced, and extensions to the case of continuous-outcome tests are detailed. Special focus is placed on the receiver operating characteristic (ROC) curve and its estimation, with the topic of covariate adjustment receiving a great deal of attention. The extension to the case of time-dependent ROC curves for evaluating prognostic accuracy is also touched upon. We apply several of the approaches described to a dataset derived from a study aimed to evaluate the ability of HOMA-IR (homeostasis model assessment of insulin resistance) levels to identify individuals at high cardio-metabolic risk and how such discriminatory ability might be influenced by age and gender. We also outline software available for the implementation of the methods.

/pdf/rocnreg-an-r-package-for-receiver-operating-characteristic-34jwdivrkm.pdf

ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference with and without Covariates

The receiver operating characteristic (ROC) curve is the most popular tool used to evaluate the discriminatory capability of diagnostic tests/biomarkers measured on a continuous scale when distinguishing between two alternative disease states (e.g, diseased and nondiseased). In some circumstances, the test's performance and its discriminatory ability may vary according to subject-specific characteristics or different test settings. In such cases, information-specific accuracy measures, such as the covariate-specific and the covariate-adjusted ROC curve are needed, as ignoring covariate information may lead to biased or erroneous results. This paper introduces the R package ROCnReg that allows estimating the pooled (unadjusted) ROC curve, the covariate-specific ROC curve, and the covariate-adjusted ROC curve by different methods, both from (semi) parametric and nonparametric perspectives and within Bayesian and frequentist paradigms. From the estimated ROC curve (pooled, covariate-specific or covariate-adjusted), several summary measures of accuracy, such as the (partial) area under the ROC curve and the Youden index, can be obtained. The package also provides functions to obtain ROC-based optimal threshold values using several criteria, namely, the Youden Index criterion and the criterion that sets a target value for the false positive fraction. For the Bayesian methods, we provide tools for assessing model fit via posterior predictive checks, while model choice can be carried out via several information criteria. Numerical and graphical outputs are provided for all methods. The package is illustrated through the analyses of data from an endocrine study where the aim is to assess the capability of the body mass index to detect the presence or absence of cardiovascular disease risk factors. The package is available from CRAN at this https URL.

ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference with and without Covariate Information

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Papers

Statistical Evaluation of Medical Tests

Parameter clustering in Bayesian functional principal component analysis of neuroscientific data.

Statistical Evaluation of Medical Tests

ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference with and without Covariates

ROCnReg: An R Package for Receiver Operating Characteristic Curve Inference with and without Covariate Information