arXiv subjects

Vanda Inacio

Publications and source records attributed to Vanda Inacio.

7 recordsLinked to original sources

arXiv2025

The underlap coefficient as a measure of a biomarker's discriminatory ability

The first step in evaluating a potential diagnostic biomarker is to examine the variation in its values across different disease groups. In a three-class disease setting, the volume under the receiver operating characteristic surface and the three-class Youden index are commonly used summary measures of a biomarker's discriminatory ability. However, these measures rely on a stochastic ordering assumption for the distributions of biomarker outcomes across the three groups. This assumption can be restrictive, particularly when covariates are involved, and its violation may lead to incorrect conclusions about a biomarker's ability to distinguish between the three disease classes. Even when a stochastic ordering exists, the order may vary across different biomarkers in discovery studies involving dozens or even thousands of candidate biomarkers, complicating automated ranking. To address these challenges and complement existing measures, we propose the underlap coefficient, a novel summary index of a biomarker's ability to distinguish between three (or more) disease groups, and study its properties. Additionally, we introduce Bayesian nonparametric estimators for both the unconditional underlap coefficient and its covariate-specific counterpart. These estimators are broadly applicable to a wide range of biomarkers and populations. A simulation study reveals a good performance of the proposed estimators across a range of conceivable scenarios. We illustrate the proposed approach through an application to an Alzheimer's disease (AD) dataset aimed to assess how four potential AD biomarkers distinguish between individuals with normal cognition, mild impairment, and dementia, and how and if age and gender impact this discriminatory ability.

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arXiv2025

Likelihood-based Modeling of Covariate-Specific Time-Dependent ROC Curves

Identifying reliable biomarkers for predicting clinical events in longitudinal studies is important for accurate disease prognosis and for guiding development of new treatments. However, prognostic studies are often observational, making it difficult to account for patient heterogeneity. In amyotrophic lateral sclerosis (ALS), factors such as age, site of onset and genetic status influence both survival and biomarker levels, yet their impact on the prognostic accuracy of biomarkers over time remains unclear. While time-dependent receiver operating characteristic methods have been developed to handle censored time-to-event outcomes, most do not adjust for covariates. To address this, we propose the nonparanormal prognostic biomarker framework, which models the joint distribution of the biomarker and event time while accounting for covariates. This allows estimation of covariate-specific time-dependent ROC curves and related summary measures. We apply the NPB framework to evaluate serum neurofilament light as a prognostic biomarker in ALS, showing that its accuracy varies over time and with patient characteristics. By capturing these covariate-specific effects, the NPB framework supports more targeted risk stratification and can potentially improve the design of clinical trials for new ALS treatments.

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arXiv2023

Bayesian dependent mixture models: A predictive comparison and survey

For exchangeable data, mixture models are an extremely useful tool for density estimation due to their attractive balance between smoothness and flexibility. When additional covariate information is present, mixture models can be extended for flexible regression by modeling the mixture parameters, namely the weights and atoms, as functions of the covariates. These types of models are interpretable and highly flexible, allowing non only the mean but the whole density of the response to change with the covariates, which is also known as density regression. This article reviews Bayesian covariate-dependent mixture models and highlights which data types can be accommodated by the different models along with the methodological and applied areas where they have been used. In addition to being highly flexible, these models are also numerous; we focus on nonparametric constructions and broadly organize them into three categories: 1) joint models of the responses and covariates, 2) conditional models with single-weights and covariate-dependent atoms, and 3) conditional models with covariate-dependent weights. The diversity and variety of the available models in the literature raises the question of how to choose among them for the application at hand. We attempt to shed light on this question through a careful analysis of the predictive equations for the conditional mean and density function as well as predictive comparisons in three simulated data examples.

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arXiv2020

Statistical Evaluation of Medical Tests

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.

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arXiv2020

Robust and flexible inference for the covariate-specific ROC curve

Diagnostic tests are of critical importance in health care and medical research. Motivated by the impact that atypical and outlying test outcomes might have on the assessment of the discriminatory ability of a diagnostic test, we develop a flexible and robust model for conducting inference about the covariate-specific receiver operating characteristic (ROC) curve that safeguards against outlying test results while also accommodating for possible nonlinear effects of the covariates. Specifically, we postulate a location-scale additive regression model for the test outcomes in both the diseased and nondiseased populations, combining additive cubic B-splines and M-estimation for the regression function, while the residuals are estimated via a weighted empirical distribution function. The results of the simulation study show that our approach successfully recovers the true covariate-specific ROC curve and corresponding area under the curve on a variety of conceivable test outcomes contamination scenarios. Our method is applied to a dataset derived from a prostate cancer study where we seek to assess the ability of the Prostate Health Index to discriminate between men with and without Gleason 7 or above prostate cancer, and if and how such discriminatory capacity changes with age.

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arXiv2020

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

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 https://CRAN.R-project.org/package=ROCnReg.

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arXiv2019

Parameter clustering in Bayesian functional PCA of fMRI data

The extraordinary advancements in neuroscientific technology for brain recordings over the last decades have led to increasingly complex spatio-temporal datasets. 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 spatio-temporal 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 spatio-temporal dependence in brain time series.

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