1,720,970 research outputs found
Nonparametric confidence regions for the symmetry point-based optimal cutpoint and associated sensitivity of a continuous-scale diagnostic test
No full textIn medical research, diagnostic tests with continuous values are widely employed to attempt to distinguish between diseased and non-diseased subjects. The diagnostic accuracy of a test (or a biomarker) can be assessed by using the receiver operating characteristic (ROC) curve of the test. To summarize the ROC curve and primarily to determine an “optimal” threshold for test results to use in practice, several approaches may be considered, such as those based on the Youden index, on the so-called close-to-(0,1) point, on the concordance probability and on the symmetry point. In this paper, we focus on the symmetry point-based approach, that simultaneously controls the probabilities of the two types of correct classifications (healthy as healthy and diseased as diseased), and show how to get joint nonparametric confidence regions for the corresponding optimal cutpoint and the associated sensitivity (= specificity) value. Extensive simulation experiments are conducted to evaluate the finite sample performances of the proposed method. Real datasets are also used to illustrate its application
A New Evaluation Strategy for Diagnostic Tests Under Umbrella or Tree Ordering
No full textIn medical research, diagnostic tests (or biomarkers) with continuous values are widely employed to attempt to distinguish between diseased and non-diseased subjects. The diagnostic accuracy of a test can be assessed by its receiver operating characteristic (ROC) curve. For diseases with multiclasses, an important category of scenarios assumes tree or umbrella ordering, where the test measurement for one particular class is lower or higher than those for the other classes. In this paper, we propose a new ROC framework for tree or umbrella ordering, together with a related evaluation strategy. Such a strategy is based on new ROC representations on the plane, denoted as LTROC and UTROC, and new summary indexes. Related statistical inference is also discussed. In particular, we propose simple estimation and interval estimation procedures, in a nonparametric setting. For these procedures, we provide theoretical justification and assess the behaviour in finite samples through simulation experiments. Finally, we illustrate the proposed approach with two real data examples
A New Evaluation Strategy for Diagnostic Tests Under Umbrella or Tree Ordering
No full textIn medical research, diagnostic tests (or biomarkers) with continuous values are widely employed to attempt to distinguish between diseased and non-diseased subjects. The diagnostic accuracy of a test can be assessed by its receiver operating characteristic (ROC) curve. For diseases with multiclasses, an important category of scenarios assumes tree or umbrella ordering, where the test measurement for one particular class is lower or higher than those for the other classes. In this paper, we propose a new ROC framework for tree or umbrella ordering, together with a related evaluation strategy. Such a strategy is based on new ROC representations on the plane, denoted as LTROC and UTROC, and new summary indexes. Related statistical inference is also discussed. In particular, we propose simple estimation and interval estimation procedures, in a nonparametric setting. For these procedures, we provide theoretical justification and assess the behaviour in finite samples through simulation experiments. Finally, we illustrate the proposed approach with two real data examples
A note on the asymptotic behaviour of empirical likelihood statistics.
No full textThis paper develops some theoretical results about the asymptotic behaviour
of the empirical likelihood and the empirical profile likelihood statistics, which
originate from fairly general estimating functions. The results accommodate, within a
unified framework, various situations potentially occurring in a wide range of applications.
For this reason, they are potentially useful in several contexts, such as, for
example, in inference for dependent data. We provide examples showing that known
findings in literature about the asymptotic behaviour of some empirical likelihood
statistics in time series models can be derived as particular cases of our results
Nearest-neighbor estimation for ROC analysis under verification bias
Full text linkFor a continuous-scale diagnostic test, the receiver operating characteristic (ROC) curve is a popular tool for displaying the ability of the test to discriminate between healthy and diseased subjects. In some studies, verification of the true disease status is performed only for a subset of subjects, possibly depending on the test result and other characteristics of the subjects. Estimators of the ROC curve based only on this subset of subjects are typically biased; this is known as verification bias. Methods have been proposed to correct verification bias, in particular under the assumption that the true disease status, if missing, is missing at random (MAR). MAR assumption means that the probability of missingness depends on the true disease status only through the test result and observed covariate information. However, the existing methods require parametric models for the (conditional) probability of disease and/or the (conditional) probability of verification, and hence are subject to model misspecification: a wrong specification of such parametric models can affect the behavior of the estimators, which can be inconsistent. To avoid misspecification problems, in this paper we propose a fully nonparametric method for the estimation of the ROC curve of a continuous test under verification bias. The method is based on nearest-neighbor imputation and adopts generic smooth regression models for both the probability that a subject is diseased and the probability that it is verified. Simulation experiments and an illustrative example show the usefulness of the new method. Variance estimation is also discussed
Covariate-Specific Estimation of the Sensitivity to the Early Disease Stage in Diagnostic Tests
No full textWithin the three-class ROC analysis framework, we address the problem of making inferences about a true class fraction (TCF), given the remaining two. More precisely, we propose a procedure to estimate the covariate-specific TCF, i.e., the covariate-specific probability of correct classification at the so-called early stage, when the values for the true class fractions at first and third classes are fixed. An application to a real dataset is also presented
Semiparametric interval estimation of Pr[Y > X]
Full text linkLet X and Y be two independent continuous random variables. We discuss three techniques to obtain confidence intervals for ρ_Pr[Y > X] in a semiparametric framework. One method relies on the asymptotic normality of an estimator for ρ; the remaining methods involve empirical likelihood and combine it with maximum likelihood estimation and with full parametric likelihood, respectively. Finite-sample accuracy of the confidence intervals is assessed through a simulation study. An illustration is given using a dataset on the detection of carriers of Duchenne Muscular Dystrophy
Bayesian inference with a pairwise likelihood: an approach based on empirical likelihood
No full textIn several applications, the model of interest is such that its likelihood function is difficult, or even impractical, to compute. For these situations, it is common to substitute the likelihood with a surrogate, which resembles the full likelihood but is easier to calculate. Among these surrogates are composite likelihoods and in particular pairwise likelihoods. Their properties in classical inference have been widely discussed in the literature; their use within a Bayesian approach has been seldom considered. The substitution of the likelihood with a surrogate is not straightforward in Bayesian inference: the posterior distribution which is obtained must be validated on a case by case basis, as general results are not available. We propose a Bayesian procedure in which the surrogate is the empirical likelihood derived from the pairwise score equation. This pseudo-likelihood has standard asymptotic properties, so the validation of the posterior distribution is based on its asymptotic behavior
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