Applied Statistics
http://hdl.handle.net/20.500.11824/10
2019-03-25T07:43:01ZA beta-binomial mixed-effects model approach for analysing longitudinal discrete and bounded outcomes
http://hdl.handle.net/20.500.11824/903
A beta-binomial mixed-effects model approach for analysing longitudinal discrete and bounded outcomes
Najera-Zuloaga J.; Lee D.-J.; Arostegui I.
Patient-reported outcomes (PROs) are currently being increasingly used as primary outcome measures in observational and experimental studies since they inform clinicians and researchers about the health-status of patients and generate data to facilitate improved care. PROs usually appear as discrete and bounded with U, J or inverse J-shapes and hence, exponential family members offer inadequate distributional fits. The beta-binomial distribution has been proposed in the literature to fit PROs. However, the fact that the beta-binomial distribution does not belong to the exponential family limits its applicability in the regression model context, and classical estimation approaches are not straightforward. Moreover, PROs are usually measured in a longitudinal framework in which individuals are followed up for a certain period. Hence, each individual obtains several scores of the PRO over time, which leads to the repeated-measures and defines the correlation structure in the data. In this work, we have developed and proposed an estimation procedure for the analysis of correlated discrete and bounded outcomes, particularly PROs, by a beta-binomial mixed-effects model. Additionally, we have implemented the methodology in the PROreg package in R. Because there are similar approaches in the literature to address the same issue, this work also incorporates a comparison study between our proposal and alternative methodologies commonly implemented in R and shows the superior performance of our estimation procedure. This paper was motivated by the analysis of the health-status of patients with chronic obstructive pulmonary disease, where the main objective is the assessment of risk factors that may affect the evolution of the disease. The application of the proposed approach in the study leads to clinically relevant results.
2018-05-01T00:00:00ZA statistical framework for radiation dose estimation with uncertainty quantification from the γ-H2AX assay
http://hdl.handle.net/20.500.11824/896
A statistical framework for radiation dose estimation with uncertainty quantification from the γ-H2AX assay
Einbeck J; Ainsbury EA; Sales R; Barnard S; Kaestle F; Higueras M
Over the last decade, the γ–H2AX focus assay, which exploits the phosphorylation of the H2AX histone following DNA double–strand–breaks, has made considerable progress towards acceptance as a reliable biomarker for exposure to ionizing radiation. While the existing literature has convincingly demonstrated a dose–response effect, and also presented approaches to dose estimation based on appropriately defined calibration curves, a more widespread practical use is still hampered by a certain lack of discussion and agreement on the specific dose–response modelling and uncertainty quantification strategies, as well as by the unavailability of implementations. This manuscript intends to fill these gaps, by stating explicitly the statistical models and techniques required for calibration curve estimation and subsequent dose estimation. Accompanying this article, a web applet has been produced which implements the discussed methods.
2018-11-28T00:00:00ZA note on Poisson goodness-of-fit tests for ionizing radiation induced chromosomal aberration samples
http://hdl.handle.net/20.500.11824/814
A note on Poisson goodness-of-fit tests for ionizing radiation induced chromosomal aberration samples
Higueras M.; González J.E.; Di Giorgio M.; Barquinero J.F.
Purpose: To present Poisson exact goodness-of-fit tests as alternatives and complements to the asymptotic u-test, which is the most widely used in cytogenetic biodosimetry, to decide whether a sample of chromosomal aberrations in blood cells comes from an homogeneous or inhomogeneous exposure.
Materials and methods: Three Poisson exact goodness-of-fit test from the literature are introduced and implemented in the R environment. A Shiny R Studio application, named GOF Poisson, has been updated for the purpose of giving support to this work. The three exact tests and the u-test are applied in chromosomal aberration data from clinical and accidental radiation exposure patients.
Results: It is observed how the u-test is not an appropriate approximation in small samples with small yield of chromosomal aberrations. Tools are provided to compute the three exact tests, which is not as trivial as the implementation of the u-test.
Conclusions: Poisson exact goodness-of-fit tests should be considered jointly to the u-test for detecting inhomogeneous exposures in the cytogenetic biodosimetry practice.
2018-06-13T00:00:00ZBayesian nonparametric inference for the covariate-adjusted ROC curve
http://hdl.handle.net/20.500.11824/811
Bayesian nonparametric inference for the covariate-adjusted ROC curve
Inacio de Carvalho V.; Rodríguez-Álvarez M.X.
Accurate diagnosis of disease is of fundamental importance in clinical practice and medical research. Before a medical diagnostic test is routinely used in practice, its ability to distinguish between diseased and nondiseased states must be rigorously assessed through statistical analysis. The receiver operating characteristic (ROC) curve is the most popular used tool for evaluating the discriminatory ability of continuous-outcome diagnostic tests. It has been acknowledged that several factors (e.g., subject-specific characteristics, such as age and/or gender) can affect the test's accuracy beyond disease status. Recently, the covariate-adjusted ROC curve has been proposed and successfully applied as a global summary measure of diagnostic accuracy that takes covariate information into account. We motivate the use of the covariate-adjusted ROC curve and develop a highly robust model based on a combination of B-splines dependent Dirichlet process mixture models and the Bayesian bootstrap. Multiple simulation studies demonstrate the ability of our model to successfully recover the true covariate-adjusted ROC curve and to produce valid inferences in a variety of complex scenarios. Our methods are motivated by and applied to an endocrine study where the main goal is to assess the accuracy of the body mass index, adjusted for age and gender, for predicting clusters of cardiovascular disease risk factors. The R-package AROC, implementing our proposed methods, is provided.
2018-05-30T00:00:00Z