Hierarchical modelling of patient-reported outcomes data based on the beta-binomial distribution

The beta-binomial distribution does not belong to the exponential family and, hence classical regression techniques cannot be used when dealing with outcomes following the mentioned distribution. In this thesis we propose and develop regression models based on the beta-binomial distribution for the analysis of U, J or inverse J-shaped discrete and bounded outcomes. In fact, although this thesis is focused on the analysis of patient-reported outcomes (PROs), which usually follow the mentioned distributional shapes, proposed models can also be extended to several fields. First of all, we make a review and comparison of existing beta-binomial regression approaches in independent data context, concluding that the marginal approach is the most adequate. However, PRO studies are usually carried out in a longitudinal framework, where patients' responses are measured over time. This leads to a multilevel or correlated data structure and consequently, we extend the marginal beta-binomial regression approach to the inclusion of random effects to accommodate the hierarchical structure of the data. We develop the estimation and inference procedure for the model proposal. Furthermore, we compare the performance of our proposal with similar approaches in the literature, showing that it gets better results in terms of reducing the bias of the estimates. We apply the model to a longitudinal Chronic Obstructive Pulmonary Disease study carried out at Galdakao Hospital, reaching clinically and statistically relevant results about the evolution of the patients over time. PROs are usually obtained using rating scale questionnaires consisting of questions or items, grouped into one or more subscales, often called dimensions or domains. Therefore, we also propose a multivariate regression model based on the beta-binomial distribution for the joint analysis of all the longitudinal dimensions provided by different questionnaires. Finally, it is worth mentioning that we have implemented all the proposed regression models in the PROreg R- package which is freely available at CRAN.Department of Education, Language Policy and Culture of the Basque Government IT-620-13 program, MTM2013-40941-P and MTM2016-74931-

P02-04. Multimeric soluble CD40 ligand efficiently enhances HIV specific cellular immune responses during DNA prime and boost with attenuated poxvirus strains

1 page.-- Poster presentation.-- This article is part of the supplement: AIDS Vaccine 2009.Peer reviewe

Comparison of beta-binomial regression model approaches to analyze health related quality of life data

Health related quality of life (HRQoL) has become an increasingly important indicator of health status in clinical trials and epidemiological research. Moreover, the study of the relationship of HRQoL with patients' and disease's characteristics has become one of the primary aims of many HRQoL studies. HRQoL scores are usually assumed to be distributed as binomial random variables and often highly skewed. The use of the beta-binomial distribution in the regression context has been proposed to model such data, however, the beta-binomial regression has been performed by means of two di erent approaches in the literature: i) beta-binomial distribution with a logistic link; and ii) hierarchical generalized linear models (HGLMs). None of the existing literature in the analysis of HRQoL survey data has performed a comparison of both approaches in terms of adequacy and regression parameter interpretation context. This paper is motivated by the analysis of a real data application of HRQoL outcomes in patients with Chronic Obstructive Pulmonary Disease (COPD), where the use of both approaches yields to contradictory results in terms of covariate e ects signi cance and consequently the interpretation of the most relevant factors in HRQoL. We present an explanation of the results in both methodologies through a simulation study and address the need to apply the proper approach in the analysis of HRQoL survey data for practitioners, providing an R package.IT-620-13, MTM2013-40941-P, MTM2014-52184-P, MTM2016-74931-P, RD12/0001/0001 - REDISSE

cdcatR: An R package for cognitive diagnostic computerized adaptive testing

Cognitive diagnosis models (CDMs) are confirmatory latent class models that provide fine-grained information about skills and cognitive processes. These models have gained attention in the last few years because of their usefulness in educational and psychological settings. Recently, numerous developments have been made to allow for the implementation of cognitive diagnosis computerized adaptive testing (CD-CAT). Despite methodological advances, CD-CAT applications are still scarce. To facilitate research and the emergence of empirical applications in this area, we have developed the cdcatR package for R software. The purpose of this document is to illustrate the different functions included in this package. The package includes functionalities for data generation, model selection based on relative fit information, implementation of several item selection rules (including item exposure control), and CD-CAT performance evaluation in terms of classification accuracy, item exposure, and test length. In conclusion, an R package is made available to researchers and practitioners that allows for an easy implementation of CD-CAT in both simulation and applied studies. Ultimately, this is expected to facilitate the development of empirical applications in this areaThis research was funded by Ministerio de Ciencia e Innovación, grant number PSI2017- 85022-P, and Cátedra de Modelos y Aplicaciones Psicométricas (Instituto de Ingeniería del Conocimiento and Autonomous University of Madrid

Topological properties of inequality and deprivation in an educational system: Unveiling the key-drivers through complex network analysis

This research conceives an educational system as a complex network to incorporate a rich framework for analyzing topological and statistical proper-ties of inequality and learning deprivation at different levels, as well as to simu-late the structure, stability and fragility of the educational system. The model provides a natural way to represent educational phenomena, allowing to test public policies by computation before being implemented, bringing the oppor-tunity of calibrating control parameters for assessing order parameters over time in multiple territorial scales. This approach provides a set of unique advantages over classical analysis tools because it allows the use of large-scale assessments and other evidences for combining the richness of qualitative analysis with quantitative inferences for measuring inequality gaps. An additional advantage, as shown in our results using real data from a Latin American country, is to provide a solution to con-cerns about the limitations of case studies or isolated statistical approaches.info:eu-repo/semantics/acceptedVersio

Understanding the Logistics for the Distribution of Heme in Cells

[Image: see text] Heme is essential for the survival of virtually all living systems—from bacteria, fungi, and yeast, through plants to animals. No eukaryote has been identified that can survive without heme. There are thousands of different proteins that require heme in order to function properly, and these are responsible for processes such as oxygen transport, electron transfer, oxidative stress response, respiration, and catalysis. Further to this, in the past few years, heme has been shown to have an important regulatory role in cells, in processes such as transcription, regulation of the circadian clock, and the gating of ion channels. To act in a regulatory capacity, heme needs to move from its place of synthesis (in mitochondria) to other locations in cells. But while there is detailed information on how the heme lifecycle begins (heme synthesis), and how it ends (heme degradation), what happens in between is largely a mystery. Here we summarize recent information on the quantification of heme in cells, and we present a discussion of a mechanistic framework that could meet the logistical challenge of heme distribution

Improving reliability estimation in cognitive diagnosis modeling

Cognitive diagnosis models (CDMs) are used in educational, clinical, or personnel selection settings to classify respondents with respect to discrete attributes, identifying strengths and needs, and thus allowing to provide tailored training/treatment. As in any assessment, an accurate reliability estimation is crucial for valid score interpretations. In this sense, most CDM reliability indices are based on the posterior probabilities of the estimated attribute profiles. These posteriors are traditionally computed using point estimates for the model parameters as approximations to their populational values. If the uncertainty around these parameters is unaccounted for, the posteriors may be overly peaked, deriving into overestimated reliabilities. This article presents a multiple imputation (MI) procedure to integrate out the model parameters in the estimation of the posterior distributions, thus correcting the reliability estimation. A simulation study was conducted to compare the MI procedure with the traditional reliability estimation. Five factors were manipulated: the attribute structure, the CDM model (DINA and G-DINA), test length, sample size, and item quality. Additionally, an illustration using the Examination for the Certificate of Proficiency in English data was analyzed. The effect of sample size was studied by sampling subsets of subjects from the complete data. In both studies, the traditional reliability estimation systematically provided overestimated reliabilities, whereas the MI procedure offered more accurate results. Accordingly, practitioners in small educational or clinical settings should be aware that the reliability estimation using model parameter point estimates may be positively biased. R codes for the MI procedure are made availableOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work has been funded by the Community of Madrid through the Pluriannual Agreement with the Universidad de Universidad Autónoma de Madrid in its Programa de Estímulo a la Investigación de Jóvenes Doctores (Reference SI3/ PJI/2021-00258), and by the Spanish Ministry of Science and Innovation (FPI BES-2016-077814