Ariel Abad - Academia.edu (original) (raw)

Papers by Ariel Abad

In medical studies the categorical endpoints are quite often. Even though nowdays some models for... more In medical studies the categorical endpoints are quite often. Even though nowdays some models for handling this multicategorical variables have been developed their use is not common. This work shows an application of the Multivariate Generalized Linear Models to the analysis of Clinical Trials data. After a theoretical introduction models for ordinal and nominal responses are applied and the main results are discussed.

Statistical Modelling, May 21, 2015

In medical studies the categorical endpoints are quite often. Even though nowdays some models for... more In medical studies the categorical endpoints are quite often. Even though nowdays some models for handling this multicategorical variables have been developed their use is not common. This work shows an application of the Multivariate Generalized Linear Models to the analysis of Clinical Trials data. After a theoretical introduction models for ordinal and nominal responses are applied and the main

Clinical Trial Biostatistics and Biopharmaceutical Applications, 2014

Clinical Investigation, 2013

Statistics for Biology and Health, 2005

Computational Statistics & Data Analysis, 2004

Hierarchical models are common in complex surveys, psychometric applications, as well as agricult... more Hierarchical models are common in complex surveys, psychometric applications, as well as agricultural and biomedical applications, to name but a few. The context of interest here is meta-analysis, with emphasis on the use of such an approach in the evaluation of surrogate endpoints in randomized clinical trials. The methodology rests on the ability to replicate the e ect of treatment on both the true endpoint, as well as the candidate surrogate endpoint, across a number of trials. However, while a meta-analysis of clinical trials in the same indication seems the natural hierarchical structure, some authors have considered center or country as the unit, either because no meta-analytic data were available or because, even when available, they might not allow for a su cient level of replication. This leaves us with two important, related questions. First, how sensible is it to replace one level of replication by another one? Second, what are the consequences when a truly three-or higher-level model (e.g., trial, center, patient) is replaced by a coarser two-level structure (either trial and patient or center and patient). The same or similar questions may occur in a number of di erent settings, as soon as interest is placed on the validity of a conclusion at a certain level of the hierarchy, such as in sociological or genetic studies. Using the framework of normally distributed endpoints, these questions will be studied, using both analytic calculation as well as Monte Carlo simulation.

Biostatistics, 2010

Generalized linear mixed models have become a frequently used tool for the analysis of non-Gaussi... more Generalized linear mixed models have become a frequently used tool for the analysis of non-Gaussian longitudinal data. Estimation is often based on maximum likelihood theory, which assumes that the underlying probability model is correctly specified. Recent research shows that the results obtained from these models are not always robust against departures from the assumptions on which they are based. Therefore, diagnostic tools for the detection of model misspecifications are of the utmost importance. In this paper, we propose 2 diagnostic tests that are based on 2 equivalent representations of the model information matrix. We evaluate the power of both tests using theoretical considerations as well as via simulation. In the simulations, the performance of the new tools is evaluated in many settings of practical relevance, focusing on misspecification of the random-effects structure. In all the scenarios, the results were encouraging, however, the tests also exhibited inflated Type I error rates when the sample size was small or moderate. Importantly, a parametric bootstrap version of the tests seems to overcome this problem, although more research in this direction may be needed. Finally, both tests were also applied to analyze a real case study in psychiatry. by guest on June 2, 2016 http://biostatistics.oxfordjournals.org/ Downloaded from 772 A. ALONSO ABAD AND OTHERS

Statistics for Biology and Health, 2005

In medical studies the categorical endpoints are quite often. Even though nowdays some models for... more In medical studies the categorical endpoints are quite often. Even though nowdays some models for handling this multicategorical variables have been developed their use is not common. This work shows an application of the Multivariate Generalized Linear Models to the analysis of Clinical Trials data. After a theoretical introduction models for ordinal and nominal responses are applied and the main results are discussed.

Statistical Modelling, May 21, 2015

In medical studies the categorical endpoints are quite often. Even though nowdays some models for... more In medical studies the categorical endpoints are quite often. Even though nowdays some models for handling this multicategorical variables have been developed their use is not common. This work shows an application of the Multivariate Generalized Linear Models to the analysis of Clinical Trials data. After a theoretical introduction models for ordinal and nominal responses are applied and the main

Clinical Trial Biostatistics and Biopharmaceutical Applications, 2014

Clinical Investigation, 2013

Statistics for Biology and Health, 2005

Computational Statistics & Data Analysis, 2004

Hierarchical models are common in complex surveys, psychometric applications, as well as agricult... more Hierarchical models are common in complex surveys, psychometric applications, as well as agricultural and biomedical applications, to name but a few. The context of interest here is meta-analysis, with emphasis on the use of such an approach in the evaluation of surrogate endpoints in randomized clinical trials. The methodology rests on the ability to replicate the e ect of treatment on both the true endpoint, as well as the candidate surrogate endpoint, across a number of trials. However, while a meta-analysis of clinical trials in the same indication seems the natural hierarchical structure, some authors have considered center or country as the unit, either because no meta-analytic data were available or because, even when available, they might not allow for a su cient level of replication. This leaves us with two important, related questions. First, how sensible is it to replace one level of replication by another one? Second, what are the consequences when a truly three-or higher-level model (e.g., trial, center, patient) is replaced by a coarser two-level structure (either trial and patient or center and patient). The same or similar questions may occur in a number of di erent settings, as soon as interest is placed on the validity of a conclusion at a certain level of the hierarchy, such as in sociological or genetic studies. Using the framework of normally distributed endpoints, these questions will be studied, using both analytic calculation as well as Monte Carlo simulation.

Biostatistics, 2010

Generalized linear mixed models have become a frequently used tool for the analysis of non-Gaussi... more Generalized linear mixed models have become a frequently used tool for the analysis of non-Gaussian longitudinal data. Estimation is often based on maximum likelihood theory, which assumes that the underlying probability model is correctly specified. Recent research shows that the results obtained from these models are not always robust against departures from the assumptions on which they are based. Therefore, diagnostic tools for the detection of model misspecifications are of the utmost importance. In this paper, we propose 2 diagnostic tests that are based on 2 equivalent representations of the model information matrix. We evaluate the power of both tests using theoretical considerations as well as via simulation. In the simulations, the performance of the new tools is evaluated in many settings of practical relevance, focusing on misspecification of the random-effects structure. In all the scenarios, the results were encouraging, however, the tests also exhibited inflated Type I error rates when the sample size was small or moderate. Importantly, a parametric bootstrap version of the tests seems to overcome this problem, although more research in this direction may be needed. Finally, both tests were also applied to analyze a real case study in psychiatry. by guest on June 2, 2016 http://biostatistics.oxfordjournals.org/ Downloaded from 772 A. ALONSO ABAD AND OTHERS

Statistics for Biology and Health, 2005