A FLEXIBLE GENERAL CLASS OF MARGINAL AND CONDITIONAL RANDOM INTERCEPT MODELS FOR BINARY OUTCOMES USING MIXTURES OF NORMALS

Random intercept models for binary data are useful tools for addressing between subject heterogeneity. Unlike linear models, the non-linearity of link functions used for binary data force a distinction between marginal and conditional interpretations. This distinction is blurred in probit models with a normally distributed random intercept because the resulting model implies a probit marginal link as well. That is, this model is closed in the sense that the distribution associated with the marginal and conditional link functions and the random effect distribution are all of the same family. In this manuscript we explore another family of random intercept models with this property. In particular, we consider instances when the distributions associated with the conditional and marginal link functions and the random effect distribution are mixtures of normals. We show that this flexible family of models is related to several others presented in the literature. Moreover, we also show that this family of models offers considerable computational benefits. A diverse series of examples illustrates the wide applicability of the approach

A Simple Class of Bayesian Nonparametric Autoregression Models

We introduce a model for a time series of continuous outcomes, that can be expressed as fully nonparametric regression or density regression on lagged terms. The model is based on a dependent Dirichlet process prior on a family of random probability measures indexed by the lagged covariates. The approach is also extended to sequences of binary responses. We discuss implementation and applications of the models to a sequence of waiting times between eruptions of the Old Faithful Geyser, and to a dataset consisting of sequences of recurrence indicators for tumors in the bladder of several patients.MIUR 2008MK3AFZFONDECYT 1100010NIH/NCI R01CA075981Mathematic

Bayesian Inference in a Sample Selection Model

This paper develops methods of Bayesian inference in a sample selection model. The main feature of this model is that the outcome variable is only partially observed. We first present a Gibbs sampling algorithm for a model in which the selection and outcome errors are normally distributed. The algorithm is then extended to analyze models that are characterized by nonnormality. Specifically, we use a Dirichlet process prior and model the distribution of the unobservables as a mixture of normal distributions with a random number of components. The posterior distribution in this model can simultaneously detect the presence of selection effects and departures from normality. Our methods are illustrated using some simulated data and an abstract from the RAND health insurance experiment

A generalized linear mixed model for longitudinal binary data with a marginal logit link function

Longitudinal studies of a binary outcome are common in the health, social, and behavioral sciences. In general, a feature of random effects logistic regression models for longitudinal binary data is that the marginal functional form, when integrated over the distribution of the random effects, is no longer of logistic form. Recently, Wang and Louis [Biometrika 90 (2003) 765--775] proposed a random intercept model in the clustered binary data setting where the marginal model has a logistic form. An acknowledged limitation of their model is that it allows only a single random effect that varies from cluster to cluster. In this paper we propose a modification of their model to handle longitudinal data, allowing separate, but correlated, random intercepts at each measurement occasion. The proposed model allows for a flexible correlation structure among the random intercepts, where the correlations can be interpreted in terms of Kendall's $\tau$. For example, the marginal correlations among the repeated binary outcomes can decline with increasing time separation, while the model retains the property of having matching conditional and marginal logit link functions. Finally, the proposed method is used to analyze data from a longitudinal study designed to monitor cardiac abnormalities in children born to HIV-infected women.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS390 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

FAIRNESS OF NATIONAL HEALTH SERVICE IN ITALY: A BIVARIATE CORRELATED RANDOM EFFECTS MODEL

In this paper we consider a possible way of measuring equity in health as the absence of systematic disparities in health (or in the major social determinants of health) between groups with different levels of underlying social advantage/disadvantage. Starting from the fairness approach developed by the World Health Organization, we propose to extend the analysis of fairness in nancing contribution through a generalized linear mixed models framework by introducing a bivariate correlated random effects model. We aim at analyzing the burden of health care payment on Italian households by modeling catastrophic payments and impoverishment due to health care expenditures. For this purpose, we describe a bivariate model for binary data, where association between the outcomes is modeled through outcome-specic latent effects which are assumed to be correlated; we show how model parameters can be estimated in a nite mixture context. By using such model specication, the fairness of the Italian national health service is investigated.fairness, health care, random eects models, binary data, non parametric maximum likelihood.

A partially collapsed Gibbs sampler for Bayesian quantile regression

We introduce a set of new Gibbs sampler for Bayesian analysis of quantile re-gression model. The new algorithm, which partially collapsing an ordinary Gibbs sampler, is called Partially Collapsed Gibbs (PCG) sampler. Although the Metropolis-Hastings algorithm has been employed in Bayesian quantile regression, including median regression, PCG has superior convergence properties to an ordinary Gibbs sampler. Moreover, Our PCG sampler algorithm, which is based on a theoretic derivation of an asymmetric Laplace as scale mixtures of normal distributions, requires less computation than the ordinary Gibbs sampler and can significantly reduce the computation involved in approximating the Bayes Factor and marginal likelihood. Like the ordinary Gibbs sampler, the PCG sample can also be used to calculate any associated marginal and predictive distributions. The quantile regression PCG sampler is illustrated by analysing simulated data and the data of length of stay in hospital. The latter provides new insight into hospital perfor-mance. C-code along with an R interface for our algorithms is publicly available on request from the first author. JEL classification: C11, C14, C21, C31, C52, C53

Development and Application of Bayesian Semiparametric Models For Dependent Data

Dependent data are very common in many research fields, such as medicine (repeated measures), finance (time series), traffic (clustered), etc. Effective control/modeling of the dependency among data can enhance the performance of the models and result in better prediction. In many cases, the correlation itself may be of great interest. In this dissertation, we develop novel Bayesian semi-/nonparametric regression models to analyze data with various dependence structures. In Chapter 2, a Bayesian non- parametric multivariate ordinal regression model is proposed to fit drinking behavior survey data from DWI offenders. The responses are two-dimensional ordinal data, drinking frequency and drinking quantity at each occasion. In Chapter 3, we develop a hierarchical Gaussian process model to analyze repeated hearing test data of pe- diatric cancer patients. A penalized B-spline is used to capture the overall trend of the curve. Individual intercepts and slopes as random effects are allowed to model individual deviation from the population mean. Since the curves are theoretically smooth, a hierarchical Gaussian process is assumed on top of the individual-specific mean function. In Chapter 4, we propose a constructive approach to imposing a mean constraint in a finite mixture of multivariate normal densities. Implemented in a linear mixed model, the effectiveness of the constraint is verified by both simulation and data analysis using longitudinal cholesterol data

A goodness-of-fit test for the random-effects distribution in mixed models

In this paper, we develop a simple diagnostic test for the random-effects distribution in mixed models. The test is based on the gradient function, a graphical tool proposed by Verbeke and Molenberghs to check the impact of assumptions about the random-effects distribution in mixed models on inferences. Inference is conducted through the bootstrap. The proposed test is easy to implement and applicable in a general class of mixed models. The operating characteristics of the test are evaluated in a simulation study, and the method is further illustrated using two real data analyses