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- Air pollution (1)
- Bayesian model (1)
- Binary outcomes; Copulas; Marginal likelihood; Multivariate logit; Multivariate probit: Stable distributions (1)
- Cerebrovascular disease (1)
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- Generalized additive models (1)
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Articles 1 - 7 of 7
Full-Text Articles in Categorical Data Analysis
A Unified Approach To Modeling Multivariate Binary Data Using Copulas Over Partitions, Bruce J. Swihart, Brian Caffo, Ciprian Crainiceanu
A Unified Approach To Modeling Multivariate Binary Data Using Copulas Over Partitions, Bruce J. Swihart, Brian Caffo, Ciprian Crainiceanu
Johns Hopkins University, Dept. of Biostatistics Working Papers
Many seemingly disparate approaches for marginal modeling have been developed in recent years. We demonstrate that many current approaches for marginal modeling of correlated binary outcomes produce likelihoods that are equivalent to the proposed copula-based models herein. These general copula models of underlying latent threshold random variables yield likelihood based models for marginal fixed effects estimation and interpretation in the analysis of correlated binary data. Moreover, we propose a nomenclature and set of model relationships that substantially elucidates the complex area of marginalized models for binary data. A diverse collection of didactic mathematical and numerical examples are given to illustrate …
Multilevel Latent Class Models With Dirichlet Mixing Distribution, Chongzhi Di, Karen Bandeen-Roche
Multilevel Latent Class Models With Dirichlet Mixing Distribution, Chongzhi Di, Karen Bandeen-Roche
Johns Hopkins University, Dept. of Biostatistics Working Papers
Latent class analysis (LCA) and latent class regression (LCR) are widely used for modeling multivariate categorical outcomes in social sciences and biomedical studies. Standard analyses assume data of different respondents to be mutually independent, excluding application of the methods to familial and other designs in which participants are clustered. In this paper, we develop multilevel latent class model, in which subpopulation mixing probabilities are treated as random effects that vary among clusters according to a common Dirichlet distribution. We apply the Expectation-Maximization (EM) algorithm for model fitting by maximum likelihood (ML). This approach works well, but is computationally intensive when …
A Flexible General Class Of Marginal And Conditional Random Intercept Models For Binary Outcomes Using Mixtures Of Normals, Brian Caffo, Ming-Wen An, Charles A. Rohde
A Flexible General Class Of Marginal And Conditional Random Intercept Models For Binary Outcomes Using Mixtures Of Normals, Brian Caffo, Ming-Wen An, Charles A. Rohde
Johns Hopkins University, Dept. of Biostatistics Working Papers
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 …
A User-Friendly Introduction To Link-Probit-Normal Models, Brian S. Caffo, Michael Griswold
A User-Friendly Introduction To Link-Probit-Normal Models, Brian S. Caffo, Michael Griswold
Johns Hopkins University, Dept. of Biostatistics Working Papers
Probit-normal models have attractive properties compared to logit-normal models. In particular, they allow for easy specification of marginal links of interest while permitting a conditional random effects structure. Moreover, programming fitting algorithms for probit-normal models can be trivial with the use of well-developed algorithms for approximating multivariate normal quantiles. In typical settings, the data cannot distinguish between probit and logit conditional link functions. Therefore, if marginal interpretations are desired, the default conditional link should be the most convenient one. We refer to models with a probit conditional link an arbitrary marginal link and a normal random effect distribution as link-probit-normal …
The Proportional Odds Model For Assessing Rater Agreement With Multiple Modalities, Elizabeth Garrett-Mayer, Steven N. Goodman, Ralph H. Hruban
The Proportional Odds Model For Assessing Rater Agreement With Multiple Modalities, Elizabeth Garrett-Mayer, Steven N. Goodman, Ralph H. Hruban
Johns Hopkins University, Dept. of Biostatistics Working Papers
In this paper, we develop a model for evaluating an ordinal rating systems where we assume that the true underlying disease state is continuous in nature. Our approach in motivated by a dataset with 35 microscopic slides with 35 representative duct lesions of the pancreas. Each of the slides was evaluated by eight raters using two novel rating systems (PanIN illustrations and PanIN nomenclature),where each rater used each systems to rate the slide with slide identity masked between evaluations. We find that the two methods perform equally well but that differentiation of higher grade lesions is more consistent across raters …
Hierarchical Bivariate Time Series Models: A Combined Analysis Of The Effects Of Particulate Matter On Morbidity And Mortality, Francesca Dominici, Antonella Zanobetti, Scott L. Zeger, Joel Schwartz, Jonathan M. Samet
Hierarchical Bivariate Time Series Models: A Combined Analysis Of The Effects Of Particulate Matter On Morbidity And Mortality, Francesca Dominici, Antonella Zanobetti, Scott L. Zeger, Joel Schwartz, Jonathan M. Samet
Johns Hopkins University, Dept. of Biostatistics Working Papers
In this paper we develop a hierarchical bivariate time series model to characterize the relationship between particulate matter less than 10 microns in aerodynamic diameter (PM10) and both mortality and hospital admissions for cardiovascular diseases. The model is applied to time series data on mortality and morbidity for 10 metropolitan areas in the United States from 1986 to 1993. We postulate that these time series should be related through a shared relationship with PM10.
At the first stage of the hierarchy, we fit two seemingly unrelated Poisson regression models to produce city-specific estimates of the log relative rates of mortality …
Cross-Calibration Of Stroke Disability Measures: Bayesian Analysis Of Longitudinal Ordinal Categorical Data Using Negative Dependence, Giovanni Parmigiani, Heidi W. Ashih, Gregory P. Samsa, Pamela W. Duncan, Sue Min Lai, David B. Matchar
Cross-Calibration Of Stroke Disability Measures: Bayesian Analysis Of Longitudinal Ordinal Categorical Data Using Negative Dependence, Giovanni Parmigiani, Heidi W. Ashih, Gregory P. Samsa, Pamela W. Duncan, Sue Min Lai, David B. Matchar
Johns Hopkins University, Dept. of Biostatistics Working Papers
It is common to assess disability of stroke patients using standardized scales, such as the Rankin Stroke Outcome Scale (RS) and the Barthel Index (BI). The Rankin Scale, which was designed for applications to stroke, is based on assessing directly the global conditions of a patient. The Barthel Index, which was designed for general applications, is based on a series of questions about the patient’s ability to carry out 10 basis activities of daily living. As both scales are commonly used, but few studies use both, translating between scales is important in gaining an overall understanding of the efficacy of …