Cytoplasmic synthesis of endogenous complementary DNA via reverse transcription and implications in age-related macular degeneration.

retroelements propagate via retrotransposition by hijacking long interspersed nuclear element-1 (L1) reverse transcriptase (RT) and endonuclease activities. Reverse transcription of RNA into complementary DNA (cDNA) is presumed to occur exclusively in the nucleus at the genomic integration site. Whether cDNA is synthesized independently of genomic integration is unknown.

Flexible bivariate correlated count data regression.

Multivariate count data are common in many disciplines. The variables in such data often exhibit complex positive or negative dependency structures. We propose three Bayesian approaches to modeling bivariate count data by simultaneously considering covariate-dependent means and correlation.

Estimation of sensitivity and specificity of multiple repeated binary tests without a gold standard.

A model for multiple diagnostic tests, applied repeatedly over time on each subject, is proposed; gold standard data are not required. The model is identifiable with as few as three tests, and correlation among tests at each time point in the diseased and nondiseased populations, as well as across time points, is explicitly included. An efficient Markov chain Monte Carlo scheme allows for straightforward posterior inference; sample R code is available in the Supporting Web Materials for this paper.

Marginal Bayesian Semiparametric Modeling of Mismeasured Multivariate Interval-Censored Data.

Motivated by data gathered in an oral health study, we propose a Bayesian nonparametric approach for population-averaged modeling of correlated time-to-event data, when the responses can only be determined to lie in an interval obtained from a sequence of examination times and the determination of the occurrence of the event is subject to misclassification. The joint model for the true, unobserved time-to-event data is defined semiparametrically; proportional hazards, proportional odds, and accelerated failure time (proportional quantiles) are all fit and compared. The baseline distribution is modeled as a flexible tailfree prior.

Bayesian regression for group testing data.

Group testing involves pooling individual specimens (e.g., blood, urine, swabs, etc.

Flexible regression models for ROC and risk analysis, with or without a gold standard.

A novel semiparametric regression model is developed for evaluating the covariate-specific accuracy of a continuous medical test or biomarker. Ideally, studies designed to estimate or compare medical test accuracy will use a separate, flawless gold-standard procedure to determine the true disease status of sampled individuals. We treat this as a special case of the more complicated and increasingly common scenario in which disease status is unknown because a gold-standard procedure does not exist or is too costly or invasive for widespread use.

Sample size requirements for establishing clinical test-retest standards.

Objective: To define sample size requirements for establishing clinical serial monitoring protocols.

Design: The 95% confidence bound of a critical difference score is defined and used to identify false-negative regions suitable for sample size calculation.

Results: Reference subject sample sizes vary from about 40 to 480 subjects, depending on the minimum acceptable error rates of the clinical protocol.

A Bayesian goodness of fit test and semiparametric generalization of logistic regression with measurement data.

Logistic regression is a popular tool for risk analysis in medical and population health science. With continuous response data, it is common to create a dichotomous outcome for logistic regression analysis by specifying a threshold for positivity. Fitting a linear regression to the nondichotomized response variable assuming a logistic sampling model for the data has been empirically shown to yield more efficient estimates of odds ratios than ordinary logistic regression of the dichotomized endpoint.

A Bayesian Semiparametric Temporally-Stratified Proportional Hazards Model with Spatial Frailties.

Incorporating temporal and spatial variation could potentially enhance information gathered from survival data. This paper proposes a Bayesian semiparametric model for capturing spatio-temporal heterogeneity within the proportional hazards framework. The spatial correlation is introduced in the form of county-level frailties.

The Polya Tree Sampler: Towards Efficient and Automatic Independent Metropolis-Hastings Proposals.

We present a simple, efficient, and computationally cheap sampling method for exploring an un-normalized multivariate density on ℝ(d), such as a posterior density, called the Polya tree sampler. The algorithm constructs an independent proposal based on an approximation of the target density. The approximation is built from a set of (initial) support points - data that act as parameters for the approximation - and the predictive density of a finite multivariate Polya tree.

Diagnostic performance tests for suspected scaphoid fractures differ with conventional and latent class analysis.

Background: Evaluation of the diagnostic performance characteristics of radiographic tests for diagnosing a true fracture among suspected scaphoid fractures is hindered by the lack of a consensus reference standard. Latent class analysis is a statistical method that takes advantage of unobserved, or latent, classes in the data that can be used to determine diagnostic performance characteristics when there is no consensus reference (gold) standard.

Purposes: We therefore compared the diagnostic performance characteristics of MRI, CT, bone scintigraphy, and physical examination to identify true fractures among suspected scaphoid fractures.

DPpackage: Bayesian Non- and Semi-parametric Modelling in R.

Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on a function space, such as the space of all probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex and hence sampling methods play a key role.

A statistical model of horizontal auditory localization performance data.

Horizontal localization experiments are used to evaluate the listener's ability to locate the position of a sound source, and determine how signal characteristics affect this ability. These experiments generate circular, bimodal, and repeated data that are challenging to statistically analyze. A two-part mixture of wrapped Cauchys is proposed for these data, with the effects of signal type and position on localization bias, precision, and front-back confusion modeled using regression.

Spatially dependent polya tree modeling for survival data.

With the proliferation of spatially oriented time-to-event data, spatial modeling in the survival context has received increased recent attention. A traditional way to capture a spatial pattern is to introduce frailty terms in the linear predictor of a semiparametric model, such as proportional hazards or accelerated failure time. We propose a new methodology to capture the spatial pattern by assuming a prior based on a mixture of spatially dependent Polya trees for the baseline survival in the proportional hazards model.

Risk factors for abortion in dairy cows from commercial Holstein dairy herds in the Tehran region.

In last decade, pregnancy loss in dairy cattle has had an upward trend bringing difficulties for breeders: the annual cost is estimated around 396 billion Rials (i.e. around 40 million US$) for the Iranian dairy industry.

Predictive comparison of joint longitudinal-survival modeling: a case study illustrating competing approaches.

The joint modeling of longitudinal and survival data has received extraordinary attention in the statistics literature recently, with models and methods becoming increasingly more complex. Most of these approaches pair a proportional hazards survival with longitudinal trajectory modeling through parametric or nonparametric specifications. In this paper we closely examine one data set previously analyzed using a two parameter parametric model for Mediterranean fruit fly (medfly) egg-laying trajectories paired with accelerated failure time and proportional hazards survival models.

Mixtures of Polya trees for flexible spatial frailty survival modelling.

Mixtures of Polya trees offer a very flexible nonparametric approach for modelling time-to-event data. Many such settings also feature spatial association that requires further sophistication, either at the point level or at the lattice level. In this paper, we combine these two aspects within three competing survival models, obtaining a data analytic approach that remains computationally feasible in a fully hierarchical Bayesian framework using Markov chain Monte Carlo methods.

Identifiability of models for multiple diagnostic testing in the absence of a gold standard.

We discuss the issue of identifiability of models for multiple dichotomous diagnostic tests in the absence of a gold standard (GS) test. Data arise as multinomial or product-multinomial counts depending upon the number of populations sampled. Models are generally posited in terms of population prevalences, test sensitivities and specificities, and test dependence terms.

Do microbial interactions and cultivation media decrease the accuracy of Salmonella surveillance systems and outbreak investigations?

Cultivation methods are commonly used in Salmonella surveillance systems and outbreak investigations, and consequently, conclusions about Salmonella evolution and transmission are highly dependent on the performance characteristics of these methods. Past studies have shown that Salmonella serotypes can exhibit different growth characteristics in the same enrichment and selective media. This could lead not only to biased conclusions about the dominant strain present in a sample with mixed Salmonella populations, but also to a low sensitivity for detecting a Salmonella strain in a sample with only a single strain present.

Chemopreventive effect of kava on 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone plus benzo[a]pyrene-induced lung tumorigenesis in A/J mice.

Lung cancer is the leading cause of cancer death, and chemoprevention is a potential strategy to help control this disease. Epidemiologic survey indicates that kava may be chemopreventive for lung cancer, but there is a concern about its potential hepatotoxicity. In this study, we evaluated whether oral kava could prevent 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) plus benzo[a]pyrene (B[a]P)-induced lung tumorigenesis in A/J mice.

Bayesian semiparametric ROC curve estimation and disease diagnosis.

We develop a novel semiparametric modeling framework involving mixtures of Polya trees for screening data with the dual purpose of diagnosing infection or disease status and of assessing the accuracy of continuous diagnostic measures. In this framework, we obtain (i) predictive probabilities of 'disease' based on continuous diagnostic test outcomes in conjunction with other information, including relevant covariates and results from one or more independent binary diagnostic tests. An example would be the modeling of a serum enzyme-linked immunosorbent assay (ELISA) procedure for detecting antibodies to an infectious agent when used in conjunction with culture for antigen detection.

Bayesian nonparametric meta-analysis using Polya tree mixture models.

Summary. A common goal in meta-analysis is estimation of a single effect measure using data from several studies that are each designed to address the same scientific inquiry. Because studies are typically conducted in geographically disperse locations, recent developments in the statistical analysis of meta-analytic data involve the use of random effects models that account for study-to-study variability attributable to differences in environments, demographics, genetics, and other sources that lead to heterogeneity in populations.

Geographic variability in alcohol-related crashes in response to legalized Sunday packaged alcohol sales in New Mexico.

On July 1, 1995 the state of New Mexico lifted its ban on Sunday packaged alcohol sales. Legislation lifting the ban included a local option allowing individual communities within the state to hold an election to reinstitute the ban on Sunday packaged alcohol sales. Previous research has shown a clear statewide increase in alcohol-related crash and crash fatality rates after the ban was lifted.

Bayesian inference for prevalence and diagnostic test accuracy based on dual-pooled screening.

We propose a useful protocol for the problem of screening populations for low-prevalence characteristics such as HIV or drugs. Current HIV screening of blood that has been donated for transfusion involves the testing of individual blood units with an inexpensive enzyme-linked immunosorbent assay test and follow-up with a more accurate and more expensive western blot test for only those units that tested positive. Our cost-effective pooling strategy would enhance current methods by making it possible to accurately estimate the sensitivity and specificity of the initial screening test, and the proportion of defective units that have passed through the system.

Comparison of direct immunofluorescence, modified acid-fast staining, and enzyme immunoassay techniques for detection of Cryptosporidium spp in naturally exposed kittens.

Objective: To evaluate a modified Ziehl-Neelsen acid-fast staining technique (mZN), a direct immunofluorescence detection procedure (DIF), and 3 commercial enzyme immunoassays (EIAs) for detection of Cryptosporidium oocysts in fecal specimens from kittens.

Design: Prospective study.

Sample Population: 416 fecal specimens collected from 104 randomly selected domestic shorthair kittens (8 to 16 weeks of age) that were naturally exposed to Cryptosporidium spp.