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

Alu retroelements propagate via retrotransposition by hijacking long interspersed nuclear element-1 (L1) reverse transcriptase (RT) and endonuclease activities. Reverse transcription of Alu RNA into complementary DNA (cDNA) is presumed to occur exclusively in the nucleus at the genomic integration site. Whether Alu cDNA is synthesized independently of genomic integration is unknown. Alu RNA promotes retinal pigmented epithelium (RPE) death in geographic atrophy, an untreatable type of age-related macular degeneration. We report that Alu RNA-induced RPE degeneration is mediated via cytoplasmic L1-reverse-transcribed Alu cDNA independently of retrotransposition. Alu RNA did not induce cDNA production or RPE degeneration in L1-inhibited animals or human cells. Alu reverse transcription can be initiated in the cytoplasm via self-priming of Alu RNA. In four health insurance databases, use of nucleoside RT inhibitors was associated with reduced risk of developing atrophic macular degeneration (pooled adjusted hazard ratio, 0.616; 95% confidence interval, 0.493-0.770), thus identifying inhibitors of this Alu replication cycle shunt as potential therapies for a major cause of blindness.

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. A direct approach utilizes a bivariate negative binomial probability mass function developed in Famoye (2010, Journal of Applied Statistics). The second approach fits bivariate count data indirectly using a bivariate Poisson-gamma mixture model. The third approach is a bivariate Gaussian copula model. Based on the results from simulation analyses, the indirect and copula approaches perform better overall than the direct approach in terms of model fitting and identifying covariate-dependent association. The proposed approaches are applied to two RNA-sequencing data sets for studying breast cancer and melanoma (BRCA-US and SKCM-US), respectively, obtained through the International Cancer Genome Consortium.

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. The proposed model is broadly illustrated via simulations and an analysis of scaphoid fracture data from a prospective study. In addition, omnibus tests constructed from individual tests in parallel and serial are considered.

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. The joint model is completed by considering a parametric copula function. A general misclassification model is discussed in detail, considering the possibility that different examiners were involved in the assessment of the occurrence of the events for a given subject across time. We provide empirical evidence that the model can be used to estimate the underlying time-to-event distribution and the misclassification parameters without any external information about the latter parameters. We also illustrate the effect on the statistical inferences of neglecting the presence of misclassification.

Bayesian regression for group testing data.

Group testing involves pooling individual specimens (e.g., blood, urine, swabs, etc.) and testing the pools for the presence of a disease. When individual covariate information is available (e.g., age, gender, number of sexual partners, etc.), a common goal is to relate an individual's true disease status to the covariates in a regression model. Estimating this relationship is a nonstandard problem in group testing because true individual statuses are not observed and all testing responses (on pools and on individuals) are subject to misclassification arising from assay error. Previous regression methods for group testing data can be inefficient because they are restricted to using only initial pool responses and/or they make potentially unrealistic assumptions regarding the assay accuracy probabilities. To overcome these limitations, we propose a general Bayesian regression framework for modeling group testing data. The novelty of our approach is that it can be easily implemented with data from any group testing protocol. Furthermore, our approach will simultaneously estimate assay accuracy probabilities (along with the covariate effects) and can even be applied in screening situations where multiple assays are used. We apply our methods to group testing data collected in Iowa as part of statewide screening efforts for chlamydia, and we make user-friendly R code available to practitioners.

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. To compensate for missing data on disease status, covariate information is used to discriminate between diseased and healthy units. We thus model the probability of disease as a function of 'disease covariates'. In addition, we model test/biomarker outcome data to depend on 'test covariates', which provides researchers the opportunity to quantify the impact of covariates on the accuracy of a medical test. We further model the distributions of test outcomes using flexible semiparametric classes. An important new theoretical result demonstrating model identifiability under mild conditions is presented. The modeling framework can be used to obtain inferences about covariate-specific test accuracy and the probability of disease based on subject-specific disease and test covariate information. The value of the model is illustrated using multiple simulation studies and data on the age-adjusted ability of soluble epidermal growth factor receptor - a ubiquitous serum protein - to serve as a biomarker of lung cancer in men. SAS code for fitting the model is provided. Copyright © 2015 John Wiley & Sons, Ltd.

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. CONCLUSIONS: Sample size requirements for establishing test-retest standards are generally defined and suitable for any serial monitoring 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. We illustrate that risk inference is not robust to departures from the parametric logistic distribution. Moreover, the model assumption of proportional odds is generally not satisfied when the condition of a logistic distribution for the data is violated, leading to biased inference from a parametric logistic analysis. We develop novel Bayesian semiparametric methodology for testing goodness of fit of parametric logistic regression with continuous measurement data. The testing procedures hold for any cutoff threshold and our approach simultaneously provides the ability to perform semiparametric risk estimation. Bayes factors are calculated using the Savage-Dickey ratio for testing the null hypothesis of logistic regression versus a semiparametric generalization. We propose a fully Bayesian and a computationally efficient empirical Bayesian approach to testing, and we present methods for semiparametric estimation of risks, relative risks, and odds ratios when parametric logistic regression fails. Theoretical results establish the consistency of the empirical Bayes test. Results from simulated data show that the proposed approach provides accurate inference irrespective of whether parametric assumptions hold or not. Evaluation of risk factors for obesity shows that different inferences are derived from an analysis of a real data set when deviations from a logistic distribution are permissible in a flexible semiparametric framework.

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. In an initial "warming-up" phase, the support points are iteratively relocated to regions of higher support under the target distribution to minimize the distance between the target distribution and the Polya tree predictive distribution. In the "sampling" phase, samples from the final approximating mixture of finite Polya trees are used as candidates which are accepted with a standard Metropolis-Hastings acceptance probability. Several illustrations are presented, including comparisons of the proposed approach to Metropolis-within-Gibbs and delayed rejection adaptive Metropolis algorithm.

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. PATIENTS AND METHODS: We used data from two studies, one that prospectively studied 34 patients who had MRI and CT of the wrist, and a second that studied 78 patients who had MRI, bone scintigraphy, and structured physical examination. We compared the diagnostic performance characteristics calculated by latent class analysis with those calculated using formulas based on a reference standard. RESULTS: In the first cohort, the calculated sensitivity and specificity with latent class analysis were different than those with traditional reference standard-based calculations for the CT in the scaphoid planes (sensitivity, 0.78 versus 0.67; specificity, 1.0 versus 0.96) and the MRI (sensitivity, 0.80 versus 0.67; specificity, 0.93 versus 0.89). In the second cohort, the greatest differences were in the sensitivity of MRI (0.84 versus 0.75) and the sensitivities of physical examination maneuvers (range, 0.63-0.73 versus 1.0). CONCLUSIONS: The diagnostic performance characteristics calculated using latent class analysis may differ from those calculated according to formulas based on a reference standard. We believe latent class analysis merits further study as an option for assessing diagnostic performance characteristics for orthopaedic conditions when there is no consensus reference standard. LEVEL OF EVIDENCE: Level II, prognostic study. See the Guidelines for Authors for a complete description of levels of evidence.

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. This paper provides an introduction to a simple, yet comprehensive, set of programs for the implementation of some Bayesian non- and semi-parametric models in R, DPpackage. Currently DPpackage includes models for marginal and conditional density estimation, ROC curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered data using generalized linear mixed models, and regression data using generalized additive models. The package also contains functions to compute pseudo-Bayes factors for model comparison, and for eliciting the precision parameter of the Dirichlet process prior. To maximize computational efficiency, the actual sampling for each model is carried out using compiled FORTRAN.

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. The model is illustrated using mid- (1.0-2.0 kHz) and high- (3.0-6.0 kHz) frequency narrow band noises localization collected among ten normal hearing listeners.

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. Thanks to modern Markov chain Monte Carlo (MCMC) methods, this approach remains computationally feasible in a fully hierarchical Bayesian framework. We compare the spatially dependent mixture of Polya trees (MPT) approach to the traditional spatial frailty approach, and illustrate the usefulness of this method with an analysis of Iowan breast cancer survival data from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute. Our method provides better goodness of fit over the traditional alternatives as measured by log pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and full sample score (FSS) statistics.

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. We consider parametric and nonparametric versions of these two models, as well as a proportional odds rate model paired with a wide variety of longitudinal trajectory assumptions reflecting the types of analyses seen in the literature. In addition to developing novel nonparametric Bayesian methods for joint models, we emphasize the importance of model selection from among joint and non joint models. The default in the literature is to omit at the outset non joint models from consideration. For the medfly data, a predictive diagnostic criterion suggests that both the choice of survival model and longitudinal assumptions can grossly affect model adequacy and prediction. Specifically for these data, the simple joint model used in by Tseng et al. (Biometrika 92:587-603, 2005) and models with much more flexibility in their longitudinal components are predictively outperformed by simpler analyses. This case study underscores the need for data analysts to compare on the basis of predictive performance different joint models and to include non joint models in the pool of candidates under consideration.

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 temporal effect is introduced by considering the stratification of the proportional hazards model, where the time-dependent hazards are indirectly modeled using a probability model for related probability distributions. With this aim, an autoregressive dependent tailfree process is introduced. The full Kullback-Leibler support of the proposed process is provided. The approach is illustrated using simulated and data from the Surveillance Epidemiology and End Results database of the National Cancer Institute on patients in Iowa diagnosed with breast cancer.

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. The present study was conducted to determine the influence of maternal factors on abortion and to predict the probability of abortion as well as the effect of these factors on the fetal lifetime in Holstein dairy cattle. Data from 44,629 established pregnancies that included 14,226 heifers and 30,403 pregnancies from 12,265 parous cows in nine industrial dairy herds around Tehran were used. Overall, 4871 pregnancies of parous cows resulted in abortion. Prediction of the probability of abortion (PPA) was estimated by a logistic regression model. Survival analysis was performed using an accelerated failure time (AFT) model assuming a multi-modal hazard function. Effective factors included age of dam at conception, gravidity, open days, number of previous abortion(s), abortion before/after 60 days of gestation in previous conception, herd and season of insemination. The PPA decreased with increasing open days, increasing gravidity and no previous abortion. In addition, the PPA was greater for cows which had been inseminated during summer versus winter. However, the difference between autumn and spring was not significant. Overall, 25 sires out of 695 from which sperm was collected for artificial insemination (AI) had significantly higher risk of abortion, with odds ratios ranging between 1.44 and 4.73 compared to the average. The survival probability increased slightly during gestation as gravidity increased for cows that had a previous abortion. Cows that had aborted before 60 days of gestation in previous conception tended to abort later in their next conceptions.

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. It is commonly believed that if the degrees of freedom in the data meet or exceed the number of parameters in a fitted model then the model is identifiable. Goodman (1974, Biometrika 61, 215-231) established that this was not the case a long time ago. We discuss currently available models for multiple tests and argue in favor of an extension of a model that was developed by Dendukuri and Joseph (2001, Biometrics 57, 158-167). Subsequently, we further develop Goodman's technique, and make geometric arguments to give further insight into the nature of models that lack identifiability. We present illustrations using simulated and real data.

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. We illustrate our proposed methods with an analysis of spatially oriented breast cancer survival data from the Surveillance, Epidemiology and End Results program of the National Cancer Institute. Our results indicate appreciable advantages for our approach over competing methods that impose unrealistic parametric assumptions, ignore spatial association or both.

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. The objective of this study was to determine whether cultivation media select preferentially for specific strains of Salmonella in heterogeneous cultures. In this study, four different Salmonella strains (one Salmonella Newport, two Salmonella Typhimurium, and one Salmonella Enteritidis) were competed in a broth-based experiment and a bovine fecal experiment with varied combinations and concentrations of each strain. In all experiments, the strain of Salmonella Newport was the most competitive, regardless of the starting concentration and cultivation protocol. One strain of Salmonella Typhimurium was rarely detected in competition, even when it was the only strain present in bovine feces. Overall, the probability of detecting a specific Salmonella strain had little to do with its starting concentration in the sample. The bias introduced by culture could be dramatically biasing Salmonella surveillance systems and hindering traceback investigations during Salmonella outbreaks. Future studies should focus on the microbiological explanations for this Salmonella interstrain variability, approaches for minimizing the bias, and estimations of the public health significance of this bias.

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. Our second goal is to (ii) characterize measures of diagnostic performance of continuous tests by estimating receiver-operating characteristic curves and area under the curve, primarily when such extra information is available. When true disease status is unknown, parametric and nonparametric analyses require sufficient separation between the distributions of outcome values for the diseased and nondiseased populations. However, this overlap becomes less problematic when additional information in the form of either an informative 'prior' that is based on real (preferably data-based) scientific input, or when additional information, or both, are available. The additional information can be used to distinguish 'diseased' from 'nondiseased' individuals. We present an example using simulated data that illustrates this point. We also present an example involving data from an animal-health survey for Johne's disease, where the performance of a serum ELISA is evaluated using additional information obtained from fecal culture. Issues related to identifiability and partial identifiability are also discussed.