A Bayesian nonparametric meta-analysis model for estimating the reference interval.

A reference interval represents the normative range for measurements from a healthy population. It plays an important role in laboratory testing, as well as in differentiating healthy from diseased patients. The reference interval based on a single study might not be applicable to a broader population. Meta-analysis can provide a more generalizable reference interval based on the combined population by synthesizing results from multiple studies. However, the assumptions of normally distributed underlying study-specific means and equal within-study variances, which are commonly used in existing methods, are strong and may not hold in practice. We propose a Bayesian nonparametric model with more flexible assumptions to extend random effects meta-analysis for estimating reference intervals. We illustrate through simulation studies and two real data examples the performance of our proposed approach when the assumptions of normally distributed study means and equal within-study variances do not hold.

A modular chemigenetic calcium indicator enables in vivo functional imaging with near-infrared light.

Genetically encoded fluorescent calcium indicators have revolutionized neuroscience and other biological fields by allowing cellular-resolution recording of physiology during behavior. However, we currently lack bright, genetically targetable indicators in the near infrared that can be used in animals. Here, we describe WHaloCaMP, a modular chemigenetic calcium indicator built from bright dye-ligands and protein sensor domains that can be genetically targeted to specific cell populations. Fluorescence change in WHaloCaMP results from reversible quenching of the bound dye via a strategically placed tryptophan. WHaloCaMP is compatible with rhodamine dye-ligands that fluoresce from green to near-infrared, including several dye-ligands that efficiently label the central nervous system in animals. When bound to a near-infrared dye-ligand, WHaloCaMP1a is more than twice as bright as jGCaMP8s, and shows a 7× increase in fluorescence intensity and a 2.1 ns increase in fluorescence lifetime upon calcium binding. We use WHaloCaMP1a with near-infrared fluorescence emission to image Ca2+ responses in flies and mice, to perform three-color multiplexed functional imaging of hundreds of neurons and astrocytes in zebrafish larvae, and to quantitate calcium concentration using fluorescence lifetime imaging microscopy (FLIM).

Glutamate indicators with improved activation kinetics and localization for imaging synaptic transmission.

The fluorescent glutamate indicator iGluSnFR enables imaging of neurotransmission with genetic and molecular specificity. However, existing iGluSnFR variants exhibit low in vivo signal-to-noise ratios, saturating activation kinetics and exclusion from postsynaptic densities. Using a multiassay screen in bacteria, soluble protein and cultured neurons, we generated variants with improved signal-to-noise ratios and kinetics. We developed surface display constructs that improve iGluSnFR's nanoscopic localization to postsynapses. The resulting indicator iGluSnFR3 exhibits rapid nonsaturating activation kinetics and reports synaptic glutamate release with decreased saturation and increased specificity versus extrasynaptic signals in cultured neurons. Simultaneous imaging and electrophysiology at individual boutons in mouse visual cortex showed that iGluSnFR3 transients report single action potentials with high specificity. In vibrissal sensory cortex layer 4, we used iGluSnFR3 to characterize distinct patterns of touch-evoked feedforward input from thalamocortical boutons and both feedforward and recurrent input onto L4 cortical neuron dendritic spines.

Multimodality During Fixation - Part II: Evidence for Multimodality in Spatial Precision-Related Distributions and Impact on Precision Estimates.

This paper is a follow-on to our earlier paper (7), which focused on the multimodality of angular offsets. This paper applies the same analysis to the measurement of spatial precision. Following the literature, we refer these measurements as estimates of device precision, but, in fact, subject characteristics clearly affect the measurements. One typical measure of the spatial precision of an eye-tracking device is the standard deviation (SD) of the position signals (horizontal and vertical) during a fixation. The SD is a highly interpretable measure of spread if the underlying error distribution is unimodal and normal. However, in the context of an underlying multimodal distribution, the SD is less interpretable. We will present evidence that the majority of such distributions are multimodal (68-70% strongly multimodal). Only 21-23% of position distributions were unimodal. We present an alternative method for measuring precision that is appropriate for both unimodal and multimodal distributions. This alternative method produces precision estimates that are substantially smaller than classic measures. We present illustrations of both unimodality and multimodality with either drift or a microsaccade present during fixation. At present, these observations apply only to the EyeLink 1000, and the subjects evaluated herein.

Angular Offset Distributions During Fixation Are, More Often Than Not, Multimodal.

Typically, the position error of an eye-tracking device is measured as the distance of the eye-position from the target position in two-dimensional space (angular offset). Accuracy is the mean angular offset. The mean is a highly interpretable measure of central tendency if the underlying error distribution is unimodal and normal. However, in the context of an underlying multimodal distribution, the mean is less interpretable. We will present evidence that the majority of such distributions are multimodal. Only 14.7% of fixation angular offset distributions were unimodal, and of these, only 11.5% were normally distributed. (Of the entire dataset, 1.7% were unimodal and normal.) This multimodality is true even if there is only a single, continuous tracking fixation segment per trial. We present several approaches to measure accuracy in the face of multimodality. We also address the role of fixation drift in partially explaining multimodality.

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.

A Comparison of 6 Diagnostic Tests for Carpal Tunnel Syndrome Using Latent Class Analysis.

Background: Nerve conduction studies (NCS), ultrasonography (US), Carpal Tunnel Syndrome 6 (CTS-6), Wainner, Lo, and Kamath are clinical diagnostic tools that can be used to diagnose carpal tunnel syndrome (CTS). Latent class analysis (LCA) is a proven statistical technique that can be used to evaluate diagnostic tests in a lack of a reference standard. Given that there is no accepted reference standard, we elected to perform an LCA to evaluate the 6 clinical diagnostic tests. Methods: One hundred eighty-seven wrists were prospectively evaluated by a board-certified hand surgeon using US, the CTS-6, Wainner, Lo, and Kamath. The NCS were performed by an electrophysiologist according to the standards of the American Association of Neuromuscular & Electrodiagnostic Medicine. The LCA was performed to evaluate individual performance and pairwise combinations of the tests. Results: The NCS demonstrated the highest estimated sensitivity of 97%, and the Wainner had the highest estimated specificity of 97%. Alternatively, the Lo had the lowest estimated sensitivity (36%), and NCS had the lowest estimated specificity (40%). When evaluating pairwise combinations, positive US and NCS demonstrated the highest overall sensitivity at 86%, and negative US and NCS had a specificity of 83%. Conclusions: There is no perfect clinical diagnostic test, with the 6 clinical diagnostic tests having differing sensitivities and specificities. Pairwise combinations of the test can be used to complement one another.

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.

Bayes factors for choosing among six common survival models.

A super model that includes proportional hazards, proportional odds, accelerated failure time, accelerated hazards, and extended hazards models, as well as the model proposed in Diao et al. (Biometrics 69(4):840-849, 2013) accounting for crossed survival as special cases is proposed for the purpose of testing and choosing among these popular semiparametric models. Efficient methods for fitting and computing fast, approximate Bayes factors are developed using a nonparametric baseline survival function based on a transformed Bernstein polynomial. All manner of censoring is accommodated including right, left, and interval censoring, as well as data that are observed exactly and mixtures of all of these; current status data are included as a special case. The method is tested on simulated data and two real data examples. The approach is easily carried out via a new function in the spBayesSurv R package.

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 parametrization of variance functions for quantal response data derived from counts.

Although the Poisson model has been widely used to fit count data, a well-known drawback is that the Poisson mean equals its variance. Many alternative models for counts that are overdispersed relative to Poisson have been developed to solve this issue, including the negative binomial model. In this article, the negative binomial model with a four-parameter logistic mean is proposed to handle these types of counts, with variance that flexibly depends on the mean. Various parameterizations for the variance are considered, including extra-Poisson variability modeled as an exponentiated B-spline. Thus, the proposed model ably captures the leveling off of the mean, i.e., the "lazy-S" shape often encountered for overdispersed dose-response counts, simultaneously taking into account both overdispersion and natural mortality. Two real datasets illustrate the merits of the proposed approach: media colony counts after tuberculosis decontamination, and the number of monkeys killed by Ache hunters over several hunting trips in the Paraguayan tropical forest.

Generalized accelerated failure time spatial frailty model for arbitrarily censored data.

Flexible incorporation of both geographical patterning and risk effects in cancer survival models is becoming increasingly important, due in part to the recent availability of large cancer registries. Most spatial survival models stochastically order survival curves from different subpopulations. However, it is common for survival curves from two subpopulations to cross in epidemiological cancer studies and thus interpretable standard survival models can not be used without some modification. Common fixes are the inclusion of time-varying regression effects in the proportional hazards model or fully nonparametric modeling, either of which destroys any easy interpretability from the fitted model. To address this issue, we develop a generalized accelerated failure time model which allows stratification on continuous or categorical covariates, as well as providing per-variable tests for whether stratification is necessary via novel approximate Bayes factors. The model is interpretable in terms of how median survival changes and is able to capture crossing survival curves in the presence of spatial correlation. A detailed Markov chain Monte Carlo algorithm is presented for posterior inference and a freely available function frailtyGAFT is provided to fit the model in the R package spBayesSurv. We apply our approach to a subset of the prostate cancer data gathered for Louisiana by the surveillance, epidemiology, and end results program of the National Cancer Institute.

Assessment of DPOAE test-retest difference curves via hierarchical Gaussian processes.

Distortion product otoacoustic emissions (DPOAE) testing is a promising alternative to behavioral hearing tests and auditory brainstem response testing of pediatric cancer patients. The central goal of this study is to assess whether significant changes in the DPOAE frequency/emissions curve (DP-gram) occur in pediatric patients in a test-retest scenario. This is accomplished through the construction of normal reference charts, or credible regions, that DP-gram differences lie in, as well as contour probabilities that measure how abnormal (or in a certain sense rare) a test-retest difference is. A challenge is that the data were collected over varying frequencies, at different time points from baseline, and on possibly one or both ears. A hierarchical structural equation Gaussian process model is proposed to handle the different sources of correlation in the emissions measurements, wherein both subject-specific random effects and variance components governing the smoothness and variability of each child's Gaussian process are coupled together.

Association of green tea consumption with mortality from all-cause, cardiovascular disease and cancer in a Chinese cohort of 165,000 adult men.

Tea is the most ancient and popular beverage in the world, and its beneficial health effects has attracted tremendous attention worldwide. However, the prospective evidence relating green tea consumption to total and cause-specific mortality is still limited and inconclusive. We recruited 164,681 male participants free of pre-existing disease during 1990-1991, with green tea consumption and other covariates assessed by the standardized questionnaire and mortality follow up continued until 2006 (mean 11 years; total person-years: 1,961,791). Cox regression analyses were used to quantify the associations of green tea consumption with all-cause (n = 32,700), CVD (n = 11,839) and cancer (n = 7002) mortality, adjusting simultaneously for potential confounders. At baseline, 18 % reported regular consumption of green tea. Compared with non-green tea drinkers, regular drinkers had significantly lower all-cause mortality, with adjusted hazard ratios (HRs) being 0.94 (95 % CI 0.89, 0.99) for ≤5 g/day, 0.95 (0.91, 0.99) for 5-10 g/day and 0.89 (0.85, 0.93) for >10 g/day. For CVD mortality, the corresponding HRs were 0.93 (0.85, 1.01) 0.91 (0.85, 0.98) and 0.86 (0.79, 0.93), respectively, while for cancer they were 0.86 (0.78, 0.98), 0.92 (0.83, 1.00) and 0.79 (0.71, 0.88), respectively. The patterns of these associations varied by smoking, alcohol drinking and locality. This large prospective study shows that regular green tea consumption is associated with significantly reduced risk of death from all-cause, CVD and cancer among Chinese adults.

A Large-Scale Interface for Optogenetic Stimulation and Recording in Nonhuman Primates.

While optogenetics offers great potential for linking brain function and behavior in nonhuman primates, taking full advantage of that potential will require stable access for optical stimulation and concurrent monitoring of neural activity. Here we present a practical, stable interface for stimulation and recording of large-scale cortical circuits. To obtain optogenetic expression across a broad region, here spanning primary somatosensory (S1) and motor (M1) cortices, we used convection-enhanced delivery of the viral vector, with online guidance from MRI. To record neural activity across this region, we used a custom micro-electrocorticographic (µECoG) array designed to minimally attenuate optical stimuli. Lastly, we demonstrated the use of this interface to measure spatiotemporal responses to optical stimulation across M1 and S1. This interface offers a powerful tool for studying circuit dynamics and connectivity across cortical areas, for long-term studies of neuromodulation and targeted cortical plasticity, and for linking these to behavior.

A Comparison of Three Diagnostic Tests for Carpal Tunnel Syndrome Using Latent Class Analysis.

BACKGROUND: The current reference standard for carpal tunnel syndrome is under debate. Recent studies have demonstrated similar diagnostic accuracy between ultrasound and nerve conduction studies. The purpose of the present study was to determine the sensitivity and specificity of ultrasound, nerve conduction studies, and Carpal Tunnel Syndrome 6 (CTS-6) for the diagnosis of carpal tunnel syndrome using latent class analysis. METHODS: Latent class analysis is a statistical technique that can be used to estimate the accuracy of diagnosis when there is no universally accepted reference standard. This type of analysis is useful in the setting of carpal tunnel syndrome as there remains substantial controversy with respect to the necessity of nerve conduction studies and other confirmatory testing. CTS-6 is a validated clinical diagnostic tool for the diagnosis of carpal tunnel syndrome that has been shown to have a high sensitivity and specificity. Data from a database on the cases of eighty-five consecutive patients who had had nerve conduction studies, CTS-6, and ultrasound were analyzed using classical latent class analysis, assuming that the three tests were imperfect and conditionally independent. RESULTS: The sensitivities of ultrasound, CTS-6, and nerve conduction studies were 91% (95% confidence interval [CI], 81% to 98%), 95% (95% CI, 86% to 99%), and 91% (95% CI, 81% to 97%), respectively. The specificities of ultrasound, CTS-6, and nerve conduction studies were 94% (95% CI, 80% to 100%), 91% (95% CI, 74% to 99%), and 83% (95% CI, 66% to 95%), respectively. CONCLUSIONS: Ultrasound, nerve conduction studies, and CTS-6 have similar sensitivity and specificity for the diagnosis of carpal tunnel syndrome. The currently accepted reference standard (nerve conduction studies) had the lowest sensitivity and specificity of the three tests. These findings support previous studies that have suggested that CTS-6 and ultrasound are highly accurate in the diagnosis of carpal tunnel syndrome and that nerve conduction studies are not necessary in most cases.

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.

MODELLING COUNTY LEVEL BREAST CANCER SURVIVAL DATA USING A COVARIATE-ADJUSTED FRAILTY PROPORTIONAL HAZARDS MODEL.

Understanding the factors that explain differences in survival times is an important issue for establishing policies to improve national health systems. Motivated by breast cancer data arising from the Surveillance Epidemiology and End Results program, we propose a covariate-adjusted proportional hazards frailty model for the analysis of clustered right-censored data. Rather than incorporating exchangeable frailties in the linear predictor of commonly-used survival models, we allow the frailty distribution to flexibly change with both continuous and categorical cluster-level covariates and model them using a dependent Bayesian nonparametric model. The resulting process is flexible and easy to fit using an existing R package. The application of the model to our motivating example showed that, contrary to intuition, those diagnosed during a period of time in the 1990s in more rural and less affluent Iowan counties survived breast cancer better. Additional analyses showed the opposite trend for earlier time windows. We conjecture that this anomaly has to be due to increased hormone replacement therapy treatments prescribed to more urban and affluent subpopulations.