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Many disease diagnoses involve subjective judgments by qualified raters. For example, through the inspection of a mammogram, MRI, or ultrasound image, the clinician himself becomes part of the measuring instrument. To reduce diagnostic errors and improve the quality of diagnoses, it is necessary to assess raters' diagnostic skills and to improve their skills over time. This paper focuses on a subjective binary classification process, proposing a hierarchical model linking data on rater opinions with patient true disease-development outcomes. The model allows for the quantification of the effects of rater diagnostic skills (bias and magnifier) and patient latent disease severity on the rating results. A Bayesian Markov chain Monte Carlo (MCMC) algorithm is developed to estimate these parameters. Linking to patient true disease outcomes, the rater-specific sensitivity and specificity can be estimated using MCMC samples. Cost theory is used to identify poor- and strong-performing raters and to guide adjustment of rater bias and diagnostic magnifier to improve the rating performance. Furthermore, diagnostic magnifier is shown as a key parameter to present a rater's diagnostic ability because a rater with a larger diagnostic magnifier has a uniformly better receiver operating characteristic (ROC) curve when varying the value of diagnostic bias. A simulation study is conducted to evaluate the proposed methods, and the methods are illustrated with a mammography example. [ABSTRACT FROM AUTHOR]

This paper develops object-oriented data analysis (OODA) statistical methods that are novel and complementary to existing methods of analysis of human brain scan connectomes, defined as graphs representing brain anatomical or functional connectivity. OODA is an emerging field where classical statistical approaches (e.g., hypothesis testing, regression, estimation, and confidence intervals) are applied to data objects such as graphs or functions. By analyzing data objects directly we avoid loss of information that occurs when data objects are transformed into numerical summary statistics. By providing statistical tools that analyze sets of connectomes without loss of information, new insights into neurology and medicine may be achieved. In this paper we derive the formula for statistical model fitting, regression, and mixture models; test their performance in simulation experiments; and apply them to connectomes from fMRI brain scans collected during a serial reaction time task study. Software for fitting graphical object-oriented data analysis is provided. [ABSTRACT FROM AUTHOR]

The frailty model, an extension of the proportional hazards model, is often used to model clustered survival data. However, some extension of the ordinary frailty model is required when there exist competing risks within a cluster. Under competing risks, the underlying processes affecting the events of interest and competing events could be different but correlated. In this paper, the hierarchical likelihood method is proposed to infer the cause-specific hazard frailty model for clustered competing risks data. The hierarchical likelihood incorporates fixed effects as well as random effects into an extended likelihood function, so that the method does not require intensive numerical methods to find the marginal distribution. Simulation studies are performed to assess the behavior of the estimators for the regression coefficients and the correlation structure among the bivariate frailty distribution for competing events. The proposed method is illustrated with a breast cancer dataset. [ABSTRACT FROM AUTHOR]

We analyse paediatric ophthalmic data from a large sample of children aged between 3 and 8 years. We use a Bayesian additive conditional bivariate copula regression model with sinh-arcsinh marginal densities with location, scale, and shape parameters that depend smoothly on a covariate. We perform Bayesian inference about the unknown quantities of our model using a specially tailored Markov chain Monte Carlo algorithm. We gain new insights about the processes, which determine transformations in visual acuity with respect to age, including the nature of joint changes in both eyes as modelled with the age-related copula dependence parameter. We analyse posterior predictive distributions to identify children with unusual sight characteristics, distinguishing those who are bivariate, but not univariate outliers. In this way, we provide an innovative tool that enables clinicians to identify children with unusual sight who may otherwise be missed. We compare our simultaneous Bayesian method with a two-step frequentist generalised additive modelling approach. [ABSTRACT FROM AUTHOR]

Longitudinal measurement of biomarkers is important in determining risk factors for binary endpoints such as infection or disease. However, biomarkers are subject to measurement error, and some are also subject to left-censoring due to a lower limit of detection. Statistical methods to address these issues are few. We herein propose a generalized linear mixed model and estimate the model parameters using the Monte Carlo Newton-Raphson (MCNR) method. Inferences regarding the parameters are made by applying Louis's method and the delta method. Simulation studies were conducted to compare the proposed MCNR method with existing methods including the maximum likelihood (ML) method and the ad hoc approach of replacing the left-censored values with half of the detection limit (HDL). The results showed that the performance of the MCNR method is superior to ML and HDL with respect to the empirical standard error, as well as the coverage probability for the 95% confidence interval. The HDL method uses an incorrect imputation method, and the computation is constrained by the number of quadrature points; while the ML method also suffers from the constrain for the number of quadrature points, the MCNR method does not have this limitation and approximates the likelihood function better than the other methods. The improvement of the MCNR method is further illustrated with real-world data from a longitudinal study of local cervicovaginal HIV viral load and its effects on oncogenic HPV detection in HIV-positive women. [ABSTRACT FROM AUTHOR]

A normality assumption is typically adopted for the random effects in a clustered or longitudinal data analysis using a linear mixed model. However, such an assumption is not always realistic, and it may lead to potential biases of the estimates, especially when variable selection is taken into account. Furthermore, flexibility of nonparametric assumptions (e.g., Dirichlet process) on these random effects may potentially cause centering problems, leading to difficulty of interpretation of fixed effects and variable selection. Motivated by these problems, we proposed a Bayesian method for fixed and random effects selection in nonparametric random effects models. We modeled the regression coefficients via centered latent variables which are distributed as probit stick-breaking scale mixtures. By using the mixture priors for centered latent variables along with covariance decomposition, we could avoid the aforementioned problems and allow efficient selection of fixed and random effects from the model. We demonstrated the advantages of our proposed approach over other competing alternatives through a simulated example and also via an illustrative application to a data set from a periodontal disease study. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]