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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]

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]

When modeling longitudinal data, the true values of time-varying covariates may be unknown because of detection-limit censoring or measurement error. A common approach in the literature is to empirically model the covariate process based on observed data and then predict the censored values or mismeasured values based on this empirical model. Such an empirical model can be misleading, especially for censored values since the (unobserved) censored values may behave very differently than observed values due to the underlying data-generation mechanisms or disease status. In this paper, we propose a mechanistic nonlinear covariate model based on the underlying data-generation mechanisms to address censored values and mismeasured values. Such a mechanistic model is based on solid scientific or biological arguments, so the predicted censored or mismeasured values are more reasonable. We use a Monte Carlo EM algorithm for likelihood inference and apply the methods to an AIDS dataset, where viral load is censored by a lower detection limit. Simulation results confirm that the proposed models and methods offer substantial advantages over existing empirical covariate models for censored and mismeasured covariates. [ABSTRACT FROM AUTHOR]

Traditional Chinese medicine (TCM) is a very complex mixture containing many different ingredients. Thus, statistical analysis of traditional Chinese medicine data becomes challenging, as one needs to handle the association among the observed data across different time points and across different ingredients of the multivariate response. This paper builds a 3-stage Bayesian hierarchical model for analyzing multivariate response pharmacokinetic data. Usually, the dimensionality of the parameter space is very huge, which leads to the parameter-estimation difficulty. So we take the hybrid Markov chain Monte Carlo algorithms to obtain the posterior Bayesian estimation of corresponding parameters in our model. Both simulation study and real-data analysis show that our theoretical model and Markov chain Monte Carlo algorithms perform well, and especially the correlation among different ingredients can be calculated very accurately. [ABSTRACT FROM AUTHOR]

Interval-censored data, in which the event time is only known to lie in some time interval, arise commonly in practice, for example, in a medical study in which patients visit clinics or hospitals at prescheduled times and the events of interest occur between visits. Such data are appropriately analyzed using methods that account for this uncertainty in event time measurement. In this paper, we propose a survival tree method for interval-censored data based on the conditional inference framework. Using Monte Carlo simulations, we find that the tree is effective in uncovering underlying tree structure, performs similarly to an interval-censored Cox proportional hazards model fit when the true relationship is linear, and performs at least as well as (and in the presence of right-censoring outperforms) the Cox model when the true relationship is not linear. Further, the interval-censored tree outperforms survival trees based on imputing the event time as an endpoint or the midpoint of the censoring interval. We illustrate the application of the method on tooth emergence data. [ABSTRACT FROM AUTHOR]

Identification of treatment selection biomarkers has become very important in cancer drug development. Adaptive enrichment designs have been developed for situations where a unique treatment selection biomarker is not apparent based on the mechanism of action of the drug. With such designs, the eligibility rules may be adaptively modified at interim analysis times to exclude patients who are unlikely to benefit from the test treatment.We consider a recently proposed, particularly flexible approach that permits development of model-based multifeature predictive classifiers as well as optimized cut-points for continuous biomarkers. A single significance test, including all randomized patients, is performed at the end of the trial of the strong null hypothesis that the expected outcome on the test treatment is no better than control for any of the subset populations of patients accrued in the K stages of the clinical trial. In this paper, we address 2 issues involving inference following an adaptive enrichment design as described above. The first is specification of the intended use population and estimation of treatment effect for that population following rejection of the strong null hypothesis. The second issue is defining conditions in which rejection of the strong null hypothesis implies rejection of the null hypothesis for the intended use population. [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]

This paper develops a model for cancer screening and cancer incidence data, accommodating the partially unobserved disease status, clustered data structures, general covariate effects, and dependence between exams. The true unobserved cancer and detection status of screening participants are treated as latent variables, and a Markov Chain Monte Carlo algorithm is used to estimate the Bayesian posterior distributions of the diagnostic error rates and disease prevalence. We show how the Bayesian approach can be used to draw inferences about screening exam properties and disease prevalence while allowing for the possibility of conditional dependence between two exams. The techniques are applied to the estimation of the diagnostic accuracy of mammography and clinical breast examination using data from the Ontario Breast Screening Program in Canada. [ABSTRACT FROM AUTHOR]