Latent class model diagnosis

In many areas of medical research, such as psychiatry and gerontology, latent class variables are used to classify individuals into disease categories, often with the intention of hierarchical modeling. Problems arise when it is not clear how many disease classes are appropriate, creating a need for model selection and diagnostic techniques. Previous work has shown that the Pearson chi 2 statistic and the log-likelihood ratio G2 statistic are not valid test statistics for evaluating latent class models. Other methods, such as information criteria, provide decision rules without providing explicit information about where discrepancies occur between a model and the data. Identifiability issues further complicate these problems. This paper develops procedures for assessing Markov chain Monte Carlo convergence and model diagnosis and for selecting the number of categories for the latent variable based on evidence in the data using Markov chain Monte Carlo techniques. Simulations and a psychiatric example are presented to demonstrate the effective use of these methods.     

Robust Bayesian hierarchical model using normal/independent distributions

The multilevel item response theory (MLIRT) models have been increasingly used in longitudinal clinical studies that collect multiple outcomes. The MLIRT models account for all the information from multiple longitudinal outcomes of mixed types (e.g., continuous, binary, and ordinal) and can provide valid inference for the overall treatment effects. However, the continuous outcomes and the random effects in the MLIRT models are often assumed to be normally distributed. The normality assumption can sometimes be unrealistic and thus may produce misleading results. The normal/independent (NI) distributions have been increasingly used to handle the outlier and heavy tail problems in order to produce robust inference. In this article, we developed a Bayesian approach that implemented the NI distributions on both continuous outcomes and random effects in the MLIRT models and discussed different strategies of implementing the NI distributions. Extensive simulation studies were conducted to demonstrate the advantage of our proposed models, which provided parameter estimates with smaller bias and more reasonable coverage probabilities. Our proposed models were applied to a motivating Parkinson's disease study, the DATATOP study, to investigate the effect of deprenyl in slowing down the disease progression.     

Semiparametric models and inference for biomedical time series with extra-variation

Biomedical trials often give rise to data having the form of time series of a common process on separate individuals. One model which has been proposed to explain variations in such series across individuals is a random effects model based on sample periodograms. The use of spectral coefficients enables models for individual series to be constructed on the basis of standard asymptotic theory, whilst variations between individuals are handled by permitting a random effect perturbation of model coefficients. This paper extends such methodology in two ways: first, by enabling a nonparametric specification of underlying spectral behaviour; second, by addressing some of the tricky computational issues which are encountered when working with this class of random effect models. This leads to a model in which a population spectrum is specified nonparametrically through a dynamic system, and the processes measured on individuals within the population are assumed to have a spectrum which has a random effect perturbation from the population norm. Simulation studies show that standard MCMC algorithms give effective inferences for this model, and applications to biomedical data suggest that the model itself is capable of revealing scientifically important structure in temporal characteristics both within and between individual processes.     

A Model for a Proportional Treatment Effect on Disease Progression

Treatments intended to slow the progression of chronic diseases are often hypothesized to reduce the rate of further injury to a biological system without improving the current level of functioning. In this situation, the treatment effect may be negligible for patients whose disease would have been stable without the treatment but would be expected to be an increasing function of the progression rate in patients with worsening disease. This article considers a variation of the Laird Ware mixed effects model in which the effect of the treatment on the slope of a longitudinal outcome is assumed to be proportional to the progression rate for patients with progressive disease. Inference based on maximum likelihood and a generalized estimating equations procedure is considered. Under the proportional effect assumption, the precision of the estimated treatment effect can be increased by incorporating the functional relationship between the model parameters and the variance of the outcome variable, particularly when the magnitude of the mean slope of the outcome is small compared with the standard deviation of the slopes. An example from a study of chronic renal disease is used to illustrate insights provided by the proportional effect model that may be overlooked with models assuming additive treatment effects.     

Bayesian modeling of incidence and progression of disease from cross-sectional data

In the absence of longitudinal data, the current presence and severity of disease can be measured for a sample of individuals to investigate factors related to disease incidence and progression. In this article, Bayesian discrete-time stochastic models are developed for inference from cross-sectional data consisting of the age at first diagnosis, the current presence of disease, and one or more surrogates of disease severity. Semiparametric models are used for the age-specific hazards of onset and diagnosis, and a normal underlying variable approach is proposed for modeling of changes with latency time in disease severity. The model accommodates multiple surrogates of disease severity having different measurement scales and heterogeneity among individuals in disease progression. A Markov chain Monte Carlo algorithm is described for posterior computation, and the methods are applied to data from a study of uterine leiomyoma.     

Cumulative disease progression models for cross‐sectional data: A review and comparison

A better understanding of disease progression is beneficial for early diagnosis and appropriate individual therapy. Many different approaches for statistical modelling of cumulative disease progression have been proposed in the literature, including simple path models up to complex restricted Bayesian networks. Important fields of application are diseases such as cancer and HIV. Tumour progression is measured by means of chromosome aberrations, whereas people infected with HIV develop drug resistances because of genetic changes of the HI‐virus. These two very different diseases have typical courses of disease progression, which can be modelled partly by consecutive and partly by independent steps. This paper gives an overview of the different progression models and points out their advantages and drawbacks. Different models are compared via simulations to analyse how they work if some of their assumptions are violated. In a simulation study, we evaluate how models perform in terms of fitting induced multivariate probability distributions and topological relationships. We often find that the true model class used for generating data is outperformed by either a less or a more complex model class. The more flexible conjunctive Bayesian networks can be used to fit oncogenetic trees, whereas mixtures of oncogenetic trees with three tree components can be well fitted by mixture models with only two tree components.     

Studying health histories of cancer: A new model connecting cancer incidence and survival

The results of recent experimental and epidemiological studies provide evidence on the connection between carcinogenesis, cancer progression, and aging. Existing models, however, are traditionally focused only on one of these aspects of health deterioration. In this paper, we derive a new model of cancer, which describes the connection between the ages at disease onset, the duration of disease, and life span of respective individuals. The model combines ideas used in the two hits model of carcinogenesis with those used in the Le Bras multistate model of aging with constant transition intensities. The model is used in the joint analyses of the US demographic mortality data and SEER data for selected cancers. The results show that the developed approach is capable of explaining links among health history data and provides useful insights on mechanisms of cancer occurrence, disease progression, other aging-related changes, and mortality. Further developments of this model are discussed.     

Can Newer Therapies Delay the Progression of Type 2 Diabetes Mellitus?

ObjectiveTo review the multifactorial and progressive nature of type 2 diabetes mellitus (T2DM), the consequences of its progression, and the potential of traditional and newer therapies to delay the progression of this disease.MethodsThe relevant literature is reviewed, and the mechanisms of action of novel agents for treatment of T2DM are discussed.ResultsThe global prevalence of diabetes has been increasing in recent decades, reaching near-epidemic proportions, and is projected to more than double by 2030. More than 90% of cases of diabetes in most countries consist of T2DM, but many individuals remain undiagnosed or are diagnosed only after their disease has progressed considerably. Inadequate glycemic control in a majority of patients with T2DM is due to the progressive nature of the disease, delay in initiating pharmacotherapy, and failure to intensify treatment more quickly in patients who do not achieve glycemic targets. Traditional oral therapies are usually effective at lowering hyperglycemia initially but do not prevent disease progression; thus, many patients ultimately require insulin. Furthermore, because most antidiabetic therapies are associated with weight gain or risk of hypoglycemia (or both), patients may not adhere to treatment recommendations.ConclusionA new therapeutic approach focuses on the use of the incretin hormone glucagon-like peptide-1. Analogues of this hormone delay the progression of β-cell dysfunction and promote β-cell regeneration in animal models. In clinical trials, they have been shown to improve glycemic control by increasing glucose-stimulated insulin secretion and suppressing glucagon secretion. At high concentrations, they also slow gastric emptying and increase satiety, which often promotes weight loss. Another approach is to inhibit the enzyme dipeptidyl-peptidase 4, which rapidly inactivates glucagon-like peptide- 1 and glucose-dependent insulinotropic polypeptide, thereby increasing endogenous incretin levels. (Endocr Pract. 2008;14:625-638)     

A design for studying the clustering of plant or animal species using quadrat sizes in geometric progression

An experimental design involving randomly selected quadrats with areas in geometric progression is discussed. The procedure is suggested when the clustering habits of a plant or animal species ate unknown. Several models for the number of individuals per unit area are considered. Tests for these distributions versus random dispersal are given, along with techniques for estimating cluster area and number of individuals per cluster. Illustrations are included.     

Hepatitis C in the setting of HIV co-infection

Hepatitis C virus (HCV) co-infection is common among HIV-infected individuals and can lead to increased morbidity and mortality in this population. HIV adversely impacts the natural history of HCV disease with higher rates of liver disease progression but the effect of HCV on the natural history of HIV is disputed. Additionally, presence of HCV may decrease tolerability of highly active antiretroviral regimens for HIV treatment due to a potential increase in hepatotoxicity. Currently there is limited information available regarding HCV therapy in the setting of HIV co-infection but the HCV virologic response to interferon regimens appears to be similar to those individuals with HCV infection alone. However, additional information is required to assess the efficacy and safety of HCV therapy including possible interaction of HCV and HIV anti-viral medications in these co-infected individuals.     

Adaptation of a planar microbiaxial optomechanical device for the tubular biaxial microstructural and macroscopic characterization of small vascular tissues

Murine models of disease are a powerful tool for researchers to gain insight into disease formation, progression, and therapies. The biomechanical indicators of diseased tissue provide a unique insight into some of these murine models, since the biomechanical properties in scenarios such as aneurysm and Marfan syndrome can dictate tissue failure and mortality. Understanding the properties of the tissue on the macroscopic scale has been shown to be important, as one can then understand the tissue's ability to withstand the high stresses seen in the cardiac pulsatile cycle. Alterations in the biomechanical response can foreshadow prospective mechanical failure of the tissue. These alterations are often seen on the microstructural level, and obtaining detailed information on such changes can offer a better understanding of the phenomena seen on the macroscopic level. Unfortunately, mouse models present problems due to the size and delicate features in the mechanical testing of such tissues. In addition, some smaller arteries in large-animal studies (e.g., coronary and cerebral arteries) can present the same issues, and are sometimes unsuitable for planar biaxial testing. The purpose of this paper is to present a robust method for the investigation of the mechanical properties of small arteries and the classification of the microstructural orientation and degree of fiber alignment. This occurs through the cost-efficient modification of a planar biaxial tester that works in conjunction with a two-photon nonlinear microscope. This system provides a means to further investigate how microstructure and mechanical properties are modified in diseased transgenic animals where the tissue is in small tube form. Several other hard-to-test tubular specimens such as cerebral aneurysm arteries and atherosclerotic coronary arteries can also be tested using the described modular device.     

DIAGNOSTIC SELF‐TESTING: AUTONOMOUS CHOICES AND RELATIONAL RESPONSIBILITIES

Diagnostic self‐testing devices are being developed for many illnesses, chronic diseases and infections. These will be used in hospitals, at point‐of‐care facilities and at home. Designed to allow earlier detection of diseases, self‐testing diagnostic devices may improve disease prevention, slow the progression of disease and facilitate better treatment outcomes. These devices have the potential to benefit both the individual and society by enabling individuals to take a more proactive role in the maintenance of their health and by helping society improve health and reduce health costs. However, the full implications of future home‐based diagnostic technology for individuals and society remain unclear due to their novelty. We argue that the development of diagnostic tools, especially for home use, will heighten a number of ethical challenges. This paper will explore some of the ethical implications of home‐based self‐testing diagnostic devices for the autonomous and relational dimensions of the person. This will be facilitated by examining the impact of diagnostic devices for individual autonomy, for the delivery of accurate diagnosis and for the personal significance of the information for the user. The latter will be examined using Charles Taylor's view of personhood and his emphasis on human agency and interpretation. While the ethical issues are not necessarily new, the development of home‐based self‐testing diagnostic devices will make issues regarding autonomy, accuracy of information and personal significance more and more demanding. This will be the case particularly when an individual's autonomous choices come into conflict with the person's relational responsibilities.     

Mouse models of Huntington's disease and methodological considerations for therapeutic trials

Huntington's disease (HD) is an autosomal dominant, progressive, and fatal neurodegenerative disorder caused by an expanded polyglutamine cytosine–adenine–guanine repeat in the gene coding for the protein huntingtin. Despite great progress, a direct causative pathway from the HD gene mutation to neuronal dysfunction and death has not yet been established. One important advance in understanding the pathogenic mechanisms of this disease has been the development of multiple murine models that replicate many of the clinical, neuropathological, and molecular events in HD patients. These models have played an important role in providing accurate and experimentally accessible systems to study multiple aspects of disease pathogenesis and to test potential therapeutic treatment strategies. Understanding how disease processes interrelate has become important in identifying a pharmacotherapy in HD and in the design of clinical trials. A review of the current state of HD mouse models and their successes in elucidating disease pathogenesis are discussed. There is no clinically proven treatment for HD that can halt or ameliorate the inexorable disease progression. As such, a guide to assessing studies in mouse models and salient issues related to translation from mice to humans are included.     

Overview of advances in educational and social supports for young persons with NCL disorders

Vision loss, dementia, and motor and speech declines all impact the educational experience of individuals with Batten disease and can adversely impact effective learning. There are as yet limited data to support evidence-based approaches to meeting the educational needs of affected individuals. This paper provides an overview of recent work to evaluate and address educational issues with a life-long perspective relevant to individuals with juvenile-onset neuronal ceroid lipofuscinosis (JNCL) and the professionals that provide them with educational support. In particular, several main activities of the recently completed ‘JNCL and Education’ project are summarised, including a survey of parents, educational professionals and social/health workers, development of a formative assessment tool to identify and respond to an individual student's strengths and needs in the learning environment, and proposed strategies for prolonging literacy and language skills. A key concept that should be emphasised in the educational plan for students with JNCL is that of ‘proactive’ and ‘hastened’ learning, that is, providing an early emphasis on adaptive skills that will be required in the later stages of disease progression when new learning will be more difficult to achieve. An additional key concept is participation in real-life activities to maintain skills and quality of life, particularly in the later stages of disease progression.     

A Bayesian basket trial design that borrows information across strata based on the similarity between the posterior distributions of the response probability

Basket trials simultaneously evaluate the effect of one or more drugs on a defined biomarker, genetic alteration, or molecular target in a variety of disease subtypes, often called strata. A conventional approach for analyzing such trials is an independent analysis of each of the strata. This analysis is inefficient as it lacks the power to detect the effect of drugs in each stratum. To address these issues, various designs for basket trials have been proposed, centering on designs using Bayesian hierarchical models. In this article, we propose a novel Bayesian basket trial design that incorporates predictive sample size determination, early termination for inefficacy and efficacy, and the borrowing of information across strata. The borrowing of information is based on the similarity between the posterior distributions of the response probability. In general, Bayesian hierarchical models have many distributional assumptions along with multiple parameters. By contrast, our method has prior distributions for response probability and two parameters for similarity of distributions. The proposed design is easier to implement and less computationally demanding than other Bayesian basket designs. Through a simulation with various scenarios, our proposed design is compared with other designs including one that does not borrow information and one that uses a Bayesian hierarchical model.     

SIR-SVS epidemic models with continuous and impulsive vaccination strategies

Vaccination is important for the control of some infectious diseases. This paper considers two SIR-SVS epidemic models with vaccination, where it is assumed that the vaccination for the newborns is continuous in the two models, and that the vaccination for the susceptible individuals is continuous and impulsive, respectively. The basic reproduction numbers of two models, determining whether the disease dies out or persists eventually, are all obtained. For the model with continuous vaccination for the susceptibles, the global stability is proved by using the Lyapunov function. Especially for the endemic equilibrium, to prove the negative definiteness of the derivative of the Lyapunov function for all the feasible values of parameters, it is expressed in three different forms for all the feasible values of parameters. For the model with pulse vaccination for the susceptibles, the global stability of the disease free periodic solution is proved by the comparison theorem of impulsive differential equations. At last, the effect of vaccination strategies on the control of the disease transmission is discussed, and two types of vaccination strategies for the susceptible individuals are also compared.     

Survey and interzoo studies used to address husbandry problems in some zoo vertebrates

In addition to genetic and demographic considerations, SSP coordinators have been asked to systematically address husbandry issues. Three approaches to the study of captive management issues are typically used: (1) large numbers of individuals are housed at a single institution maintained in a situation that facilitates systematic evaluation of the captive environment, (2) an investigator travels to many institutions to gather data on a single taxon, or (3) an investigator surveys existing information by assembling data from a large number of individuals at a variety of institutions. Each approach has both advantages and disadvantages in terms of feasibility and the type of results obtained. The use of surveys to obtain information about husbandry parameters from a large number of animals maintained at a variety of zoos is quite possibly the most common approach used among zoo managers. Zoo husbandry surveys are typically developed to address issues problematic to a particular species, including reproductive failure and health issues. Unfortunately, surveys appear to be an often misused research tool among zoo professionals. Surveys can be improved by working with psychologists or sociologists at local universities, by narrowing the focus of the survey's purpose, and by carefully constructing each question. © 1994 Wiley-Liss, Inc.     

The effect of delayed death in HIV/AIDS models

HIV-infected patients who receive treatment survive for some years after they have acquired the disease. The received treatment causes sustained reduction of viral reproduction by improving the immune function, leading to prolonged progression period to AIDS development. This prolonged progression period has created variability in survival times that affects estimates produced using mathematical models that do not include delay in disease related mortality. This paper investigates the effect of including delay in AIDS death occurrence in HIV/AIDS transmission models. A simple mathematical model with two stages of HIV progression is developed and extended to include time delay in the occurrence of AIDS deaths. Numerical simulations indicate that time delay changes the mortality curves considerably but has less effect on the proportion of infectives. The study highlights the importance of incorporating delay in models of HIV/AIDS for the production of accurate HIV/AIDS estimates.     

Risk Models to Predict Chronic Kidney Disease and Its Progression: A Systematic Review

Background

Chronic kidney disease (CKD) is common, and associated with increased risk of cardiovascular disease and end-stage renal disease, which are potentially preventable through early identification and treatment of individuals at risk. Although risk factors for occurrence and progression of CKD have been identified, their utility for CKD risk stratification through prediction models remains unclear. We critically assessed risk models to predict CKD and its progression, and evaluated their suitability for clinical use.

Methods and Findings

We systematically searched MEDLINE and Embase (1 January 1980 to 20 June 2012). Dual review was conducted to identify studies that reported on the development, validation, or impact assessment of a model constructed to predict the occurrence/presence of CKD or progression to advanced stages. Data were extracted on study characteristics, risk predictors, discrimination, calibration, and reclassification performance of models, as well as validation and impact analyses. We included 26 publications reporting on 30 CKD occurrence prediction risk scores and 17 CKD progression prediction risk scores. The vast majority of CKD risk models had acceptable-to-good discriminatory performance (area under the receiver operating characteristic curve>0.70) in the derivation sample. Calibration was less commonly assessed, but overall was found to be acceptable. Only eight CKD occurrence and five CKD progression risk models have been externally validated, displaying modest-to-acceptable discrimination. Whether novel biomarkers of CKD (circulatory or genetic) can improve prediction largely remains unclear, and impact studies of CKD prediction models have not yet been conducted. Limitations of risk models include the lack of ethnic diversity in derivation samples, and the scarcity of validation studies. The review is limited by the lack of an agreed-on system for rating prediction models, and the difficulty of assessing publication bias.

Conclusions

The development and clinical application of renal risk scores is in its infancy; however, the discriminatory performance of existing tools is acceptable. The effect of using these models in practice is still to be explored. Please see later in the article for the Editors'' Summary