Phylogenetic tree selection by the adjusted k-means approach

The reconstruction of phylogenetic trees is one of the most important and interesting problems of the evolutionary study. There are many methods proposed in the literature for constructing phylogenetic trees. Each approach is based on different criteria and evolutionary models. However, the topologies of trees constructed from different methods may be quite different. The topological errors may be due to unsuitable criterions or evolutionary models. Since there are many tree construction approaches, we are interested in selecting a better tree to fit the true model. In this study, we propose an adjusted k-means approach and a misclassification error score criterion to solve the problem. The simulation study shows this method can select better trees among the potential candidates, which can provide a useful way in phylogenetic tree selection.     

Statistical and Combinatorial Aspects of Comparative Genomics*

Abstract. This document presents a survey of the statistical and combinatorial aspects of four areas of comparative genomics: gene order based measures of evolutionary distances between species, construction of phylogenetic trees, detection of horizontal transfer of genes, and detection of ancient whole genome duplications.     

The Rise of Statistical Phylogenetics

This paper provides a review of the many applications of statistics within the field of phylogenetics, that is, the study of evolutionary history. The reader is assumed to be a statistician rather than a phylogeneticist, so some background is given on what phylogenetics is, along with a brief history of different approaches to phylogenetic inference. The latter half of the paper focuses on a series of open statistical problems in the field with the aim of encouraging more statisticians to engage with this fascinating area of research.     

Bayesian phylogenetic inference from animal mitochondrial genome arrangements

Summary. The determination of evolutionary relationships is a fundamental problem in evolutionary biology. Genome arrangement data are potentially more informative than deoxyribonucleic acid sequence data for inferring evolutionary relationships between distantly related taxa. We describe a Bayesian framework for phylogenetic inference from mitochondrial genome arrangement data using Markov chain Monte Carlo methods. We apply the method to assess evolutionary relationships between eight animal phyla.     

A Bayesian Approach to Modelling Reticulation Events with Application to the Ribosomal Protein Gene rps11 of Flowering Plants

Traditional phylogenetic inference assumes that the history of a set of taxa can be explained by a tree. This assumption is often violated as some biological entities can exchange genetic material giving rise to non‐treelike events often called reticulations. Failure to consider these events might result in incorrectly inferred phylogenies. Phylogenetic networks provide a flexible tool which allows researchers to model the evolutionary history of a set of organisms in the presence of reticulation events. In recent years, a number of methods addressing phylogenetic network parameter estimation have been introduced. Some of them are based on the idea that a phylogenetic network can be defined as a directed acyclic graph. Based on this definition, we propose a Bayesian approach to the estimation of phylogenetic network parameters which allows for different phylogenies to be inferred at different parts of a multiple DNA alignment. The algorithm is tested on simulated data and applied to the ribosomal protein gene rps11 data from five flowering plants, where reticulation events are suspected to be present. The proposed approach can be applied to a wide variety of problems which aim at exploring the possibility of reticulation events in the history of a set of taxa.     

Cofactors and markers of disease progression in human immunodeficiency virus infection

The identification of factors which are related to human immunodeficiency virus (HIV) disease progression, either by a direct interaction with HIV to increase the rate of disease progression or by providing an indication of an infected individual's likely prognosis, can have great value when understanding HIV pathogenesis and in the development of novel therapeutic approaches. This paper describes the roles of the CD4 cell count and the viral load as markers of disease progression and discusses the recent findings on chemokine receptors in HIV infection. Our current knowledge on these factors is summarized and unresolved statistical issues are highlighted.     

A Bayesian approach for estimating antiviral efficacy in HIV dynamic models

The study of HIV dynamics is one of the most important developments in recent AIDS research. It has led to a new understanding of the pathogenesis of HIV infection. Although important findings in HIV dynamics have been published in prestigious scientific journals, the statistical methods for parameter estimation and model-fitting used in those papers appear surprisingly crude and have not been studied in more detail. For example, the unidentifiable parameters were simply imputed by mean estimates from previous studies, and important pharmacological/clinical factors were not considered in the modelling. In this paper, a viral dynamic model is developed to evaluate the effect of pharmacokinetic variation, drug resistance and adherence on antiviral responses. In the context of this model, we investigate a Bayesian modelling approach under a non-linear mixed-effects (NLME) model framework. In particular, our modelling strategy allows us to estimate time-varying antiviral efficacy of a regimen during the whole course of a treatment period by incorporating the information of drug exposure and drug susceptibility. Both simulated and real clinical data examples are given to illustrate the proposed approach. The Bayesian approach has great potential to be used in many aspects of viral dynamics modelling since it allow us to fit complex dynamic models and identify all the model parameters. Our results suggest that Bayesian approach for estimating parameters in HIV dynamic models is flexible and powerful.     

Analysing direct effects in randomized trials with secondary interventions: an application to human immunodeficiency virus prevention trials

Summary.  The 'Methods for improving reproductive health in Africa' trial is a recently completed randomized trial that investigated the effect of diaphragm and lubricant gel use in reducing infection by the human immunodeficiency virus (HIV) among susceptible women. 5045 women were randomly assigned to either the active treatment arm or not. Additionally, all subjects in both arms received intensive condom counselling and provision, the 'gold standard' HIV prevention barrier method. There was much lower reported use of condoms in the intervention arm than in the control arm, making it difficult to answer important public health questions based solely on the intention-to-treat analysis. We adapt an analysis technique from causal inference to estimate the 'direct effects' of assignment to the diaphragm arm, adjusting for use of condoms in an appropriate sense. Issues raised in the trial apply to other trials of HIV prevention methods, some of which are currently being conducted or designed.     

Human immunodeficiency virus risk: is it possible to dissuade people from having unsafe sex?

Summary. The cumulative number of human immunodeficiency virus (HIV) infections worldwide has reached 60 million in little over 30 years. HIV continues to spread despite a detailed understanding of the manner in which it spreads and measures which can prevent spread. Some governments have been highly successful in containing the spread of HIV through blood products and from mother to child and among injecting drug users. Lack of political will, lack of resources or challenges to widely accepted scientific evidence have held back similar interventions in other countries. It has proved much more difficult to reduce the sexual transmission of HIV in both high and low income countries. A wide range of strategies has been identified but it remains unclear which strategies deserve priority and what methods of promoting them have the greatest effect. There is ample evidence that awareness of HIV and changes in sexual behaviour have occurred widely but the penetration of information remains poor in some vulnerable groups especially adolescents and women in poorer countries. Further obstacles face those who have information about the risk. The subordinate position of women and a desire for large families are important obstacles to condom negotiation and use. Urbanization, poverty, conflict and declining public services all exacerbate unsafe sexual behaviour. We argue that so-called 'structural' interventions directed at these wider contexts of unsafe behaviour merit greater attention. Such approaches have the added benefit of being less susceptible to 'risk compensation' which has the potential to undermine strategies directed at reducing the transmission efficiency of HIV.     

Robust modelling of the relationship between CD4 and viral load for complex AIDS data

CD4 and viral load play important roles in HIV/AIDS studies, and the study of their relationship has received much attention with well-known results. However, AIDS datasets are often highly complex in the sense that they typically contain outliers, measurement errors, and missing data. These data complications can greatly affect statistical analysis results, but much of the literature fail to address these issues in data analysis. In this paper, we re-visit the important relationship between CD4 and viral load and propose methods which simultaneously address outliers, measurement errors, and missing data. We find that the strength of the relationship may be severely mis-estimated if measurement errors and outliers are ignored. The proposed methods are general and can be used in other settings, where jointly modelling several different types of longitudinal data is required in the presence of data complications.     

Trend analysis with response incompatible formats and measurement error

The increasing popularity of longitudinal studies, along with the rapid advances in science and technology, has created a potential incompatibility between data formats, which leads to an inference problem when applying conventional statistical methods. This inference problem is further compounded by measurement error, since incompatible data format often arise in the context of measuring latent constructs. Without a systematic study of the impact of scale differences, ad-hoc approaches generally lead to inconsistent estimates and thus, invalid statistical inferences. In this paper, we examine the asymptotic properties and identify conditions that guarantee consistent estimation within the context of a trend analysis with response incompatible formats and measurement error. For model estimation, we introduce two competing methods that use a generalized estimating equation approach to provide inferences for the parameters of interest, and highlight the relative strengths of each method. The approach is illustrated by data obtained from a multi-centre AIDS cohort study (MACS), where a trend analysis of an immunologic marker of HIV infection is of interest.     

统计功效与分析软件

一、背景的说明统计功效（ＳｔａｔｉｓｔｉｃａｌＰｏｗｅｒ）是统计学中的一个重要概念,也是一个十分有用的测度指标。国外的许多教科书中对统计功效有过比较详细的讨论,国内出版的教科书中对此也有涉及（参见：袁卫等编著《新编统计学教程》,经济科学出版社,１９９９年２月）。简单地说,统计功效是指,在假设检验中,拒绝原假设后,接受正确的替换假设的概率。我们知道,在假设检验中有α错误和β错误。α错误是弃真错误,β错误是取伪错误。取伪错误是指,拒绝原假设后,接受错误的替换假设的概率。由此可知,统计功效等于１－β。…     

Mixed-effects Models with Skewed Distributions for Time-varying Decay Rate in HIV Dynamics

After initiation of treatment, HIV viral load has multiphasic changes, which indicates that the viral decay rate is a time-varying process. Mixed-effects models with different time-varying decay rate functions have been proposed in literature. However, there are two unresolved critical issues: (i) it is not clear which model is more appropriate for practical use, and (ii) the model random errors are commonly assumed to follow a normal distribution, which may be unrealistic and can obscure important features of within- and among-subject variations. Because asymmetry of HIV viral load data is still noticeable even after transformation, it is important to use a more general distribution family that enables the unrealistic normal assumption to be relaxed. We developed skew-elliptical (SE) Bayesian mixed-effects models by considering the model random errors to have an SE distribution. We compared the performance among five SE models that have different time-varying decay rate functions. For each model, we also contrasted the performance under different model random error assumptions such as normal, Student-t, skew-normal, or skew-t distribution. Two AIDS clinical trial datasets were used to illustrate the proposed models and methods. The results indicate that the model with a time-varying viral decay rate that has two exponential components is preferred. Among the four distribution assumptions, the skew-t and skew-normal models provided better fitting to the data than normal or Student-t model, suggesting that it is important to assume a model with a skewed distribution in order to achieve reasonable results when the data exhibit skewness.     

Improving prediction performance of stellar parameters using functional models

This paper investigates the problem of prediction of stellar parameters, based on the star's electromagnetic spectrum. The knowledge of these parameters permits to infer on the evolutionary state of the star. From a statistical point of view, the spectra of different stars can be represented as functional data. Therefore, a two-step procedure decomposing the spectra in a functional basis combined with a regression method of prediction is proposed. We also use a bootstrap methodology to build prediction intervals for the stellar parameters. A practical application is also provided to illustrate the numerical performance of our approach.     

Bayesian Experimental Design for Long-Term Longitudinal HIV Dynamic Studies

The study of HIV dynamics is one of the most important developments in recent AIDS research for understanding the pathogenesis of HIV-1 infection and antiviral treatment strategies. Currently a large number of AIDS clinical trials on HIV dynamics are in development worldwide. However, many design issues that arise from AIDS clinical trials have not been addressed. In this paper, we use a simulation-based approach to deal with design problems in Bayesian hierarchical nonlinear (mixed-effects) models. The underlying model characterizes the long-term viral dynamics with antiretroviral treatment where we directly incorporate drug susceptibility and exposure into a function of treatment efficacy. The Bayesian design method is investigated under the framework of hierarchical Bayesian (mixed-effects) models. We compare a finite number of feasible candidate designs numerically, which are currently used in AIDS clinical trials from different perspectives, and provide guidance on how a design might be chosen in practice.     

Adaptive trait evolution in random environment

Current phylogenetic comparative methods generally employ the Ornstein–Uhlenbeck(OU) process for modeling trait evolution. Being able of tracking the optimum of a trait within a group of related species, the OU process provides information about the stabilizing selection where the population mean adopts a particular trait value. The optima of a trait may follow certain stochastic dynamics along the evolutionary history. In this paper, we extend the current framework by adopting a rate of evolution which behave according to pertinent stochastic dynamics. The novel model is applied to analyze about 225 datasets collected from the existing literature. Results validate that the new framework provides a better fit for the majority of these datasets.     

Review of the use of context in statistical image analysis

SUMMARY This paper is a review of the use of contextual information in statistical image analysis. After defining what we mean by 'context', we describe the Bayesian approach to high-level image analysis using deformable templates. We describe important aspects of work on character recognition and syntactic pattern recognition; in particular, aspects of the work which are relevant to scene understanding. We conclude with a review of some work on knowledge-based systems which use context to aid object recognition.     

Reconstructing the incidence of human immunodeficiency virus (HIV) in Hong Kong by using data from HIV positive tests and diagnoses of acquired immune deficiency syndrome

Summary. The human immunodeficiency virus–acquired immune deficiency syndrome (HIV–AIDS) epidemic in Hong Kong has been under surveillance in the form of voluntary reporting since 1984. However, there has been little discussion or research on the reconstruction of the HIV incidence curve. This paper is the first to use a modified back-projection method to estimate the incidence of HIV in Hong Kong on the basis of the number of positive HIV tests only. The model proposed has several advantages over the original back-projection method based on AIDS data only. First, not all HIV-infected individuals will develop AIDS by the time of analysis, but some of them may undertake an HIV test; therefore, the HIV data set contains more information than the AIDS data set. Second, the HIV diagnosis curve usually has a smoother pattern than the AIDS diagnosis curve, as it is not affected by redefinition of AIDS. Third, the time to positive HIV diagnosis is unlikely to be affected by treatment effects, as it is unlikely that an individual receives medication before the diagnosis of HIV. Fourth, the induction period from HIV infection to the first HIV positive test is usually shorter than the incubation period which is from HIV infection to diagnosis of AIDS. With a shorter induction period, more information becomes available for estimating the HIV incidence curve. Finally, this method requires the number of positive HIV diagnoses only, which is readily available from HIV–AIDS surveillance systems in many countries. It is estimated that, in Hong Kong, the cumulative number of HIV infections during the period 1979–2000 is about 2600, whereas an estimate based only on AIDS data seems to give an underestimate.     

A multistate approach for estimating the incidence of human immunodeficiency virus by using data from a prevalent cohort study

Summary.  Cohort studies of individuals infected with the human immunodeficiency virus (HIV) provide useful information on the past pattern of HIV diagnoses, progression of the disease and use of antiretroviral therapy. We propose a new method for using individual data from an open prevalent cohort study to estimate the incidence of HIV, by jointly modelling the HIV diagnosis, the inclusion in the cohort and the progression of the disease in a Markov model framework. The estimation procedure involves the construction of a likelihood function which takes into account the probability of observing the total number of subjects who are enrolled in the cohort and the probabilities of passage through the stages of disease for each observed subject conditionally on being included in the cohort. The estimator of the HIV infection rate is defined as the function which maximizes a penalized likelihood, and the solution of this maximization problem is approximated on a basis of cubic M -splines. The method is illustrated by using cohort data from a hospital-based surveillance system of HIV infection in Aquitaine, a region of south-western France. A simulation study is performed to study the ability of the model to reconstruct the incidence of HIV from prevalent cohort data.     

On the assessment of various factors effecting the improvement in CD4 count of aids patients undergoing antiretroviral therapy using generalized Poisson regression

An important marker for identifying the progression of human immunodeficiency virus (HIV) infection in an individual is the CD4 cell count. Antiretroviral therapy (ART) is a treatment for HIV/AIDS (AIDS, acquired immune-deficiency syndrome) which prolongs and improves the lives of patients by improving the CD4 cell count and strengthen the immune system. This strengthening of the immune system in terms of CD4 count, not only depends on various biological factors, but also other behavioral factors. Previous studies have shown the effect of CD4 count on the mortality, but nobody has attempted to study the factors which are likely to influence the improvement in CD4 count of patients diagnosed of AIDS and undergoing ART. In this paper, we use Poisson regression model (GPR) for exploring the effect of various socio-demographic covariates such as age, gender, geographical location, and drug usage on the improvement in the CD4 count of AIDS patients. However, if the CD4 count data suffers from under or overdispersion, we use GPR model and compare it with negative binomial distribution. Finally, the model is applied for the analysis of data on patients undergoing the ART in the Ram Manohar Lohia Hospital, Delhi, India. The data exhibited overdispersion and hence, GPR model provided the best fit.