Statistical Methods for Analyzing Right‐Censored Length‐Biased Data under Cox Model

Summary Length‐biased time‐to‐event data are commonly encountered in applications ranging from epidemiological cohort studies or cancer prevention trials to studies of labor economy. A longstanding statistical problem is how to assess the association of risk factors with survival in the target population given the observed length‐biased data. In this article, we demonstrate how to estimate these effects under the semiparametric Cox proportional hazards model. The structure of the Cox model is changed under length‐biased sampling in general. Although the existing partial likelihood approach for left‐truncated data can be used to estimate covariate effects, it may not be efficient for analyzing length‐biased data. We propose two estimating equation approaches for estimating the covariate coefficients under the Cox model. We use the modern stochastic process and martingale theory to develop the asymptotic properties of the estimators. We evaluate the empirical performance and efficiency of the two methods through extensive simulation studies. We use data from a dementia study to illustrate the proposed methodology, and demonstrate the computational algorithms for point estimates, which can be directly linked to the existing functions in S‐PLUS or R .     

An approximate likelihood for genetic data under a model with recombination and population splitting

We describe a new approximate likelihood for population genetic data under a model in which a single ancestral population has split into two daughter populations. The approximate likelihood is based on the ‘Product of Approximate Conditionals’ likelihood and ‘copying model’ of Li and Stephens [Li, N., Stephens, M., 2003. Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data. Genetics 165 (4), 2213–2233]. The approach developed here may be used for efficient approximate likelihood-based analyses of unlinked data. However our copying model also considers the effects of recombination. Hence, a more important application is to loosely-linked haplotype data, for which efficient statistical models explicitly featuring non-equilibrium population structure have so far been unavailable. Thus, in addition to the information in allele frequency differences about the timing of the population split, the method can also extract information from the lengths of haplotypes shared between the populations. There are a number of challenges posed by extracting such information, which makes parameter estimation difficult. We discuss how the approach could be extended to identify haplotypes introduced by migrants.     

Statistical Analysis of Uniparental Disomy Data Using Hidden Markov Models

Genetic studies of uniparental disomy (UPD) employing many markers have helped geneticists to gain a better understanding of the molecular mechanisms underlying nondisjunction. However, most existing methods cannot simultaneously analyze all genetic markers and consistently incorporate crossover interference; they thus fail to make the most use of genetic information in the data. In the present article, we describe a hidden Markov model for multilocus uniparental disomy data. This method is based on the chi-square model for the crossover process and can simultaneously incorporate all marker information including untyped and uninformative markers. We then apply this novel method to analyze a set of UPD15 data.     

On a Continuous Branching Process and Birth-Death and Immigration Models for Testing the Efficacy of Some Control Programmes for AIDS

The present communication is an attempt to describe the mode of propagation of AIDS epidemic and its control programme using a branching process as well as a birth-death and immigration model. A comparison of the project of AIDS control programme on the basis of its propagation by a continuous branching process model with that of a linear birth and death process with immigration shows a remarkable contrast. Branching process model shows that it is possible to control the propagation of the disease by suitably increasing the detection rate and lowering the infection rate. However, the propagation of AIDS models by birth and death Process with or without immigration shows that it is increasingly difficult to control the invasion of AIDS merely by controlling the birth, death and immigration parameters.     

A Note on the Two-Compartment Model in Pharmacokinetics

The almost-one parameter hypothesis is applied to the two-compartment model in pharmacokinetics. If the model is represented by a difference equation, then its coefficients are highly correlated, as was expected from the hypothesis.     

Statistical inference in a stochastic epidemic SEIR model with control intervention: Ebola as a case study

A stochastic discrete-time susceptible-exposed-infectious-recovered (SEIR) model for infectious diseases is developed with the aim of estimating parameters from daily incidence and mortality time series for an outbreak of Ebola in the Democratic Republic of Congo in 1995. The incidence time series exhibit many low integers as well as zero counts requiring an intrinsically stochastic modeling approach. In order to capture the stochastic nature of the transitions between the compartmental populations in such a model we specify appropriate conditional binomial distributions. In addition, a relatively simple temporally varying transmission rate function is introduced that allows for the effect of control interventions. We develop Markov chain Monte Carlo methods for inference that are used to explore the posterior distribution of the parameters. The algorithm is further extended to integrate numerically over state variables of the model, which are unobserved. This provides a realistic stochastic model that can be used by epidemiologists to study the dynamics of the disease and the effect of control interventions.     

Statistical analysis for haplotype-based matched case-control studies

Using unphased genotype data, we studied statistical inference for association between a disease and a haplotype in matched case-control studies. Statistical inference for haplotype data is complicated due to ambiguity of genotype phases. An estimating equation-based method is developed for estimating odds ratios and testing disease-haplotype association. The method potentially can also be applied to testing haplotype-environment interaction. Simulation studies show that the proposed method has good performance. The performance of the method in the presence of departures from Hardy-Weinberg equilibrium is also studied.     

Mixture Markov regression model with application to mosquito surveillance data analysis

A mixture Markov regression model is proposed to analyze heterogeneous time series data. Mixture quasi‐likelihood is formulated to model time series with mixture components and exogenous variables. The parameters are estimated by quasi‐likelihood estimating equations. A modified EM algorithm is developed for the mixture time series model. The model and proposed algorithm are tested on simulated data and applied to mosquito surveillance data in Peel Region, Canada.     

一个造血模型的概周期正解

应用Ｓｃｈａｕｄｅｒ不动点定理,研究了一个造血模型概周期正解的存在性及唯一性。     

Statistical Inference for a Gibbs Point Process of Mutually Non-Intersecting Discs

For a Gibbs point process of mutually non-intersecting discs a parameter estimation method is suggested. It is applied to a pattern of positions of beadlet anemones, for which, until now, no appropriate Gibbs process model has been found.     

On stochastic logistic population growth models with immigration and multiple births

This paper develops a stochastic logistic population growth model with immigration and multiple births. The differential equations for the low-order cumulant functions (i.e., mean, variance, and skewness) of the single birth model are reviewed, and the corresponding equations for the multiple birth model are derived. Accurate approximate solutions for the cumulant functions are obtained using moment closure methods for two families of model parameterizations, one for badger and the other for fox population growth. For both model families, the equilibrium size distribution may be approximated well using the Normal approximation, and even more accurately using the saddlepoint approximation. It is shown that in comparison with the corresponding single birth model, the multiple birth mechanism increases the skewness and the variance of the equilibrium distribution, but slightly reduces its mean. Moreover, the type of density-dependent population control is shown to influence the sign of the skewness and the size of the variance.     

麦蚜复合种群动态预测的Fuzzy推理模式及应用

本研究利用了山东省曲阜地区１９８２－１９９４年共１３年的资料，选用了３月下旬至４月上旬平均气温（℃）和４月７上旬温湿系数作为预报因子，麦蚜蚜量始达５００（头／丰株）的日期作为预报对象组建Ｆｕｚｚｙ推理模式。对历史资料进行回代验证，其历史拟合率达１００％。将１９９５年的观测数据作为独立样本进行试报，预测结果与实际一致。为麦蚜复合种群动态预测提供了一种新的研究方法。     

一个造血模型的持久生存

考虑下述造血模型dN(t)/dt=-δN(t)-βθ^nN(t)/θ^n N^n(t) 2βθ^nN(t-τ）/θ^n N^n(t-τ)e^γτ,t≥0,其中δ、β、θ、γ、τ∈（0, ∞）,n∈（1, ∞）,得到持久生存的充分条件。     

Model selection and parameter estimation for ion channel recordings with an application to the K + outward-rectifier in barley leaf

We present a statistical method, and its accompanying algorithms, for the selection of a mathematical model of the gating mechanism of an ion channel and for the estimation of the parameters of this model. The method assumes a hidden Markov model that incorporates filtering, colored noise and state-dependent white excess noise for the recorded data. The model selection and parameter estimation are performed via a Bayesian approach using Markov chain Monte Carlo. The method is illustrated by its application to single-channel recordings of the K⁺ outward-rectifier in barley leaf.Acknowledgement The authors thank Sake Vogelzang, Bert van Duijn and Bert de Boer for their helpful advice and useful comments and suggestions.     

A Sufficient Condition for Reducing Recursions in Hidden Markov Models

In hidden Markov models, the probability of observing a set of strings can be computed using recursion relations. We construct a sufficient condition for simplifying the recursion relations for a certain class of hidden Markov models. If the condition is satisfied, then one can construct a reduced recursion where the dependence on Markov states completely disappears. We discuss a specific example—namely, statistical multiple alignment based on the TKF-model—in which the sufficient condition is satisfied.     

Statistical Inference for Nanopore Sequencing with a Biased Random Walk?Model

Nanopore sequencing promises long read-lengths and single-molecule resolution, but the stochastic motion of the DNA molecule inside the pore is, as of this writing, a barrier to high accuracy reads. We develop a method of statistical inference that explicitly accounts for this error, and demonstrate that high accuracy (>99%) sequence inference is feasible even under highly diffusive motion by using a hidden Markov model to jointly analyze multiple stochastic reads. Using this model, we place bounds on achievable inference accuracy under a range of experimental parameters.     

Bayesian Inference for a Random Tessellation Process

This article describes an inhomogeneous Poisson point process in the plane with an intensity function based on a Dirichlet tessellation process and a method for using observations on the point process to make fully Bayesian inferences about the underlying tessellation. The method is implemented using a Markov chain Monte Carlo approach. An application to modeling the territories of clans of badgers, Meles meles, is described.