marginalized transition models for longitudinal binary data with ignorable and non ignorable drop out we extend the marginalized transition model of heagerty to accommodate non ignorable monotone drop out using a selection model weakly identified drop out parameters are held constant and their effects evaluated through sensitivity analysis for data missing at random mar efficiency of inverse probability of censoring weighted generalized estimating equations ipcw gee is as low as 40 per cent compared to a likelihood based marginalized transition model mtm with comparable modelling burden mtm and ipcw gee regression parameters both display misspecification bias for mar and non ignorable missing data and both reduce bias noticeably by improving model fit

lattice boltzmann simulation of blood flow in digitized vessel networks efficient flow of red blood cells rbcs and white blood cells wbcs through the microcirculation is necessary for oxygen and nutrient delivery as well as immune cell function because blood is a dense particulate suspension consisting of 40 rbcs by volume it is difficult to analyze the physical mechanisms by which individual blood cells contribute to the bulk flow properties of blood both experimental and computational approaches are hindered by these non newtonian properties and predicting macroscopic blood flow characteristics such as viscosity has historically been an empirical process in order to examine the effect of the individual cells on macroscopic blood rheology we developed a lattice boltzmann model that considers the blood as a suspension of particles in plasma accounting explicitly for cell cell and cell wall interactions previous studies have concluded that the abundance of leukocyte rolling in postcapillary venules is due to the interactions between red blood cells and leukocytes as they enter postcapillary expansions similar fluid dynamics may be involved in the initiation of rolling at branch points a phenomenon linked to atherosclerosis the lattice boltzmann approach is used to analyze the interactions of red and white blood cells as they flow through vascular networks digitized from normal and tumor tissue a major advantage of the lattice boltzmann method is the ability to simulate particulate flow dynamically and in any geometry using this approach we can accurately determine rbc wbc forces particle trajectories the pressure changes in each segment that accompany cellular traffic in the network and the forces felt by the vessel wall at any location in this technique intravital imaging using vascular contrast agents produces the network outline that is fed to the lattice boltzmann model this powerful and flexible model can be used to predict blood flow properties in any vessel geometry and with any blood composition

lorelogram a regression approach to exploring dependence in longitudinal categorical responses abstract we propose flexible regression estimators of the marginal pairwise log odds ratio measure of association for longitudinal categorical responses the function that we estimate is the log odds ratio analog of the correlogram hence we name the function the lorelogram measuring the association of categorical responses on the log odds scale allows ease of interpretation and allows pairwise association to remain unconstrained by the marginal means a feature not shared by correlations with binary or multinomial responses estimation of the function is achieved through the use of standard parametric estimating equations or through an extension of generalized additive models that allows nonparametric estimation of dependence functions for fixed smoothing parameters we apply the methodology to binary longitudinal data where scientific interest focuses on the dependence structure

a general class of pattern mixture models for nonignorable dropout with many possible dropout times in this article we consider the problem of fitting pattern mixture models to longitudinal data when there are many unique dropout times we propose a marginally specified latent class pattern mixture model the marginal mean is assumed to follow a generalized linear model whereas the mean conditional on the latent class and random effects is specified separately because the dimension of the parameter vector of interest the marginal regression coefficients does not depend on the assumed number of latent classes we propose to treat the number of latent classes as a random variable we specify a prior distribution for the number of classes and calculate approximate posterior model probabilities in order to avoid the complications with implementing a fully bayesian model we propose a simple approximation to these posterior probabilities the ideas are illustrated using data from a longitudinal study of depression in hiv infected women

accurate analysis of large scale periodic structures using an efficient sub entire domain basis function method an efficient sub entire domain sed basis function method has been proposed to analyze large scale periodic structures with finite sizes accurately the sed basis function is defined on the support of each single cell of the periodic structure after introducing dummy cells with respect to an observation cell the real physics of sed basis function is captured accurately by solving a small size problem further analysis has shown that all kinds of sed basis functions used in the periodic structure can be obtained by solving a single small problem hence the original large scale problem involving n n sub 0 m unknowns is decomposed into two small size problems one of which contains 9m unknowns and the other of which contains only n sub 0 unknowns here n sub 0 is the total cell number in the periodic structure and m is the number of subdomain basis functions in each unit cell several examples are given to test the validity and efficiency of the proposed method numerical results from the new method have excellent agreements with those from the conventional method of moments however the cpu time has been greatly reduced

a mixture of transition models for heterogeneous longitudinal ordinal data with applications to longitudinal bacterial vaginosis data markov models used to analyze transition patterns in discrete longitudinal data are based on the limiting assumption that individuals follow the common underlying transition process however when one is interested in diseases with different disease or severity subtypes explicitly modeling subpopulation specific transition patterns may be appropriate we propose a model which captures heterogeneity in the transition process through a finite mixture model formulation and provides a framework for identifying subpopulations at different risks we apply the procedure to longitudinal bacterial vaginosis bv study data and demonstrate that the model fits the data well further we show that under the mixture model formulation we can make the important distinction between how covariates affect transition patterns unique to each of the subpopulations and how they affect which subgroup a participant will belong to practically covariate effects on subpopulation specific transition behavior and those on subpopulation membership can be interpreted as effects on short term and long term transition behavior we further investigate models with higher order subpopulation specific transition dependence

estimating time to event from longitudinal categorical data an analysis of multiple sclerosis progression the expanded disability status scale edss is an ordinal score that measures progression in multiple sclerosis ms progression is defined as reaching edss of a certain level absolute progression or increasing edss by one point relative progression survival methods for time to progression are not adequate for such data because they do not exploit the edss level at the end of follow up instead we suggest a markov transitional model applicable for repeated categorical or ordinal data this approach enables derivation of covariate specific survival curves obtained after estimation of the regression coefficients and manipulations of the resulting transition matrix large sample theory and resampling methods are employed to derive pointwise confidence intervals which perform well in simulation methods for generating survival curves for time to edss of a certain level time to increase edss by at least one point and time to two consecutive visits with edss greater than 3 are described explicitly t

longitudinal studies of binary response data following case control and stratified case control sampling design and analysis we discuss design and analysis of longitudinal studies after case control sampling wherein interest is in the relationship between a longitudinal binary response that is related to the sampling case control variable and a set of covariates we propose a semiparametric modeling framework based on a marginal longitudinal binary response model and an ancillary model for subjects case control status in this approach the analyst must posit the population prevalence of being a case which is then used to compute an offset term in the ancillary model parameter estimates from this model are used to compute offsets for the longitudinal response model examining the impact of population prevalence and ancillary model misspecification we show that time invariant covariate parameter estimates other than the intercept are reasonably robust but intercept and time varying covariate parameter estimates can be sensitive to such misspecification we study design and analysis issues impacting study efficiency namely choice of sampling variable and the strength of its relationship to the response sample stratification choice of working covariance weighting and degree of flexibility of the ancillary model the research is motivated by a longitudinal study following case control sampling of the time course of attention deficit hyperactivity disorder adhd symptoms

a class of markov models for longitudinal ordinal data summary generalized linear models with serial dependence are often used for short longitudinal series heagerty 2002 has proposed marginalized transition models for the analysis of longitudinal binary data in this paper we extend this work to accommodate longitudinal ordinal data fisher scoring algorithms are developed for estimation methods are illustrated on quality of life data from a recent colorectal cancer clinical trial

analysis of a large linear antenna array of uniformly spaced thin wire dipoles parallel to a perfectly conducting plane a method of moments solution is presented for the analysis of a large linear antenna array of uniformly spaced thin wire dipoles parallel to an infinite perfectly conducting plane a single piecewise sinusoidal expansion and testing function per dipole is used in a galerkin procedure this results in a symmetric toeplitz generalized impedance matrix requiring approximately 2n 2 multiplications and divisions to determine the unknown dipole currents the solution corresponds to a savings of a factor of n 6 over a gaussian elimination solution computed results are given for a 1000 element dipole array

estimation of average treatment effect with incompletely observed longitudinal data application to a smoking cessation study we study the problem of estimation and inference on the average treatment effect in a smoking cessation trial where an outcome and some auxiliary information were measured longitudinally and both were subject to missing values dynamic generalized linear mixed effects models linking the outcome the auxiliary information and the covariates are proposed the maximum likelihood approach is applied to the estimation and inference on the model parameters the average treatment effect is estimated by the g computation approach and the sensitivity of the treatment effect estimate to the nonignorable missing data mechanisms is investigated through the local sensitivity analysis approach the proposed approach can handle missing data that form arbitrary missing patterns over time we applied the proposed method to the analysis of the smoking cessation trial

antedependence models for nonstationary categorical longitudinal data with ignorable missingness likelihood based inference time index ordered random variables are said to be antedependent ad of order p1 p2 pn if the kth variable conditioned on the pk immediately preceding variables is independent of all further preceding variables inferential methods associated with ad models are well developed for continuous primarily normal longitudinal data but not for categorical longitudinal data in this article we develop likelihood based inferential procedures for unstructured ad models for categorical longitudinal data specifically we derive maximum likelihood estimators mles of model parameters penalized likelihood criteria and likelihood ratio tests for determining the order of antedependence and likelihood ratio tests for homogeneity across groups time invariance of transition probabilities and strict stationarity we give closed form expressions for mles and test statistics which allow for the possibility of empty cells and monotone missing data for all cases save strict stationarity for data with an arbitrary missingness pattern we derive an efficient restricted expectation maximization algorithm for obtaining mles we evaluate the performance of the tests by simulation we apply the methods to longitudinal studies of toenail infection severity measured on a binary scale and alzheimer s disease severity measured on an ordinal scale the analysis of the toenail infection severity data reveals interesting nonstationary behavior of the transition probabilities and indicates that an unstructured first order ad model is superior to stationary and other structured first order ad models that have previously been fit to these data the analysis of the alzheimer s severity data indicates that the antedependence is second order with time invariant transition probabilities suggesting the use of a second order autoregressive cumulative logit model

exact artificial boundary conditions for a lattice boltzmann method abstract when using a lattice boltzmann method on an unbounded or very large domain one has to confine this spatial domain to a computational domain this is realized by introducing so called artificial boundary conditions until recently characteristic boundary conditions for the euler equations were considered and adapted to the lattice boltzmann method in this work we propose novel discrete artificial boundary conditions which are derived directly for the chosen lattice boltzmann model i e on the discrete level they represent the first exact artificial boundary conditions for lattice boltzmann methods doing so we avoid any detour of considering continuous equations and obtain boundary conditions that are perfectly adapted to the chosen numerical scheme we illustrate the idea for a one dimensional two velocity d1q2 lattice boltzmann method and show how the computational efficiency can be increased by a finite memory approach analytical investigations and numerical results finally demonstrate the advantages of our new boundary condition compared to previously used artificial boundary conditions

marginalized models for moderate to long series of longitudinal binary response data marginalized models heagerty 1999 biometrics 55 688 698 permit likelihood based inference when interest lies in marginal regression models for longitudinal binary response data two such models are the marginalized transition and marginalized latent variable models the former captures within subject serial dependence among repeated measurements with transition model terms while the latter assumes exchangeable or nondiminishing response dependence using random intercepts in this article we extend the class of marginalized models by proposing a single unifying model that describes both serial and long range dependence this model will be particularly useful in longitudinal analyses with a moderate to large number of repeated measurements per subject where both serial and exchangeable forms of response correlation can be identified we describe maximum likelihood and bayesian approaches toward parameter estimation and inference and we study the large sample operating characteristics under two types of dependence model misspecification data from the madras longitudinal schizophrenia study thara et al 1994 acta psychiatrica scandinavica 90 329 336 are analyzed

frequency domain green s function for a planar periodic semi infinite phased array i truncated floquet wave formulation this two part sequence deals with the derivation and physical interpretation of a uniform high frequency solution for the field radiated at finite distance by a planar semi infinite phased array of parallel elementary electric dipoles the field obtained by direct summation over the contributions from the individual radiators is restructured into a double series of wavenumber spectral integrals whose asymptotic reduction yields a series encompassing propagating and evanescent floquet waves fws together with corresponding diffracted rays which arise from scattering of the fw at the edge of the array the formal aspects of the solution are treated in the present paper they involve a sequence of manipulations in the complex spectral wavenumber planes that prepare the integrands for subsequent efficient and physically incisive asymptotics based on the method of steepest descent different species of spectral poles define the various species of propagating and evanescent fw their interception by the steepest descent path sdp determines the variety of shadow boundaries for the edge truncated fw the uniform asymptotic reduction of the sdp integrals performed by the van der waerden 1951 procedure and yielding a variety of edge diffracted fields completes the formal treatment

an appraisal of methods for the analysis of longitudinal categorical data with mar drop outs a number of methods for analysing longitudinal ordinal categorical data with missing at random drop outs are considered two are maximum likelihood methods maxlik which employ marginal global odds ratios to model associations the remainder use weighted or unweighted generalized estimating equations gee two of the gee use cholesky decomposed standardized residuals to model the association structure while another three extend methods developed for longitudinal binary data in which the association structures are modelled using either gaussian estimation multivariate normal estimating equations or conditional residuals simulated data sets were used to discover differences among the methods in terms of biases variances and convergence rates when the association structure is misspecified the methods were also applied to a real medical data set two of the gee methods referred to as cond and ml norm in this paper and by their originators were found to have relatively good convergence rates and mean squared errors for all sample sizes 80 120 300 considered and one more referred to as mgee in this paper and by its originators worked fairly well for all but the smallest sample size 80

global cross ratio models for bivariate discrete ordered responses summary a family of statistical models is presented for bivariate discrete response to a regressor when both components of the response have ordered categories association between components is expressed in terms of global cross ratios cross product ratios of quadrant probabilities for each double dichotomy of the response table of probabilities into quadrants pearson and heron 1913 biometrika 9 159 315 these models are extensions to the work of plackett 1965 journal of the american statistical association 60 516 522 and mantel and brown 1973 biometrics 29 649 665 the marginal cumulative probabilities may satisfy linear logistic or other generalized linear models mccullagh 1980 journal of the royal statistical society series b 42 109 142 an analysis of patients postoperative pain level and medication frequency illustrates these methods

bayesian semiparametric regression for longitudinal binary processes with missing data longitudinal studies with binary repeated measures are widespread in biomedical research marginal regression approaches for balanced binary data are well developed whereas for binary process data where measurement times are irregular and may differ by individuals likelihood based methods for marginal regression analysis are less well developed in this article we develop a bayesian regression model for analyzing longitudinal binary process data with emphasis on dealing with missingness we focus on the settings where data are missing at random mar which require a correctly specified joint distribution for the repeated measures in order to draw valid likelihood based inference about the marginal mean to provide maximum flexibility the proposed model specifies both the marginal mean and serial dependence structures using nonparametric smooth functions serial dependence is allowed to depend on the time lag between adjacent outcomes as well as other relevant covariates inference is fully bayesian using simulations we show that adequate modeling of the serial dependence structure is necessary for valid inference of the marginal mean when the binary process data are mar longitudinal viral load data from the hiv epidemiology research study are analyzed for illustration

characteristic boundary conditions in the lattice boltzmann method for fluid and gas dynamics for numerically solving fluid dynamics problems efficiently one is often facing the problem of having to confine the computational domain to a small domain of interest introducing so called non reflecting boundary conditions nrbcs in this work we address the problem of supplying nrbcs in fluid simulations in two space dimensions using the lattice boltzmann method lbm so called characteristic boundary conditions are revisited and transferred to the framework of lattice boltzmann simulations numerical tests show clearly that the unwanted unphysical reflections can be reduced significantly by applying our newly developed methods hereby the key idea is to transfer and generalize thompson s boundary conditions originally developed for the nonlinear euler equations of gas dynamics to the setting of lattice boltzmann methods finally we give strong numerical evidence that the proposed methods possess a long time stability property

approximate inference in generalized linear mixed models statistical approaches to overdispersion correlated errors shrinkage estimation and smoothing of regression relationships may be encompassed within the framework of the generalized linear mixed model glmm given an unobserved vector of random effects observations are assumed to be conditionally independent with means that depend on the linear predictor through a specified link function and conditional variances that are specified by a variance function known prior weights and a scale factor the random effects are assumed to be normally distributed with mean zero and dispersion matrix depending on unknown variance components for problems involving time series spatial aggregation and smoothing the dispersion may be specified in terms of a rank deficient inverse covariance matrix approximation of the marginal quasi likelihood using laplace s method leads eventually to estimating equations based on penalized quasilikelihood or pql for the mean parameters and pseudo likelihood for the variances im

edge effects in axial arrays of circular rings a boundary value problem is solved for the current induced by any source in particular for an incident plane wave on any number of identical rings that are arranged to form an axisymmetric linear array the net aperture distribution along the finite array is approximated by a combination of three physically identifiable components a term from the related infinite array plus contributions from two independent edge effect waves that originate from each of the two ends however if individually resonant loops are closer than a critical spacing the entire structure exhibits a wild resonance and the simple approximation becomes invalid

eigenmodes and scattering by an axial array of tape rings source free and forced solutions for the electromagnetic fields in and around a periodic array of finite length conducting cylinders are assembled from a galerkin procedure using a chebyshev polynomial basis with built in edge condition behavior a localized source excites slow surface waves under certain conditions on the electrical circumference and spacing of the tubular structure these eigenmodes are the solutions to the homogeneous boundary value problem a numerical search for the axial propagation constant that minimizes the smallest singular value of the governing galerkin matrix provides the required dispersion relation

fast analysis of large antenna arrays using the characteristic basis function method and the adaptive cross approximation algorithm the characteristic basis function method cbfm has been hybridized with the adaptive cross approximation aca algorithm to construct a reduced matrix equation in a time efficient manner and to solve electrically large antenna array problems in core with a solve time orders of magnitude less than those in the conventional methods various numerical examples are presented that demonstrate that the proposed method has a very good accuracy computational efficiency and reduced memory storage requirement specifically we analyze large 1 d and 2 d arrays of electrically interconnected tapered slot antennas tsas the entire computational domain is subdivided into many smaller subdomains each of which supports a set of characteristic basis functions cbfs we also present a novel scheme for generating the cbfs that do not conform to the edge condition at the truncated edge of each subdomain and provide a minor overlap between the cbfs in adjacent subdomains as a result the cbfs preserve the continuity of the surface current across the subdomain interfaces thereby circumventing the need to use separate ldquoconnectionrdquo basis functions

a truncated floquet wave diffraction method for the full wave analysis of large phased arrays i basic principles and 2 d cases this two part sequence deals with the formulation of an efficient method for the full wave analysis of large phased array antennas this is based on the method of moments mom solution of a fringe integral equation ie in which the unknown function is the difference between the exact solution of the finite array and that of the associated infinite array the unknown currents can be interpreted as produced by the field diffracted at the array edge which is excited by the floquet waves fws pertinent to the infinite configuration following this physical interpretation the unknown in the ie is efficiently represented by a very small number of basis functions with domain on the entire array aperture in order to illustrate the basic concepts the first part of this sequence deals with the two dimensional example of a linearly phased slit array it is shown that the dominant phenomenon fur describing the current perturbation with respect to the infinite array is accurately represented in most cases by only three diffracted ray shaped unknown functions this also permits a simple interpretation of the element by element current oscillation which was described by other authors

a likelihood based method for analysing longitudinal binary responses summary in this paper we discuss a likelihood based method for analysing correlated binary responses based on a multivariate model it is related to the pseudo maximum likelihood approach suggested recently by zhao prentice 1990 their parameterization results in a simple pairwise model in which the association between responses is modelled in terms of correlations while the present paper uses conditional log odds ratios with this approach higher order associations can be incorporated in a natural way one important advantage of this parameterization is that the maximum likelihood estimates of the marginal mean parameters are robust to misspecification of the time dependence we describe an iterative two stage procedure for obtaining the maximum likelihood estimates two examples are presented to illustrate this methodology

an eigencurrent approach for the analysis of finite antenna arrays an accurate description of typical finite array behavior such as edge effects and array resonances is essential in the design of various types of antennas the analysis approach proposed in this paper is essentially based on the concept of eigencurrents and is capable of describing finite array behavior in the approach numerical simulation is carried out first by computing element eigencurrents from chosen expansion functions and second by expanding a limited set of array eigencurrents in terms of element eigencurrents that contribute to the mutual coupling in the array both types of eigencurrents are eigenfunctions of an impedance operator that relates the current to the excitation field highlighting both mathematical and physical features we describe the basic concepts of the approach in particular the relation between eigenvalues and mutual coupling we illustrate these features for uniform linear arrays of loops and dipoles and demonstrate that the approach provides significant improvements in terms of computation time and memory use

marginalized models for longitudinal ordinal data with application to quality of life studies random effects are often used in generalized linear models to explain the serial dependence for longitudinal categorical data marginalized random effects models mrems for the analysis of longitudinal binary data have been proposed to permit likelihood based estimation of marginal regression parameters in this paper we introduce an extension of the mrem to accommodate longitudinal ordinal data maximum marginal likelihood estimation is implemented utilizing quasi newton algorithms with monte carlo integration of the random effects our approach is applied to analyze the quality of life data from a recent colorectal cancer clinical trial dropout occurs at a high rate and is often due to tumor progression or death to deal with progression death we use a mixture model for the joint distribution of longitudinal measures and progression death times and principal stratification to draw causal inferences about survivors

characteristic nonreflecting boundary conditions for open boundaries in lattice boltzmann methods a boundary condition for lattice boltzmann methods based on the movement of information through euler characteristic directions is developed with respect to the similar conditions used in finite difference or finite volume implementations some corrections are needed to compensate the isothermal compressible nature of standard lattice boltzmann methods for fluid flow the results show that the proposed method for inlets and outlets is highly nonreflecting and mass conserving

analysis of semiparametric regression models for repeated outcomes in the presence of missing data abstract we propose a class of inverse probability of censoring weighted estimators for the parameters of models for the dependence of the mean of a vector of correlated response variables on a vector of explanatory variables in the presence of missing response data the proposed estimators do not require full specification of the likelihood they can be viewed as an extension of generalized estimating equations estimators that allow for the data to be missing at random but not missing completely at random these estimators can be used to correct for dependent censoring and nonrandom noncompliance in randomized clinical trials studying the effect of a treatment on the evolution over time of the mean of a response variable the likelihood based parametric g computation algorithm estimator may also be used to attempt to correct for dependent censoring and nonrandom noncompliance but because of possible model misspecification the parametric g computation algorithm estimator in contrast with the proposed w

logistic regression for autocorrelated data with application to repeated measures summary a stochastic model is proposed for the study of the influence of time dependent covariates on the marginal distribution of the binary response in serially correlated binary data markov chains are expressed in terms of transitional rather than marginal probabilities we show how to construct the model so that the covariates relate only to the mean value of the process independently of the association parameter after formulating the stochastic model for a simple sequence of data with possibly missing data the same approach is applied to a repeated measures setting and illustrated with a real data example

marginalized transition models and likelihood inference for longitudinal categorical data summary marginal generalized linear models are now frequently used for the analysis of longitudinal data semiparametric inference for marginal models was introduced by liang and zeger 1986 biometrics73 13 22 this article develops a general parametric class of serial dependence models that permits likelihood based marginal regression analysis of binary response data the methods naturally extend the first order markov models of azzalini 1994 biometrika81 767 775 and prove computationally feasible for long series

marginal modeling of correlated ordinal data using a multivariate plackett distribution abstract an extension of the bivariate model suggested by dale is proposed for the analysis of dependent ordinal categorical data the so called multivariate dale model is constructed by first generalizing the bivariate plackett distribution to any dimensions because the approach is likelihood based it satisfies properties that are not fulfilled by other popular methods such as the generalized estimating equations approach the proposed method models both the marginal and the association structure in a flexible way the attractiveness of the multivariate dale model is illustrated in three key examples covering areas such as crossover trials longitudinal studies with patients dropping out from the study and discriminant analysis applications the differences and similarities with the generalized estimating approach are highlighted

concept for a one dimensional discrete artificial boundary condition for the lattice boltzmann method this article deals with artificial boundaries which you encounter when a large spatial domain is confined to a smaller computational domain such an artificial boundary condition should not preferably interact with the fluid at all standard boundary conditions e g a pressure or velocity condition result in unphysical reflections so far existing artificial boundary conditions for the lattice boltzmann method lbm are transferred from macroscopic formulations in this work we propose novel discrete artificial boundary conditions abcs which are tailored on the lbm s mesoscopic level they are derived directly for the chosen lbm with the aim of higher accuracy we describe the idea of discrete abcs in a three velocity d1q3 model governing the navier stokes equations in one dimension numerical results finally demonstrate the superiority of our new boundary condition in terms of accuracy compared to previously used abcs

modeling of multivariate monotone disease processes in the presence of misclassification motivated by a longitudinal oral health study the signal tandmobiel study we propose a multivariate binary inhomogeneous markov model in which unobserved correlated response variables are subject to an unconstrained misclassification process and have a monotone behavior the multivariate baseline distributions and markov transition matrices of the unobserved processes are defined as a function of covariates through the specification of compatible full conditional distributions distinct misclassification models are discussed in all cases the possibility that different examiners were involved in the scoring of the responses of a given subject across time is taken into account a full bayesian implementation of the model is described and its performance is evaluated using simulated data we provide theoretical and empirical evidence that the parameters can be estimated without any external information about the misclassification parameters finally the analyses of the motivating study are presented a

longitudinal data analysis using generalized linear models summary this paper proposes an extension of generalized linear models to the analysis of longitudinal data we introduce a class of estimating equations that give consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence the estimating equations are derived without specifying the joint distribution of a subject s observations yet they reduce to the score equations for multivariate gaussian outcomes asymptotic theory is presented for the general class of estimators specific cases in which we assume independence m dependence and exchangeable correlation structures from each subject are discussed efficiency of the proposed estimators in two simple situations is considered the approach is closely related to quasi likelih ood some key ironh estimating equation generalized linear model longitudinal data quasi likelihood repeated measures

an approximate marginal logistic distribution for the analysis of longitudinal ordinal data subject specific and marginal models have been developed for the analysis of longitudinal ordinal data subject specific models often lack a population average interpretation of the model parameters due to the conditional formulation of random intercepts and slopes marginal models frequently lack an underlying distribution for ordinal data in particular when generalized estimating equations are applied to overcome these issues latent variable models underneath the ordinal outcomes with a multivariate logistic distribution can be applied in this article we extend the work of o brien and dunson 2004 who studied the multivariate t distribution with marginal logistic distributions we use maximum likelihood instead of a bayesian approach and incorporated covariates in the correlation structure in addition to the mean model we compared our method with gee and demonstrated that it performs better than gee with respect to the fixed effect parameter estimation when the latent variables have an approximately elliptical distribution and at least as good as gee for other types of latent variable distributions