Search

We propose a new class of state space models for longitudinal discrete response data where the observation equation is specified in an additive form involving both deterministic and random linear predictors. These models allow us to explicitly address the effects of trend, seasonal or other time-varying covariates while preserving the power of state space models in modeling serial dependence in the data. We develop a Markov chain Monte Carlo algorithm to carry out statistical inference for models with binary and binomial responses, in which we invoke de Jong and Shephard’s (Biometrika 82(2):339–350, 1995) simulation smoother to establish an efficient sampling procedure for the state variables. To quantify and control the sensitivity of posteriors on the priors of variance parameters, we add a signal-to-noise ratio type parameter in the specification of these priors. Finally, we illustrate the applicability of the proposed state space mixed models for longitudinal binomial response data in both simulation studies and data examples.

A kidney paired donation (KPD) pool consists of transplant candidates and their incompatible donors along with non-directed donors (NDDs). In a match run, exchanges are arranged among pairs in the pool via cycles, as well as chains created from NDDs. A problem of importance is how to arrange cycles and chains to optimize the number of transplants. We outline and examine, through example and by simulation, four schemes for selecting potential matches in a realistic model of a KPD system; our proposed schemes take account of probabilities that chosen transplants may not be completed as well as allowing for contingency plans when the optimal solution fails. Using data on candidate/donor pairs and NDDs from the Alliance for Paired Donation, the simulations extend over 8 match runs, with 30 pairs and 1 NDD added between each run. Schemes that incorporate uncertainties and fallbacks into the selection process yield substantially more transplants on average, increasing the number of transplants by as much as 40% compared to a standard selection scheme. The gain depends on the degree of uncertainty in the system. The proposed approaches can be easily implemented and provide substantial advantages over current KPD matching algorithms.

It has been a long history of using interactions in regression analysis to investigate alterations in covariate-effects on response variables. In this article, we aim to address two kinds of new challenges arising from the inclusion of such high-order effects in the regression model for complex data. The first kind concerns a situation where interaction effects of individual covariates are weak but those of combined covariates are strong, and the other kind pertains to the presence of nonlinear interactive effects directed by low-effect covariates. We propose a new class of semiparametric models with varying index coefficients, which enables us to model and assess nonlinear interaction effects between grouped covariates on the response variable. As a result, most of the existing semiparametric regression models are special cases of our proposed models. We develop a numerically stable and computationally fast estimation procedure using both profile least squares method and local fitting. We establish both estimation consistency and asymptotic normality for the proposed estimators of index coefficients as well as the oracle property for the nonparametric function estimator. In addition, a generalized likelihood ratio test is provided to test for the existence of interaction effects or the existence of nonlinear interaction effects. Our models and estimation methods are illustrated by simulation studies, and by an analysis of child growth data to evaluate alterations in growth rates incurred by mother’s exposures to endocrine disrupting compounds during pregnancy. Supplementary materials for this article are available online.

The Patient Reported Outcomes Measurement Information System (PROMIS) II is a prospective study that evaluates patient reported outcomes in pediatric chronic diseases as a measure of health-related quality of life (HRQOL). We have evaluated the influence of disease duration on HRQOL and, for the first time, compared the findings of the PROMIS measures to those of the PedsQL™ 4.0 Generic Scales (PedsQL) from the PROMIS II nephrotic syndrome (NS) longitudinal cohort.This was a prospective study in which 127 children (age range 8-17 years) with active NS from 14 centers were enrolled. Children with active NS defined as the presence of nephrotic range proteinuria (2+ urinalysis and edema or urine protein/creatinine ratio2 g/g) were eligible. Comparisons were made between children with prevalent (N = 67) and incident (N = 60) disease at the study enrollment visit.The PROMIS scores were worse in prevalent patients in the domains of peer relationship (p = 0.01) and pain interference (p 0.01). The PedsQL showed worse scores in prevalent patients for social functioning (p 0.01) and school functioning (p = 0.03). Multivariable analyses showed that prevalent patients had worse scores in PROMIS pain interference (p = 0.02) and PedsQL social functioning (p 0.01).The PROMIS measures detected a significant impact of disease duration on HRQOL in children, such that peer relationships were worse and pain interfered with daily life to a greater degree among those with longer disease duration. These findings were in agreement with those for similar domains in the PedsQL legacy instrument.

The authors define a class of "partially linear single-index" survival models that are more flexible than the classical proportional hazards regression models in their treatment of covariates. The latter enter the proposed model either via a parametric linear form or a nonparametric single-index form. It is then pos sible to model both linear and functional effects of covariates on the logarithm of the hazard function and if necessary, to reduce the dimensionality of multiple covariates via the single-index component. The par tially linear hazards model and the single-index hazards model are special cases of the proposed model. The authors develop a likelihood-based inference to estimate the model components via an iterative algorithm. They establish an asymptotic distribution theory for the proposed estimators, examine their finite-sample behaviour through simulation, and use a set of real data to illustrate their approach. Une classe de modeles de survie partiellement lineaires a indice simple Rgsum=: Les auteurs d6finissent une classe de modeles de survie dits "partiellement lineaires a indice simple" qui s'averent plus flexibles que les modeles de regression a risques proportionnels classiques dans le traitement des covariables. Ces dernieres entrent dans le modele propose soit sous une forme lin6aire param6trique, soit sous une forme a indice simple non parametrique. I1 est alors possible de modeliser a la fois des effets lineaires et fonctionnels de covariables sur le logarithme du risque, et de reduire au besoin la dimension de covariables multiples par l'intermediaire de la composante a indice simple. Les modeles a risques partiellement lineaires et a indice simple sont des cas speciaux du modele propose. Les auteurs developpent une methode d'inf6rence vraisemblantiste pour l'estimation des composantes du modele au moyen d'un algorithme it6ratif. Ils d6terminent la loi asymptotique des estimateurs proposes, en etudient le comportement a taille finie par voie de simulation et illustrent leur approche a l'aide de donnees reelles.

We introduce a novel statistical procedure for clustering categorical data based on Hamming distance (HD) vectors. The proposed method is conceptually simple and computationally straightforward, because it does not require any specific statistical models or any convergence criteria. Moreover, unlike most currently existing algorithms that compute the class membership or membership probability for every data point at each iteration, our algorithm sequentially extracts clusters from the given dataset. That is, at each iteration our algorithm strives to identify only one cluster, which will then be deleted from the dataset at the next iteration; this procedure repeats until there are no more significant clusters in the remaining data. Consequently, the number of clusters can be determined automatically by the algorithm. As for the identification and extraction of a cluster, we first locate the cluster center by using a Pearson chi-squared–type statistic on the basis of HD vectors. The partition of the datase...

This article presents and examines a new algorithm for solving a score equation for the maximum likelihood estimate in certain problems of practical interest. The method circumvents the need to compute second-order derivatives of the full likelihood function. It exploits the structure of certain models that yield a natural decomposition of a very complicated likelihood function. In this decomposition, the first part is a log-likelihood from a simply analyzed model, and the second part is used to update estimates from the first part. Convergence properties of this iterative (fixed-point) algorithm are examined, and asymptotics are derived for estimators obtained using only a finite number of iterations. Illustrative examples considered in the article include multivariate Gaussian copula models, nonnormal random-effects models, generalized linear mixed models, and state-space models. Properties of the algorithm and of estimators are evaluated in simulation studies on a bivariate copula model and a nonnormal...

A class of autoregressive moving-average (ARMA) models proposed by Jorgensen and Song [Journal of Applied Probability (1998), vol. 35, pp. 78–92] with exponential dispersion model margins are useful to deal with non-normal stationary time series with high-order autocorrelation. One property associated with the class of models is that the projection process takes the exact form of the classical Box and Jenkins ARMA representation, leading to considerable ease to establish theories. This paper focuses on the issue of parameter estimation for such models, which has not been thoroughly investigated in Jorgensen and Song's paper. The key of the proposed approach is to treat the residual process associated with the projection essentially as a measurement error, which enables us to formulate directly an ARMA representation for the observed time series. The parameter estimation therefore becomes straightforward using the existing methods for the Box and Jenkins ARMA models such as the quasi-likelihood method. The approach is illustrated by simulation studies and by an analysis of myoclonic seizure counts.

This paper presents and examines a new algorithm for solving a score equation for the maximum likelihood estimate in certain problems of practical interest. The method circumvents the need to compute second order derivatives of the full likelihood function. It exploits the structure of certain models that yield a natural decomposition of a very complicated likelihood function. In this decomposition, the first part is a log likelihood from a simply analyzed model and the second part is used to update estimates from the first. Convergence properties of this fixed point algorithm are examined and asymptotics are derived for estimators obtained by using only a finite number of steps. Illustrative examples considered in the paper include bivariate and multivariate Gaussian copula models, nonnormal random effects and state space models. Properties of the algorithm and of estimators are evaluated in simulation studies on a bivariate copula model and a nonnormal linear random effects model.

Summary Continuous proportional data is common in biomedical research, e.g., the pre-post therapy percent change in certain physiological and molecular variables such as glomerular filtration rate, certain gene expression level, or telomere length. As shown in (Song and Tan, 2000) such data requires methods beyond the common generalised linear models. However, the original marginal simplex model of (Song and Tan, 2000) for such longitudinal continuous proportional data assumes a constant dispersion parameter. This assumption of dispersion homogeneity is imposed mainly for mathematical convenience and may be violated in some situations. For example, the dispersion may vary in terms of drug treatment cohorts or follow-up times. This paper extends their original model so that the heterogeneity of the dispersion parameter can be assessed and accounted for in order to conduct a proper statistical inference for the model parameters. A simulation study is given to demonstrate that statistical inference can be seriously affected by mistakenly assuming a varying dispersion parameter to be constant in the application of the available GEEs method. In addition, residual analysis is developed for checking various assumptions made in the modelling process, e.g., assumptions on error distribution. The methods are illustrated with the same eye surgery data in (Song and Tan, 2000) for ease of comparison.

In the presence of data contamination or outliers, some empirical studies have indicated that the two methods of generalised estimating equations and quadratic inference functions appear to have rather different robustness behaviour. This paper presents a theoretical investigation from the perspective of the influence function to identify the causes for the difference. We show that quadratic inference functions lead to bounded influence functions and the corresponding M-estimator has a redescending property, but the generalised estimating equation approach does not. We also illustrate that, unlike generalised estimating equations, quadratic inference functions can still provide consistent estimators even if part of the data is contaminated. We conclude that the quadratic inference function is a preferable method to the generalised estimating equation as far as robustness is concerned. This conclusion is supported by simulations and real-data examples.

This article proposes stochastic conditional duration (SCD) models with ‘‘leverage effect’’ for financial transaction data, which extends both the autoregressive conditional duration (ACD) model (Engle and Russell, 1998, Econometrica, 66, 1127–1162) and the existing SCD model (Bauwens and Veredas, 2004, Journal of Econometrics, 119, 381–412). The proposed models belong to a class of linear nongaussian state-space models, where the observation equation for the duration process takes an additive form of a latent process and a noise term. The latent process is driven by an autoregressive component to characterize the transition property and a term associated with the observed duration. The inclusion of such a term allows the model to capture the asymmetric behavior or ‘‘leverage effect’’ of the expected duration. The Monte Carlo maximum-likelihood (MCML) method is employed for consistent and efficient parameter estimation with applications to the transaction data of IBM and other stocks. Our analysis suggests that trade intensity is correlated with stock return volatility and modeling the duration process with ‘‘leverage effect’’ can enhance the forecasting performance of intraday volatility.

SUMMARY We address the matter of determining whether or not missing data in longitudinal studies are ignorable with regard to quasilikelihood or estimating equations approaches. This involves testing for whether or not the zero-mean property of estimating equations holds true. Chen & Little (1999) proposed testing for significant differences among parameter estimators calculated from sample subsets with different patterns of missing data, whereas we propose a more unified generalised score-type test. This avoids exhaustive estimation of parameters for each missing-data pattern, testing instead with a single quadratic score test statistic whether or not there is a common parameter under which the means of all the pattern-specific estimating equations are zero. Comparisons are made for the two approaches with both simulations and real data examples.

The author studies state space models for multivariate binomial time series, focussing on the development of the Kalman filter and smoothing for state variables. He proposes a Monte Carlo approach employing the latent variable representation which transplants the classical Kalman filter and smoothing developed for Gaussian state space models to discrete models and leads to a conceptually simple and computationally convenient approach. The method is illustrated through simulations and concrete examples. L'auteur s'interesse aux modeles a espace d'etats pour des chroniques binomiales multivariees, et plus particulierement au developpement du filtre de Kalman et au lissage pour les variables d'etat. Il propose une approche de Monte-Carlo exploitant la representation des variables latentes, laquelle permet d'adapter aux modeles discrets le filtre de Kalman et le lissage classiques des modeles a espace d'etats gaussiens, ce qui conduit a une approche simple, tant au plan conceptuel que calculatoire. La methode est illustree au moyen de simulations et d'exemples concrets.

In this paper a class of multivariate dispersion models generated from the multivariate Gaussian copula is presented. Being a multivariate extension of Jorgensen's (1987a) dispersion models, this class of multivariate models is parametrized by marginal position, dispersion and dependence parameters, producing a large variety of multivariate discrete and continuous models including the multivariate normal as a special case. Properties of the multivariate distributions are investigated, some of which are similar to those of the multivariate normal distribution, which makes these models potentially useful for the analysis of correlated non-normal data in a way analogous to that of multivariate normal data. As an example, we illustrate an application of the models to the regression analysis of longitudinal data, and establish an asymptotic relationship between the likelihood equation and the generalized estimating equation of Liang & Zeger (1986).

Continuous proportional data arise when the response of interest is a percentage between zero and one, e.g., the percentage of decrease in renal function at different follow-up times from the baseline. In this paper, we propose methods to directly model the marginal means of the longitudinal proportional responses using the simplex distribution of Barndorff-Nielsen and Jørgensen that takes into account the fact that such responses are percentages restricted between zero and one and may as well have large dispersion. Parameters in such a marginal model are estimated using an extended version of the generalized estimating equations where the score vector is a nonlinear function of the observed response. The method is illustrated with an ophthalmology study on the use of intraocular gas in retinal repair surgeries.

Incomplete repeated measurement data often arise in medical studies. A problem that has recently drawn much attention in the literature in this situation is that the incompleteness or missingness is informative or related to the underlying variable of interest. In this paper we propose a non-parametric global test for treatment comparison in the presence of informative incompleteness. A semi-parametric regression model is also presented for assessing conditional treatment effects given the drop-out patterns, adopting the idea similar to that behind the pattern-mixture modelling approach and discussed in Shih and Quan. The proposed methods can be easily implemented and are conceptually simple and similar too, but can be applied to more general cases than those given in Yao et al. They are evaluated by numerical studies and applied to data from a clinical trial of adult schizophrenics.

In the context of regression rnodels with random effects, repeated response are traditionally assumed to be mutually independent conditional on the random effects. In order to asseess the validity of such an assumption and its impact on parameter inference, we propose an estimating equation methodology where both random eifects and within-subject correlation are modeled. This fllows a subsequent analysis on the statistical sianificance of the conditional correlation. We illustrate this method with the epilepsy data of Thall and Vail (1990), and find our method useh in a proper representation for khe random effect modeling.

SUMMARY We propose a nonstationary state space model for multivariate longitudinal count data driven by a latent gamma Markov process. The Poisson counts are assumed to be conditionally independent given the latent process, both over time and across categories. We consider a regression model where time-varying covariates may enter via either the Poisson model or the latent gamma process. Estimation is based on the Kalman smoother, and we consider analysis of residuals from both the Poisson model and the latent process. A reanalysis of Zeger's (1988) polio data shows that the choice between a stationary and nonstationary model is crucial for the correct assessment of the evidence of a long-term decrease in the rate of U.S. polio infection.

We consider a class of stationary infinite-order moving average processes with margins in the class of infinitely divisible exponential dispersion models. The processes are constructed by means of the thinning operation of Joe (1996), generalizing the binomial thinning used by McKenzie (1986, 1988) and Al-Osh and Alzaid (1987) for integer-valued time series. As a special case we obtain a class of autoregressive moving average processes that are different from the ARMA models proposed by Joe (1996). The range of possible marginal distributions for the new models is extensive and includes all infinitely divisible distributions with finite moment generating functions, hereunder many known discrete, continuous and mixed distributions.

In this paper we present a unified approach to generating dependent random numbers with given correlation structures and margins from the family of exponential dispersion models (Jorgensen, 1987), generalizing the methods proposed in Sim (1993) where only two distributions, Poisson and gamma, are discussed. This algorithm can accomodate many marginal distributions including normal, Poisson, gamma, negative binomial, inverse Gaussian and compound Poisson. The key to the unification of the simulation procedure is the so-called thinning operation proposed in Joe (1996). Some sufficient conditions for the algorithm are investigated. The algorithm is illustrated by some special cases with four given margins-normal, Poisson, gamma and negative binomial and four types of correlation structures-independencem-dependence, exchangeability and autocorrelation. This simulation procedure is particularly recommended to generate longitudinal data.

Model systems demonstrate that progression to ESRD is driven by progressive podocyte depletion (the podocyte depletion hypothesis) and can be noninvasively monitored through measurement of urine pellet podocyte mRNAs. To test these concepts in humans, we analyzed urine pellet mRNAs from 358 adult and pediatric kidney clinic patients and 291 controls (n=1143 samples). Compared with controls, urine podocyte mRNAs increased 79-fold (P

This paper concerns goodness-of-fit test for semiparametric copula models. Our contribution is two-fold: we first propose a new test constructed via the comparison between "in-sample" and "out-of-sample" pseudolikelihoods, which avoids the use of any probability integral transformations. Under the null hypothesis that the copula model is correctly specified, we show that the proposed test statistic converges in probability to a constant equal to the dimension of the parameter space and establish the asymptotic normality for the test. Second, we introduce a hybrid mechanism to combine several test statistics, so that the resulting test will make a desirable test power among the involved tests. This hybrid method is particularly appealing when there exists no single dominant optimal test. We conduct comprehensive simulation experiments to compare the proposed new test and hybrid approach with the best "blank test" shown in Genest et al. (2009). For illustration, we apply the proposed tests to analyze three real datasets.

Objectives. We assessed changes in asthma-related health care use by low-income children in communities across the country where 6 Allies Against Asthma coalitions (Hampton Roads, VA; Washington, DC; Milwaukee, WI; King County/Seattle, WA; Long Beach, CA; and Philadelphia, PA) mobilized stakeholders to bring about policy changes conducive to asthma control. Methods. Allies intervention zip codes were matched with comparison communities by median household income, asthma prevalence, total population size, and race/ethnicity. Five years of data provided by the Center for Medicare and Medicaid Services on hospitalizations, emergency department (ED) use, and physician urgent care visits for children were analyzed. Intervention and comparison sites were compared with a stratified recurrent event analysis using a Cox proportional hazard model. Results. In most of the assessment years, children in Allies communities were significantly less likely (P Conclusions. Mobilizing a diverse group of stakeholders, and focusing on policy and system changes generated significant reductions in health care use for asthma in vulnerable communities.

Background and objectivesNephrotic syndrome (NS) represents a common disease in pediatric nephrology typified by a relapsing and remitting course and characterized by the presence of edema that can significantly affect the health-related quality of life in children and adolescents. The PROMIS pediatric measures were constructed to be publically available, efficient, precise, and valid across a variety of diseases to assess patient reports of symptoms and quality of life. This study was designed to evaluate the ability of children and adolescents with NS to complete the PROMIS assessment via computer and to initiate validity assessments of the short forms and full item banks in pediatric NS. Successful measurement of patient reported outcomes will contribute to our understanding of the impact of NS on children and adolescents.DesignThis cross-sectional study included 151 children and adolescents 8-17 years old with NS from 16 participating institutions in North America. The children completed the PROMIS pediatric depression, anxiety, social-peer relationships, pain interference, fatigue, mobility and upper extremity functioning measures using a web-based interface. Responses were compared between patients experiencing active NS (n = 53) defined by the presence of edema and patients with inactive NS (n = 96) defined by the absence of edema.ResultsAll 151 children and adolescents were successfully able to complete the PROMIS assessment via computer. As hypothesized, the children and adolescents with active NS were significantly different on 4 self-reported measures (anxiety, pain interference, fatigue, and mobility). Depression, peer relationships, and upper extremity functioning were not different between children with active vs. inactive NS. Multivariate analysis showed that the PROMIS instruments remained sensitive to NS disease activity after adjusting for demographic characteristics.ConclusionsChildren and adolescents with NS were able to successfully complete the PROMIS instrument using a web-based interface. The computer based pediatric PROMIS measurement effectively discriminated between children and adolescents with active and inactive NS. The domain scores found in this study are consistent with previous reports investigating the health-related quality of life in children and adolescents with NS. This study establishes known-group validity and feasibility for PROMIS pediatric measures in children and adolescents with NS.

In this chapter we introduce a class of multi-dimensional regression models for vector outcomes, termed as the vector generalized linear models (VGLMs), which is a multivariate analogue of the univariate generalized linear models (GLMs). A unified framework of such regression models is established with the utility of Gaussian copula, accommodating discrete, continuous and mixed vector outcomes. Both full likelihood and composite likelihood estimations and inferences are discussed. A Gauss–Newton type algorithm is suggested to carry out the simultaneous estimation for all model parameters. Numerical illustrations are focused on VGLMs for correlated binary outcomes, correlated count outcomes, and mixed normal and binary outcomes. In the simulation studies, we compare the VGLM to the popular generalized estimating equations (GEEs) approach. The simulation results indicate that the VGLMs provide more efficient inference for the regression coefficients than the GEEs. The VGLM is also illustrated via real-data examples.

We propose a class of state-space models for multivariate longitudinal data where the components of the response vector may have different distributions. The approach is based on the class of Tweedie exponential dispersion models, which accommodates a wide variety of discrete, continuous and mixed data. The latent process is assumed to be a Markov process, and the observations are conditionally independent given the latent process, over time as well as over the components of the response vector. This provides a fully parametric alternative to the quasilikelihood approach of Liang and Zeger. We estimate the regression parameters for time-varying covariates entering either via the observation model or via the latent process, based on an estimating equation derived from the Kalman smoother. We also consider analysis of residuals from both the observation model and the latent process. Nous proposons une classe de modeles pour des donnees longitudinales multivariees dont les composantes du vecteur de reponse peuvent avoir differentes distributions. L'approche est basee sur la classe des modeles exponentiels de dispersion de Tweedie, qui s'adapte a une grande variete de donnees, soit discretes, soit continues, soit mixtes. On suppose que le processus sous-jacent est markovien et que, en conditionnant sur ce processus, les observations sont independantes, a la fois dans le temps et dans les composantes du vecteur de reponse. Ceci constitue une alternative totalement parametrique a l'approche de quasi-vraisemblance de Liang et Zeger. Nous estimons les parametres de regression pour les covariables, qui varient dans le temps et qui interviennent dans le modele d'observation ainsi que dans le processus sous-jacent. Cette estimation est basee sur une equation derivee du regularisateur de Kalman. Nous etudions agalement les residus, pour le modele d'observation et pour le processus sous-jacent.

Spatial-clustered data refer to high-dimensional correlated measurements collected from units or subjects that are spatially clustered. Such data arise frequently from studies in social and health sciences. We propose a unified modeling framework, termed as GeoCopula, to characterize both large-scale variation, and small-scale variation for various data types, including continuous data, binary data, and count data as special cases. To overcome challenges in the estimation and inference for the model parameters, we propose an efficient composite likelihood approach in that the estimation efficiency is resulted from a construction of over-identified joint composite estimating equations. Consequently, the statistical theory for the proposed estimation is developed by extending the classical theory of the generalized method of moments. A clear advantage of the proposed estimation method is the computation feasibility. We conduct several simulation studies to assess the performance of the proposed models and estimation methods for both Gaussian and binary spatial-clustered data. Results show a clear improvement on estimation efficiency over the conventional composite likelihood method. An illustrative data example is included to motivate and demonstrate the proposed method.

Older adults with asthma have worse quality of life, asthma control and increased health care utilization than do healthy peers. Factors that contribute to this are currently unknown.To identify demographic, psychological or physiologic characteristics associated with asthma quality of life, control and health care utilization in older adults.By using a cross-sectional design, subjects older than 65 years with a history of physician-diagnosed asthma were enrolled. Demographic, psychological and physiological characteristics (including spirometry, atopy testing and exhaled nitric oxide) were collected. Correlations between these factors and the mini Asthma Quality of Life Questionnaire, Asthma Control Questionnaire and health care utilization were assessed.Seventy subjects were enrolled in the study, with a mean age of 73.3 years and mean duration of asthma diagnosis of 28.5 years. Higher depression screening scores and self-reported depression were strongly correlated with poorer quality of life and asthma control after controlling for confounding effects through a regression model. In addition, worse overall functional status was correlated with poorer asthma quality of life (P.01), presence of atopy was associated with decreased health care utilization (P.01) and subjects who lived alone were more likely to have unscheduled visits to a physician's office (P = .06).Comorbid depression is strongly associated with poorer asthma quality of life and control in older adults. In addition, worse functional status and living alone may be associated with poorer outcomes. Screening for these conditions is important in the care of the elderly asthmatic population.

The objective of this study was to evaluate the healthcare utilization of hospitalized children with hypertension. The Healthcare Cost and Utilization Project Kids' Inpatient Database, years 1997, 2000, 2003, and 2006, was used to identify hypertension hospitalizations. We examined the association of patient and hospital characteristics on hypertension charges. Data from each cohort year were used to analyze trends in charges. We found that 71282 pediatric hypertension hospitalizations generated $3.1 billion in total charges from 1997 to 2006. Approximately 68% were 10 to 18 years old, 55% were boys, and 47% were white. Six percent of claims with a diagnosis code for hypertension also had a diagnosis code for end-stage renal disease or renal transplant. The frequency of hypertension discharges increased over time ( P =0.02 for each of age groups 2–9 years and 2–18 years; P =0.03 for age group 10–18 years), as well as the fraction of inpatient charges attributed to hypertension ( P P P =0.03, respectively). During the 10-year study period, the frequency of hypertension-associated hospitalizations was increasing across all of the age groups, and the fraction of charges related to hypertension was also increasing. The coexisting condition of end-stage renal disease resulted in a significant increase in healthcare charges.

Purpose Serum creatinine functions as a poor surrogate marker of renal allograft dysfunction and long-term graft survival. By measuring multiple proteins simultaneously in the serum of transplant patients, we can identify unique protein signatures of graft dysfunction. Experimental design We utilized training and validation cohorts composed of healthy and volunteer subjects, stable renal transplant patients, and renal transplant patients experiencing acute allograft rejection. Utilizing our antibody microarray, we measured 108 proteins simultaneously in these groups. Results Using Mann–Whitney tests with Bonferroni correction, we identified ten serum proteins from 19 renal transplant patients with stable renal function, which are differentially expressed, compared to healthy control subjects. In addition, we identified 17 proteins that differentiate rejecting renal transplant recipients from stable renal transplant. Validation cohorts substantiated these findings. Conclusion and clinical relevance Our preliminary results support that a specific pattern of protein expression or “protein signature” may be able to differentiate between stable transplant patients from those with rejection. Future studies will focus on other etiologies of renal allograft dysfunction and the effect of treatment on protein expression and long-term outcome.

Kidney transplantation is typically the most effective treatment for patients with end-stage renal disease. However, the supply of kidneys is far short of the fast-growing demand. Kidney paired donation (KPD) programs provide an innovative approach for increasing the number of available kidneys. In a KPD program, willing but incompatible donor-candidate pairs may exchange donor organs to achieve mutual benefit. Recently, research on exchanges initiated by altruistic donors (ADs) has attracted great attention because the resultant organ exchange mechanisms offer advantages that increase the effectiveness of KPD programs. Currently, most KPD programs focus on rule-based strategies of prioritizing kidney donation. In this paper, we consider and compare two graph-based organ allocation algorithms to optimize an outcome-based strategy defined by the overall expected utility of kidney exchanges in a KPD program with both incompatible pairs and ADs. We develop an interactive software-based decision support system to model, monitor, and visualize a conceptual KPD program, which aims to assist clinicians in the evaluation of different kidney allocation strategies. Using this system, we demonstrate empirically that an outcome-based strategy for kidney exchanges leads to improvement in both the quantity and quality of kidney transplantation through comprehensive simulation experiments.

In order to assist physicians and other health professionals for health care improvement, clinical decision support systems, through interactive computerized software, become very popular in clinical practice. The crisis associated with kidney organ shortage has triggered an innovative strategy, termed as Kidney Paired Donation (KPD) program, to address a rapidly expanding demand for donor kidneys. KPD program involves how to making optimal decision for allowing patients with incompatible living donors to receive compatible organs by best matching donors. Although some computerized optimization tools are being used in the current KPD program, there still lacks a general decision support system which enables us to evaluate and compare different kidney allocation strategies and effects of policy. In this paper, we discuss a general computer-based KPD decision model that appropriately reflects the real world clinical application. Also, the whole decision process is to be visualized by our Graphical User Interface (GUI) software, which offers a user friendly platform not only to provide a convenient interface for clinicians but also to assess different kidney exchange strategies of clinical importance.

In longitudinal biomedical studies, there is often interest in the rate functions, which describe the functional rates of change of biomarker profiles. This article proposes a semiparametric approach to model these functions as the realizations of stochastic processes defined by stochastic differential equations. These processes are dependent on the covariates of interest and vary around a specified parametric function. An efficient Markov chain Monte Carlo algorithm is developed for inference. The proposed method is compared with several existing methods in terms of goodness of fit and more importantly the ability to forecast future functional data in a simulation study. The proposed methodology is applied to prostate-specific antigen profiles for illustration. Supplementary materials for this article are available online.

A new parametric instability phenomenon characterized by infinitely compressed, shocklike waves with a bounded displacement and an unbounded vibratory energy is discovered in a translating string with a constant length and tension and a sinusoidally varying velocity. A novel method based on the wave solutions and the fixed point theory is developed to analyze the instability phenomenon. The phase functions of the wave solutions corresponding to the phases of the sinusoidal part of the translation velocity, when an infinitesimal wave arrives at the left boundary, are established. The period number of a fixed point of a phase function is defined as the number of times that the corresponding infinitesimal wave propagates between the two boundaries before the phase repeats itself. The instability conditions are determined by identifying the regions in a parameter plane where attracting fixed points of the phase functions exist. The period-1 instability regions are analytically obtained, and the period-i (i>1) instability regions are numerically calculated using bifurcation diagrams. The wave patterns corresponding to different instability regions are determined, and the strength of instability corresponding to different period numbers is analyzed.

Background. Patients with acute kidney injury (AKI) requiring initiation of renal replacement therapy (RRT) have poor short- and long-term outcomes, including the development of dialysis dependence. Currently, little is known about what factors may predict renal recovery in this population. Methods. We conducted a single-center, retrospective analysis of 170 hospitalized adult patients with AKI attributed to acute tubular necrosis who required inpatient initiation of RRT. Data collection included patient characteristics, laboratory data, details of hospital course and degree of fluid overload at RRT initiation. The primary outcome was recovery of renal function to dialysis independence. Results. Within 1 year of RRT initiation, 35.9% (61/170) of patients reached the primary end point of renal recovery. The median (interquartile range) duration of RRT was 11 (3–33) days and 83.6% (51/61) recovered prior to hospital discharge. Recovering patients had significantly less fluid overload at the time of RRT initiation compared to non-recovering patients (3.5 versus 9.3%, P ¼ 0.004). In multivariate Cox proportional hazard regression analysis, a rise in percent fluid overload at dialysis initiation remained a significant negative predictor of renal recovery (hazard ratio 0.97, 95% confidence interval 0.95–1.00, P ¼ 0.024). Conclusions. In patients with AKI, a higher degree of fluid overload at RRT initiation predicts worse renal recovery at 1 year. Clinical trials are needed to determine whether interventions targeting fluid overload may improve patient and renal outcomes.

A new parametric instability phenomenon characterized by infinitely compressed, shock-like waves with a bounded displacement and an unbounded vibratory energy is discovered in a translating string with a constant length and tension and a sinusoidally varying velocity. A novel method based on the wave solutions and the fixed point theory is developed to analyze the instability phenomenon. The phase functions of the wave solutions corresponding to the phases of the sinusoidal part of the translation velocity, when an infinitesimal wave arrives at the left boundary, are established. The period number of a fixed point of a phase function is defined as the number of times that the corresponding infinitesimal wave propagates between the two boundaries before the phase repeats itself. The instability conditions are determined by identifying the regions in a parameter plane where attracting fixed points of the phase functions exist. The period-1 instability regions are analytically obtained, and the period-i (i > 1) instability regions are numerically calculated using bifurcation diagrams. The wave patterns corresponding to different instability regions are determined, and the strength of instability corresponding to different period numbers is analyzed.Copyright © 2010 by ASME

The quadratic inference function (QIF) is a new statistical methodology developed for the estimation and inference in longitudinal data analysis using marginal models. This method is an alternative to the popular generalized estimating equations approach, and it has several useful properties such as robustness, a goodness-of-fit test and model selection. This paper presents an introductory review of the QIF, with a strong emphasis on its applications. In particular, a recently developed SAS MACRO QIF is illustrated in this paper to obtain numerical results.

This paper presents a new class of time-deformation (or stochastic volatility) models for stock returns sampled in transaction time and directed by a generalized duration process. Stochastic volatility in this model is driven by an observed duration process and a latent autoregressive process. Parameter estimation in the model is carried out by using the method of simulated moments (MSM) due to its analytical feasibility and numerical stability for the proposed model. Simulations are conducted to validate the choices of the moments used in the formulation of the MSM. Both the simulation and empirical results obtained in this paper indicate that this approach works well for the proposed model. The main empirical findings for the IBM transaction return data can be summarized as follows: (i) the return distribution conditional on the duration process is not Gaussian, even though the duration process itself can marginally function as a directing process; (ii) the return process is highly leveraged; (iii) a longer trade duration tends to be associated with a higher return volatility; and (iv) the proposed model is capable of reproducing return whose marginal density function is close to that of the empirical return.