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Data sharing barriers present paramount challenges arising from multicenter clinical studies where multiple data sources are stored and managed in a distributed fashion at different local study sites. Merging such data sources into a common data storage for a centralized statistical analysis requires a data use agreement, which is often time‐consuming. Data merging may become more burdensome when propensity score modeling is involved in the analysis because combining many confounding variables, and systematic incorporation of this additional modeling in a meta‐analysis has not been thoroughly investigated in the literature. Motivated from a multicenter clinical trial of basal insulin treatment for reducing the risk of post‐transplantation diabetes mellitus, we propose a new inference framework that avoids the merging of subject‐level raw data from multiple sites at a centralized facility but needs only the sharing of summary statistics. Unlike the architecture of federated learning, the proposed collaborative inference does not need a center site to combine local results and thus enjoys maximal protection of data privacy and minimal sensitivity to unbalanced data distributions across data sources. We show theoretically and numerically that the new distributed inference approach has little loss of statistical power compared to the centralized method that requires merging the entire data. We present large‐sample properties and algorithms for the proposed method. We illustrate its performance by simulation experiments and the motivating example on the differential average treatment effect of basal insulin to lower risk of diabetes among kidney‐transplant patients compared to the standard‐of‐care. [ABSTRACT FROM AUTHOR]

Longitudinal outcomes are prevalent in clinical studies, where the presence of missing data may make the statistical learning of individualized treatment rules (ITRs) a much more challenging task. We analyzed a longitudinal calcium supplementation trial in the ELEMENT Project and established a novel ITR to reduce the risk of adverse outcomes of lead exposure on child growth and development. Lead exposure, particularly in the form of in utero exposure, can seriously impair children's health, especially their cognitive and neurobehavioral development, which necessitates clinical interventions such as calcium supplementation intake during pregnancy. Using the longitudinal outcomes from a randomized clinical trial of calcium supplementation, we developed a new ITR for daily calcium intake during pregnancy to mitigate persistent lead exposure in children at age 3 years. To overcome the technical challenges posed by missing data, we illustrate a new learning approach, termed longitudinal self‐learning (LS‐learning), that utilizes longitudinal measurements of child's blood lead concentration in the derivation of ITR. Our LS‐learning method relies on a temporally weighted self‐learning paradigm to synergize serially correlated training data sources. The resulting ITR is the first of this kind in precision nutrition that will contribute to the reduction of expected blood lead concentration in children aged 0‐3 years should this ITR be implemented to the entire study population of pregnant women. [ABSTRACT FROM AUTHOR]

New data collection and storage technologies have given rise to a new field of streaming data analytics, called real‐time statistical methodology for online data analyses. Most existing online learning methods are based on homogeneity assumptions, which require the samples in a sequence to be independent and identically distributed. However, inter‐data batch correlation and dynamically evolving batch‐specific effects are among the key defining features of real‐world streaming data such as electronic health records and mobile health data. This article is built under a state‐space mixed model framework in which the observed data stream is driven by a latent state process that follows a Markov process. In this setting, online maximum likelihood estimation is made challenging by high‐dimensional integrals and complex covariance structures. In this article, we develop a real‐time Kalman‐filter‐based regression analysis method that updates both point estimates and their standard errors for fixed population average effects while adjusting for dynamic hidden effects. Both theoretical justification and numerical experiments demonstrate that our proposed online method has statistical properties similar to those of its offline counterpart and enjoys great computational efficiency. We also apply this method to analyze an electronic health record dataset. [ABSTRACT FROM AUTHOR]

Summary: Background: Alterations in body composition (BC) during adolescence relates to future metabolic risk, yet underlying mechanisms remain unclear. Objectives: To assess the association between the metabolome with changes in adiposity (body mass index [BMI], waist circumference [WC], triceps skinfold [TS], fat percentage [BF%]) and muscle mass (MM). Methods: In Mexican adolescents (n = 352), untargeted serum metabolomics was profiled at baseline. and data were reduced by pairing hierarchical clustering with confirmatory factor analysis, yielding 30 clusters with 51 singleton metabolites. At the baseline and follow‐up visits (1.6–3.5 years apart), anthropometry was collected to identify associations between baseline metabolite clusters and change in BC (∆) using seemingly unrelated and linear regression. Results: Between visits, MM increased in boys and adiposity increased in girls. Sex differences were observed between metabolite clusters and changes in BC. In boys, aromatic amino acids (AAA), branched chain amino acids (BCAA) and fatty acid oxidation metabolites were associated with increases in ∆BMI, and ∆BF%. Phospholipids were associated with decreases in ∆TS and ∆MM. Negative associations were observed for ∆MM in boys with a cluster including AAA and BCAA, whereas positive associations were found for a cluster containing tryptophan metabolites. Few associations were observed between metabolites and BC change in girls, with one cluster comprising methionine, proline and lipids associated with decreases in ∆BMI, ∆WC and ∆MM. Conclusion: Sex‐specific associations between the metabolome and change in BC were observed, highlighting metabolic pathways underlying adolescent physical growth. [ABSTRACT FROM AUTHOR]

As a guide to establishing a safe exposure level for fluoride exposure in pregnancy, we applied benchmark dose modeling to data from two prospective birth cohort studies. We included mother–child pairs from the Early Life Exposures in Mexico to Environmental Toxicants (ELEMENT) cohort in Mexico and the Maternal‐Infant Research on Environmental Chemicals (MIREC) cohort in Canada. Maternal urinary fluoride concentrations (U‐F, in mg/L, creatinine‐adjusted) were measured in urine samples obtained during pregnancy. Children were assessed for intelligence quotient (IQ) at age 4 (n = 211) and between six and 12 years (n = 287) in the ELEMENT cohort, and three to four years (n = 407) in the MIREC cohort. We calculated covariate‐adjusted regression coefficients and their standard errors to assess the association of maternal U‐F concentrations with children's IQ measures. Assuming a benchmark response of 1 IQ point, we derived benchmark concentrations (BMCs) and benchmark concentration levels (BMCLs). No deviation from linearity was detected in the dose–response relationships, but boys showed lower BMC values than girls. Using a linear slope for the joint cohort data, the BMC for maternal U‐F associated with a 1‐point decrease in IQ scores was 0.31 mg/L (BMCL, 0.19 mg/L) for the youngest boys and girls in the two cohorts, and 0.33 mg/L (BMCL, 0.20 mg/L) for the MIREC cohort and the older ELEMENT children. Thus, the joint data show a BMCL in terms of the adjusted U‐F concentrations in the pregnant women of approximately 0.2 mg/L. These results can be used to guide decisions on preventing excess fluoride exposure in pregnant women. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis (ALS) is a neurological disease that starts at a focal point and gradually spreads to other parts of the nervous system. One of the main clinical symptoms of ALS is muscle weakness. To study spreading patterns of muscle weakness, we analyze spatiotemporal binary muscle strength data, which indicates whether observed muscle strengths are impaired or healthy. We propose a hidden Markov model-based approach that assumes the observed disease status depends on two latent disease states. The model enables us to estimate the incidence rate of ALS disease and the probability of disease state transition. Specifically, the latter is modeled by a logistic autoregression in that the spatial network of susceptible muscles follows a Markov process. The proposed model is flexible to allow both historical muscle conditions and their spatial relationships to be included in the analysis. To estimate the model parameters, we provide an iterative algorithm to maximize sparse-penalized likelihood with bias correction, and use the Viterbi algorithm to label hidden disease states. We apply the proposed approach to analyze the ALS patients' data from EMPOWER Study. [ABSTRACT FROM AUTHOR]

Summary: Multi‐compartment models have been playing a central role in modelling infectious disease dynamics since the early 20th century. They are a class of mathematical models widely used for describing the mechanism of an evolving epidemic. Integrated with certain sampling schemes, such mechanistic models can be applied to analyse public health surveillance data, such as assessing the effectiveness of preventive measures (e.g. social distancing and quarantine) and forecasting disease spread patterns. This review begins with a nationwide macromechanistic model and related statistical analyses, including model specification, estimation, inference and prediction. Then, it presents a community‐level micromodel that enables high‐resolution analyses of regional surveillance data to provide current and future risk information useful for local government and residents to make decisions on reopenings of local business and personal travels. r software and scripts are provided whenever appropriate to illustrate the numerical detail of algorithms and calculations. The coronavirus disease 2019 pandemic surveillance data from the state of Michigan are used for the illustration throughout this paper. [ABSTRACT FROM AUTHOR]

Background: Alcohol intake increases the risk of developing colon cancer. Circadian disruption promotes alcohol's effect on colon carcinogenesis through unknown mechanisms. Alcohol's metabolites induce DNA damage, an early step in carcinogenesis. We assessed the effect of time of alcohol consumption on markers of tissue damage in the colonic epithelium. Methods: Mice were treated by alcohol or phosphate‐buffered saline (PBS), at 4‐hour intervals for 3 days, and their colons were analyzed for (i) proliferation (Ki67) and antiapoptosis (Bcl‐2) markers, (ii) DNA damage (γ‐H2AX), and (iii) the major acetaldehyde (AcH)–DNA adduct, N2‐ethylidene‐dG. To model circadian disruption, mice were shifted once weekly for 12 h and then were sacrificed at 4‐hour intervals. Samples of mice with a dysfunctional molecular clock were analyzed. The dynamics of DNA damage repair from AcH treatment as well as role of xeroderma pigmentosum, complementation group A (XPA) in their repair were studied in vitro. Results: Proliferation and survival of colonic epithelium have daily rhythmicity. Alcohol induced colonic epithelium proliferation in a time‐dependent manner, with a stronger effect during the light/rest period. Alcohol‐associated DNA damage also occurred more when alcohol was given at light. Levels of DNA adduct did not vary by time, suggesting rather lower repair efficiency during the light versus dark. XPA gene expression, a key excision repair gene, was time‐dependent, peaking at the beginning of the dark. XPA knockout colon epithelial cells were inefficient in repair of the DNA damage induced by alcohol's metabolite. Conclusions: Time of day of alcohol intake may be an important determinant of colon tissue damage and carcinogenicity. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The last two decades have witnessed an explosion in research focused on the development and assessment of novel biomarkers for improved prognosis of diseases. As a result, best practice standards guiding biomarker research have undergone extensive development. Currently, there is great interest in the promise of biomarkers to enhance research efforts and clinical practice in the setting of chronic kidney disease, acute kidney injury, and glomerular disease. However, some have questioned whether biomarkers currently add value to the clinical practice of nephrology. The current state of the art pertaining to statistical analyses regarding the use of such measures is critical. In December 2014, the National Institute of Diabetes and Digestive and Kidney Diseases convened a meeting, "Toward Building Better Biomarker Statistical Methodology," with the goals of summarizing the current best practice recommendations and articulating new directions for methodological research. This report summarizes its conclusions and describes areas that need attention. Suggestions are made regarding metrics that should be commonly reported. We outline the methodological issues related to traditional metrics and considerations in prognostic modeling, including discrimination and case mix, calibration, validation, and cost-benefit analysis. We highlight the approach to improved risk communication and the value of graphical displays. Finally, we address some "new frontiers" in prognostic biomarker research, including the competing risk framework, the use of longitudinal biomarkers, and analyses in distributed research networks. [ABSTRACT FROM AUTHOR]

Objective: The goal of this study was to identify metabolites associated with metabolic risk, separately by sex, in Mexican adolescents.Methods: Untargeted metabolomic profiling was carried out on fasting serum of 238 youth aged 8 to 14 years, and metabolites associated with a metabolic syndrome risk z-score (MetRisk z-score) were identified separately for boys and girls, using the simulation and extrapolation algorithm. Associations of each metabolite with MetRisk z-score were examined using linear regression models that accounted for maternal education, child's age, and pubertal status.Results: Of the 938 features identified in metabolomics analysis, 7 named compounds (of 27 identified metabolites) were associated with MetRisk z-score in girls, and 3 named compounds (of 14 identified) were associated with MetRisk z-score in boys. In girls, diacylglycerol (DG) 16:0/16:0, 1,3-dielaidin, myo-inositol, and urate corresponded with higher MetRisk z-score, whereas N-acetylglycine, thymine, and dodecenedioic acid were associated with lower MetRisk z-score. For example, each z-score increment in DG 16:0/16:0 corresponded with 0.60 (95% CI: 0.47-0.74) units higher MetRisk z-score. In boys, positive associations of DG 16:0/16:0, tyrosine, and 5'-methylthioadenosine with MetRisk z-score were found.Conclusions: Metabolites on lipid, amino acid, and carbohydrate metabolism pathways are associated with metabolic risk in girls. Compounds on lipid and DNA pathways correspond with metabolic risk in boys. [ABSTRACT FROM AUTHOR]

Diagnostics for heteroscedasticity in linear regression models have been intensively investigated in the literature. However, limited attention has been paid on how to identify covariates associated with heteroscedastic error variances. This problem is critical in correctly modelling the variance structure in weighted least squares estimation, which leads to improved estimation efficiency. We propose covariate-specific statistics based on information ratios formed as comparisons between the model-based and sandwich variance estimators. A two-step diagnostic procedure is established, first to detect heteroscedasticity in error variances, and then to identify covariates the error variance structure might depend on. This proposed method is generalized to accommodate practical complications, such as when covariates associated with the heteroscedastic variances might not be associated with the mean structure of the response variable, or when strong correlation is present amongst covariates. The performance of the proposed method is assessed via a simulation study and is illustrated through a data analysis in which we show the importance of correct identification of covariates associated with the variance structure in estimation and inference. The Canadian Journal of Statistics 43: 358-377; 2015 © 2015 Statistical Society of Canada [ABSTRACT FROM AUTHOR]

This paper focuses on efficient estimation, optimal rates of convergence and effective algorithms in the partly linear additive hazards regression model with current status data. We use polynomial splines to estimate both cumulative baseline hazard function with monotonicity constraint and nonparametric regression functions with no such constraint. We propose a simultaneous sieve maximum likelihood estimation for regression parameters and nuisance parameters and show that the resultant estimator of regression parameter vector is asymptotically normal and achieves the semiparametric information bound. In addition, we show that rates of convergence for the estimators of nonparametric functions are optimal. We implement the proposed estimation through a backfitting algorithm on generalized linear models. We conduct simulation studies to examine the finite-sample performance of the proposed estimation method and present an analysis of renal function recovery data for illustration. [ABSTRACT FROM AUTHOR]

The expression of the cell cycle inhibitor p21 is increased in response to various stimuli and stress signals through p53-dependent and independent pathways. We demonstrate in this study that forkhead box A1/2 (FOXA1/2) is a crucial transcription factor in the activation of p21 transcription via direct binding to the p21 promoter in p53-null H1299 lung carcinoma cells. In addition, histone deacetylase inhibitor trichostatin A (TSA)-mediated upregulation of p21 expression was repressed by knockdown of FOXA1/2 in H1299 cells. Consequently, these results suggest that FOXA1/2 is required for p53-independent p21 expression. [ABSTRACT FROM AUTHOR]

This paper studies a new approach to longitudinal data analysis using the conditional empirical likelihood (CEL) method within the framework of marginal models. The possible unbalanced follow-up visits are dealt with via stratification according to distinctive follow-up patterns. The CEL method does not require any explicit modelling of the variance-covariance of the longitudinal outcomes. Instead, it implicitly incorporates a consistently estimated variance-covariance matrix in a nonparametric fashion. The proposed CEL estimator is connected to the generalized estimating equations (GEE) estimator, and achieves the same efficiency as the GEE estimator employing the true variance-covariance. The asymptotic distribution of the CEL estimator is derived, and simulation studies are conducted to assess the finite sample performance. Data collected from a longitudinal nutrition study are analysed as an application. The Canadian Journal of Statistics 42: 404-422; 2014 © 2014 Statistical Society of Canada [ABSTRACT FROM AUTHOR]

[1] Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R²). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS. [ABSTRACT FROM AUTHOR]

Objectives To evaluate a self-regulation intervention for asthma for older adults. Design A blinded randomized controlled trial. Setting Single-center tertiary care academic center. Participants Seventy older adults aged 65 and older with persistent asthma randomized to an intervention or control group. Intervention Participants participate in a six-session program conducted over the telephone and in group sessions. Participants selected an asthma-specific goal, identified problems, and addressed potential barriers. Measurements Outcomes were assessed at 1, 6, and 12 months and included the mini- Asthma Quality of Life Questionnaire (m AQLQ), Asthma Control Questionnaire ( ACQ), healthcare utilization, exhaled nitric oxide ( FENO), and percentage of predicted forced expiratory volume in 1 second ( FEV1%). Results The m AQLQ score was significantly higher in the intervention group at 1, 6, and 12 months, even after controlling for confounding factors. The between-group difference decreased over time, although at 12 months, it remained greater than 0.5 points. The ACQ was better in the intervention group than in the control group at 1, 6, and 12 months. At 12 months, those in the intervention group were 4.2 times as likely as those in the control group to have an ACQ score in the controlled range. Healthcare utilization was lower in the intervention group, although no difference was observed in FENO or predicted FEV1%. Conclusion A self-regulation intervention can improve asthma control, quality of life, and healthcare utilization in older adults. [ABSTRACT FROM AUTHOR]

Objective: To evaluate inpatient health care utilization for children with systemic lupus erythematosus (SLE) with and without kidney disease.Methods: The Healthcare Cost and Utilization Project Kids' Inpatient Database for the years 2000, 2003, and 2006 was used for this analysis. SLE hospitalizations from the 2006 cohort were identified and classified as those with versus without kidney involvement by International Classification of Diseases, Ninth Revision, Clinical Modification codes. Analyses were performed to examine determinants of hospitalization charges and changes in charges over time.Results: In the US, 7,390 SLE-related pediatric hospitalizations generated $267 million in total charges in 2006. Of these, 4,193 discharges had kidney involvement. The average hospitalization charge was greater for SLE patients with kidney involvement compared to those without kidney involvement ($43,100 versus $28,500; P < 0.0001). In multivariate analysis, kidney involvement remained a significant predictor of hospitalization charges, independent of demographic and hospital characteristics (P < 0.0001). SLE-associated acute kidney failure, transplant, and end-stage kidney disease resulted in greater hospitalization charges than SLE without kidney involvement by $74,900 (P < 0.0001), $32,700 (P = 0.0002), and $27,400 (P < 0.0001), respectively.Conclusion: In the US, >7,000 hospitalizations occurred in 2006 among children with SLE, with nearly 57% demonstrating kidney involvement. Kidney involvement is a major determinant of hospitalization charges for these children. This study represents one of the first large-scale assessments of in-hospital health care utilization by children with SLE. [ABSTRACT FROM AUTHOR]

Quadratic inference functions (QIF) methodology is an important alternative to the generalized estimating equations (GEE) method in the longitudinal marginal model, as it offers higher estimation efficiency than the GEE when correlation structure is misspecified. The focus of this paper is on sample size determination and power calculation for QIF based on the Wald test in a marginal logistic model with covariates of treatment, time, and treatment-time interaction. We have made three contributions in this paper: (i) we derived formulas of sample size and power for QIF and compared their performance with those given by the GEE; (ii) we proposed an optimal scheme of sample size determination to overcome the difficulty of unknown true correlation matrix in the sense of minimal average risk; and (iii) we studied properties of both QIF and GEE sample size formulas in relation to the number of follow-up visits and found that the QIF gave more robust sample sizes than the GEE. Using numerical examples, we illustrated that without sacrificing statistical power, the QIF design leads to sample size saving and hence lower study cost in comparison with the GEE analysis. We conclude that the QIF analysis is appealing for longitudinal studies. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Norman SP, Song PXK, Hu Y, Ojo AO. Transition from donor candidates to live kidney donors: the impact of race and undiagnosed medical disease states. Clin Transplant 2011: 25: 136-145. © 2009 John Wiley & Sons A/S. Living kidney donors (LKD) allow for increased access to lifesaving organs for transplantation. There is a relative paucity of African American (AA) live kidney donors. The prevalence of medical disease in LKD candidates has not been well studied. We examined the medical limitations to living kidney donation in a large Midwestern transplant center. A total of 2519 adults (age ≥ 18 ) evaluated as potential LKD (PD) between January 1, 1996 and June 30, 2006 were prospectively followed until evaluation outcome (completed live donation, medical exclusion from live donation, non-medical exclusion from live donation). Logistic regression was used to examine the effect of age on donor exclusion, and chi-square tests were used to compare the likelihood of donor exclusions between racial and gender groups. Sixty percent of PD were female (n = 1300), and 86% were Caucasian (CA) (n = 1862). Overall, 48.7% of PD who underwent evaluation became LKD. The odds of donation were 52% lower in AA compared to CA (OR 0.48 p < 0.001). Among PD excluded from donation, the most common medical diagnoses were hypertension (HTN) (24.7%), inadequate creatinine clearance (10.6%) and a positive final crossmatch (10.5%). The rate of PD exclusion for obesity was twofold higher in AA compared to CA (12.8% vs. 5.8%, p < 0.001). Hypertension in PD is equally significant barrier to living kidney donation in AA and CA whereas obesity is a greater barrier in AA. [ABSTRACT FROM AUTHOR]

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. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this study, a HPA3NT3-analog (FKKLKKLFKKILKLK-NH2) peptide was designed. In this analog, two Trp residues (positions 12, 14) were replaced with Leu, and Arg and Asn (positions 3, 13) were replaced with Lys to investigate the role of amino acid substitution and increased cationicity on antimicrobial and hemolytic activities. In fungal and Gram-negative bacterial cells, HPA3NT3-analog activity was unchanged or slightly enhanced when compared to the HPA3NT3 peptide. In addition, a twofold decrease in activity against Gram-positive bacteria was observed. The HPA3NT3-analog also induced less hemolysis (4.2%) than the HPA3NT3 peptide (71%) at 200 µ M. Circular dichroism (CD) spectra revealed that the HPA3NT3-analog peptide had an unordered structure in buffer and egg yolk L-2-phosphatidyl choline (EYPC), but adapted an α-helical conformation in 50% 2,2,2-trifluoroethanol (TFE) and negatively charged egg yolk L-2-phosphatidyl glycerol (EYPG), while the parent peptide showed an ordered structure in the EYPC. Additionally, the HPA3NT3-analog peptide induced the leakage of calcein from egg yolk L-2-phosphatidyl ethanolamine (EYPE)/EYPG (7:3 w/w) large unilamellar vesicles (LUVs); however, the activity was slightly weaker than that of the HPA3NT3 peptide. The molecular dynamics (MD) structures revealed that the amino acid substitutions induced a significant variation in peptide structure. These results suggest that the substitutions of Arg and Asn with Lys and two Trp with Leu resulted in small changes in HPA3NT3-analog activity and significant decreases in hemolytic activity. Copyright © 2009 European Peptide Society and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Continuous proportional outcomes are collected from many practical studies, where responses are confined within the unit interval (0,1). Utilizing Barndorff-Nielsen and Jørgensen's simplex distribution, we propose a new type of generalized linear mixed-effects model for longitudinal proportional data, where the expected value of proportion is directly modelled through a logit function of fixed and random effects. We establish statistical inference along the lines of Breslow and Clayton's penalized quasi-likelihood (PQL) and restricted maximum likelihood (REML) in the proposed model. We derive the PQL/REML using the high-order multivariate Laplace approximation, which gives satisfactory estimation of the model parameters. The proposed model and inference are illustrated by simulation studies and a data example. The simulation studies conclude that the fourth order approximate PQL/REML performs satisfactorily. The data example shows that Aitchison's technique of the normal linear mixed model for logit-transformed proportional outcomes is not robust against outliers. [ABSTRACT FROM AUTHOR]

Modèles avec espace d'états stationnaires pour des donnèes longitudinales Les auteurs s'intéressent à l'emploi d'une classe de modèles avec espace d'états aux fins d'analyse de données longitudinales non-normales dont le processus latent est un modèle AR(1) stationnaire combiné à des lois exponentielles dispersives pour les marges. Ils proposent d'estimer les paramètres au moyen d'équations d'estimation fondées sur le lissage de Kalman. Ils peuvent ainsi analyser facilement un large éventail de données non-normales. Ils illustrent leur approche au moyen de simulations et d'analyses de données concernant les précipitations au Brésil et les cas de poliomyélite aux États-Unis. [ABSTRACT FROM AUTHOR]

A class of autoregressive moving-average (ARMA) models proposed by Jørgensen 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 Jørgensen 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. [ABSTRACT FROM AUTHOR]

L'auteur s'intéresse aux modèles à espace d'états pour des chroniques binomiales multivariées, et plus particulièrement au développement du filtre de Kalman et au lissage pour les variables d'état. Il propose une approche de Monte-Carlo exploitant la représentation des variables latentes, laquelle permet d'adapter aux modèles discrets le filtre de Kalman et le lissage classiques des modèles à espace d'états gaussiens, ce qui conduit à une approche simple, tant au plan conceptuel que calculatoire. La méthode est illustrée au moyen de simulations et d'exemples concrets. [ABSTRACT FROM AUTHOR]

Presents a class of multivariate dispersion models generated from the multivariate Gaussian copula. Parameters of the multivariate models; Investigation on the properties of the multivariate distributions; Correlation between the likelihood equation and a generalized estimating equation.

This article shows that a single 2k-p fractional factorial design matrix and the traditional data analysis techniques associated with this design can be used effectively to achieve the goal of parameter design while reducing the total number of experiments required. A real example is used to compare Taguchi's two-part design, and S/N analysis to a 2k-p design and its associated analysis. Also, a flow diagram is presented to guide a practitioner through the parameter design process using a 2k-p design. This flow diagram provides an annoted guide to the literature on 2k-p decision and shows the design processes leading to proper choice of design and appropriate data analysis techniques. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)