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Covariate Effects (covariate + effects)
Selected AbstractsModeling mood variation associated with smoking: an application of a heterogeneous mixed-effects model for analysis of ecological momentary assessment (EMA) dataADDICTION, Issue 2 2009Donald Hedeker ABSTRACT Aims Mixed models are used increasingly for analysis of ecological momentary assessment (EMA) data. The variance parameters of the random effects, which indicate the degree of heterogeneity in the population of subjects, are considered usually to be homogeneous across subjects. Modeling these variances can shed light on interesting hypotheses in substance abuse research. Design We describe how these variances can be modeled in terms of covariates to examine the covariate effects on between-subjects variation, focusing on positive and negative mood and the degree to which these moods change as a function of smoking. Setting The data are drawn from an EMA study of adolescent smoking. Participants Participants were 234 adolescents, either in 9th or 10th grades, who provided EMA mood reports from both random prompts and following smoking events. Measurements We focused on two mood outcomes: measures of the subject's negative and positive affect and several covariates: gender, grade, negative mood regulation and smoking level. Findings and conclusions Following smoking, adolescents experienced higher positive affect and lower negative affect than they did at random, non-smoking times. Our analyses also indicated an increased consistency of subjective mood responses as smoking experience increased and a diminishing of mood change. [source] Forecasting new product trial in a controlled test market environmentJOURNAL OF FORECASTING, Issue 5 2003Peter S. Fader Abstract A number of researchers have developed models that use test market data to generate forecasts of a new product's performance. However, most of these models have ignored the effects of marketing covariates. In this paper we examine what impact these covariates have on a model's forecasting performance and explore whether their presence enables us to reduce the length of the model calibration period (i.e. shorten the duration of the test market). We develop from first principles a set of models that enable us to systematically explore the impact of various model ,components' on forecasting performance. Furthermore, we also explore the impact of the length of the test market on forecasting performance. We find that it is critically important to capture consumer heterogeneity, and that the inclusion of covariate effects can improve forecast accuracy, especially for models calibrated on fewer than 20 weeks of data.,Copyright © 2003 John Wiley & Sons, Ltd. [source] Another look into the effect of premarital cohabitation on duration of marriage: an approach based on matchingJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES A (STATISTICS IN SOCIETY), Issue 1 2009Stefano Mazzuco Summary., The paper proposes an alternative approach to studying the effect of premarital cohabitation on subsequent duration of marriage on the basis of a strong ignorability assumption. The approach is called propensity score matching and consists of computing survival functions conditional on a function of observed variables (the propensity score), thus eliminating any selection that is derived from these variables. In this way, it is possible to identify a time varying effect of cohabitation without making any assumption either regarding its shape or the functional form of covariate effects. The output of the matching method is the difference between the survival functions of treated and untreated individuals at each time point. Results show that the cohabitation effect on duration of marriage is indeed time varying, being close to zero for the first 2,3 years and rising considerably in the following years. [source] Semiparametric analysis of case series dataJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 5 2006C. P. Farrington Summary., The case series model for estimating the association between an age-dependent exposure and an outcome event requires information only on cases and implicitly adjusts for all age-independent multiplicative confounders, while allowing for an age-dependent base-line incidence. In the paper the model is presented in greater generality than hitherto, including more general discussion of its derivation, underlying assumptions, applicability, limitations and efficiency. A semiparametric version of the model is developed, in which the age-specific relative incidence is left unspecified. Modelling covariate effects and testing assumptions are discussed. The small sample performance of this model is studied in simulations. The methods are illustrated with several examples from epidemiology. [source] Estimating catch at age from market sampling data by using a Bayesian hierarchical modelJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 1 2004David Hirst Summary., The paper develops a Bayesian hierarchical model for estimating the catch at age of cod landed in Norway. The model includes covariate effects such as season and gear, and can also account for the within-boat correlation. The hierarchical structure allows us to account properly for the uncertainty in the estimates. [source] Choice of parametric models in survival analysis: applications to monotherapy for epilepsy and cerebral palsyJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 2 2003G. P. S. Kwong Summary. In the analysis of medical survival data, semiparametric proportional hazards models are widely used. When the proportional hazards assumption is not tenable, these models will not be suitable. Other models for covariate effects can be useful. In particular, we consider accelerated life models, in which the effect of covariates is to scale the quantiles of the base-line distribution. Solomon and Hutton have suggested that there is some robustness to misspecification of survival regression models. They showed that the relative importance of covariates is preserved under misspecification with assumptions of small coefficients and orthogonal transformation of covariates. We elucidate these results by applications to data from five trials which compare two common anti-epileptic drugs (carbamazepine versus sodium valporate monotherapy for epilepsy) and to survival of a cohort of people with cerebral palsy. Results on the robustness against model misspecification depend on the assumptions of small coefficients and on the underlying distribution of the data. These results hold in cerebral palsy but do not hold in epilepsy data which have early high hazard rates. The orthogonality of coefficients is not important. However, the choice of model is important for an estimation of the magnitude of effects, particularly if the base-line shape parameter indicates high initial hazard rates. [source] Proportional Intensity Models Robustness with Overhaul IntervalsQUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, Issue 3 2006Shwu-Tzy Jiang Abstract The class of semi-parametric proportional intensity (PI) models applies to recurrent failure event modeling for a repairable system with explanatory variables (covariates). Certain repairable systems (e.g. aircraft and electrical power generating plants) experience a substantial period of downtime due to performing maintenance (i.e. major overhaul) at scheduled intervals or following a major failure. Other systems (e.g. emergency power units) experience extended periods of non-operating dormancy. These discontinuities in observation time have potential effects on the accuracy of estimation for covariate effects, particularly where calendar time is the life metric. This paper examines the robustness of two PI methods (Prentice,Williams,Peterson gap time (PWP-GT) and Andersen,Gill (AG)) as a function of the overhaul or dormancy duration. The PWP-GT model proves to perform well for sample size of 60 (30 per level of a class covariate), constant or moderately decreasing/increasing rate of occurrence of failures, and relative overhaul (dormancy) durations less than half of the immediately preceding interval between failures. The AG model performs consistently well for a small sample size of 20 (10 per level of a class covariate) for homogeneous Poisson processes, regardless of the relative overhaul (dormancy) duration. Copyright © 2005 John Wiley & Sons, Ltd. [source] Nonparametric covariate adjustment for receiver operating characteristic curvesTHE CANADIAN JOURNAL OF STATISTICS, Issue 1 2010Fang Yao Abstract The accuracy of a diagnostic test is typically characterized using the receiver operating characteristic (ROC) curve. Summarizing indexes such as the area under the ROC curve (AUC) are used to compare different tests as well as to measure the difference between two populations. Often additional information is available on some of the covariates which are known to influence the accuracy of such measures. The authors propose nonparametric methods for covariate adjustment of the AUC. Models with normal errors and possibly non-normal errors are discussed and analyzed separately. Nonparametric regression is used for estimating mean and variance functions in both scenarios. In the model that relaxes the assumption of normality, the authors propose a covariate-adjusted Mann,Whitney estimator for AUC estimation which effectively uses available data to construct working samples at any covariate value of interest and is computationally efficient for implementation. This provides a generalization of the Mann,Whitney approach for comparing two populations by taking covariate effects into account. The authors derive asymptotic properties for the AUC estimators in both settings, including asymptotic normality, optimal strong uniform convergence rates and mean squared error (MSE) consistency. The MSE of the AUC estimators was also assessed in smaller samples by simulation. Data from an agricultural study were used to illustrate the methods of analysis. The Canadian Journal of Statistics 38:27,46; 2010 © 2009 Statistical Society of Canada La précision d'un test diagnostique est habituellement établie en utilisant les courbes caracté-ristiques de fonctionnement du récepteur (« ROC »). Des statistiques telles que l'aire sous la courbe ROC (« AUC ») sont utilisées afin de comparer différents tests et pour mesurer la différence entre deux populations. Souvent de l'information supplémentaire est disponible sur quelques covariables dont l'influence sur de telles statistiques est connue. Les auteurs suggèrent des méthodes non paramétriques afin d'ajuster la statistique AUC pour prendre en compte les covariables. Des modèles avec des erreurs gaussiennes et même non gaussiennes sont présentés et analysés séparément. Une régression non paramétrique est utilisée afin d'estimer les fonctions moyenne et variance dans les deux scénarios. Pour le modèle sans l'hypothèse de normalité, les auteurs proposent un estimateur de Mann-Whithney tenant compte des covariables pour l'AUC qui utilise l'information disponible dans les données afin de construire des échantillons d'analyse pour n'importe quelle valeur des covariables. Cet estimateur est implanté, car il est calculable de façon efficace. Il généralise l'approche de Mann-Whitney pour comparer deux populations en considérant l'effet des covariables. Les auteurs obtiennent les propriétés asymptotiques des estimateurs AUC pour les deux scénarios incluant la normalité asymptotique, les vitesses optimales de convergence uniforme forte et la convergence en erreur quadratique moyenne (« MSE »). Le MSE de l'estimateur de l'AUC est aussi étudié pour les petits échantillons à l'aide de simulations. Des données provenant d'une étude dans le domaine agricole sont utilisées afin d'illustrer les méthodes d'analyse. La revue canadienne de statistique 38: 27,46; 2010 © 2009 Sociètè statistique du Canada [source] Likelihood analysis of joint marginal and conditional models for longitudinal categorical dataTHE CANADIAN JOURNAL OF STATISTICS, Issue 2 2009Baojiang Chen MSC 2000: Primary 62H12; secondary 62F10 Abstract The authors develop a Markov model for the analysis of longitudinal categorical data which facilitates modelling both marginal and conditional structures. A likelihood formulation is employed for inference, so the resulting estimators enjoy the optimal properties such as efficiency and consistency, and remain consistent when data are missing at random. Simulation studies demonstrate that the proposed method performs well under a variety of situations. Application to data from a smoking prevention study illustrates the utility of the model and interpretation of covariate effects. The Canadian Journal of Statistics © 2009 Statistical Society of Canada Les auteurs développent un modèle de Markov pour l'analyse de données catégorielles longitudinales facilitant la représentation des structures marginales et conditionnelles. L'inférence est basée sur une fonction de vraisemblance afin d'obtenir des estimateurs efficaces, cohérents et qui le demeurent lorsqu'il y a des données manquantes au hasard. Des études de simulation montrent que la méthode proposée se comporte bien dans les différents scénarios considérés. L'application à des données provenant d'une étude sur la lutte contre le tabagisme illustre bien l'utilité de ce modèle et permet une interprétation des effets des covariables. La revue canadienne de statistique © 2009 Société statistique du Canada [source] Non-parametric regression with a latent time seriesTHE ECONOMETRICS JOURNAL, Issue 2 2009Oliver Linton Summary, In this paper we investigate a class of semi-parametric models for panel data sets where the cross-section and time dimensions are large. Our model contains a latent time series that is to be estimated and perhaps forecasted along with a non-parametric covariate effect. Our model is motivated by the need to be flexible with regard to the functional form of covariate effects but also the need to be practical with regard to forecasting of time series effects. We propose estimation procedures based on local linear kernel smoothing; our estimators are all explicitly given. We establish the pointwise consistency and asymptotic normality of our estimators. We also show that the effects of estimating the latent time series can be ignored in certain cases. [source] Dissecting the heterogeneity of rheumatoid arthritis through linkage analysis of quantitative traitsARTHRITIS & RHEUMATISM, Issue 1 2007Lindsey A. Criswell Objective To dissect the heterogeneity of rheumatoid arthritis (RA) through linkage analysis of quantitative traits, specifically, IgM rheumatoid factor (IgM-RF) and anti,cyclic citrullinated peptide (anti-CCP) autoantibody titers. Methods Subjects, 1,002 RA patients from 491 multiplex families recruited by the North American RA Consortium, were typed for 379 microsatellite markers. Anti-CCP titers were determined based on a second-generation enzyme-linked immunosorbent assay, and IgM-RF levels were quantified by immunonephelometry. We used the Merlin statistical package to perform nonparametric quantitative trait linkage analysis. Results For each of the quantitative traits, evidence of linkage, with logarithm of odds (LOD) scores of >1.0, was found in 9 regions. For both traits, the strongest evidence of linkage was for marker D6S1629 on chromosome 6p (LOD 14.02 for anti-CCP and LOD 12.09 for RF). Six other regions with LOD scores of >1.0 overlapped between the 2 traits, on chromosomes 1p21.1, 5q15, 8p23.1, 16p12.1, 16q23.1, and 18q21.31. Evidence of linkage to anti-CCP titer but not to RF titer was found in 2 regions (chromosomes 9p21.3 and 10q21.1), and evidence of linkage to RF titer but not to anti-CCP titer was found in 2 regions (chromosomes 5p15.2 and 1q42.3). Several covariates were significantly associated with 1 or both traits, and linkage analysis exploring the covariate effects revealed striking effects of sex in modulating linkage signals for several chromosomal regions. For example, sex had a striking impact on the linkage results for both quantitative traits on chromosome 6p (P = 0.0007 for anti-CCP titer and P = 0.0012 for RF titer), suggesting a sex,HLA region interaction. Conclusion Analysis of quantitative components of RA is a promising approach for dissecting the genetic heterogeneity of this complex disorder. These results highlight the potential importance of sex or other covariates that may modulate some of the genetic effects that influence the risk of specific disease manifestations. [source] ACCELERATED FAILURE TIME MODELS WITH NONLINEAR COVARIATES EFFECTSAUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 2 2007Chenlei Leng Summary As a flexible alternative to the Cox model, the accelerated failure time (AFT) model assumes that the event time of interest depends on the covariates through a regression function. The AFT model with non-parametric covariate effects is investigated, when variable selection is desired along with estimation. Formulated in the framework of the smoothing spline analysis of variance model, the proposed method based on the Stute estimate (Stute, 1993[Consistent estimation under random censorship when covariables are present, J. Multivariate Anal.45, 89,103]) can achieve a sparse representation of the functional decomposition, by utilizing a reproducing kernel Hilbert norm penalty. Computational algorithms and theoretical properties of the proposed method are investigated. The finite sample size performance of the proposed approach is assessed via simulation studies. The primary biliary cirrhosis data is analyzed for demonstration. [source] Fitting Semiparametric Additive Hazards Models using Standard Statistical SoftwareBIOMETRICAL JOURNAL, Issue 5 2007Douglas E. Schaubel Abstract The Cox proportional hazards model has become the standard in biomedical studies, particularly for settings in which the estimation covariate effects (as opposed to prediction) is the primary objective. In spite of the obvious flexibility of this approach and its wide applicability, the model is not usually chosen for its fit to the data, but by convention and for reasons of convenience. It is quite possible that the covariates add to, rather than multiply the baseline hazard, making an additive hazards model a more suitable choice. Typically, proportionality is assumed, with the potential for additive covariate effects not evaluated or even seriously considered. Contributing to this phenomenon is the fact that many popular software packages (e.g., SAS, S-PLUS/R) have standard procedures to fit the Cox model (e.g., proc phreg, coxph), but as of yet no analogous procedures to fit its additive analog, the Lin and Ying (1994) semiparametric additive hazards model. In this article, we establish the connections between the Lin and Ying (1994) model and both Cox and least squares regression. We demonstrate how SAS's phreg and reg procedures may be used to fit the additive hazards model, after some straightforward data manipulations. We then apply the additive hazards model to examine the relationship between Model for End-stage Liver Disease (MELD) score and mortality among patients wait-listed for liver transplantation. (© 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source] A Mixture Point Process for Repeated Failure Times, with an Application to a Recurrent DiseaseBIOMETRICAL JOURNAL, Issue 7 2003O. Pons Abstract We present a model that describes the distribution of recurring times of a disease in presence of covariate effects. After a first occurrence of the disease in an individual, the time intervals between successive cases are supposed to be independent and to be a mixture of two distributions according to the issue of the previous treatment. Both sub-distributions of the model and the mixture proportion are allowed to involve covariates. Parametric inference is considered and we illustrate the methods with data of a recurrent disease and with simulations, using piecewise constant baseline hazard functions. [source] Statistical Methods for Analyzing Right-Censored Length-Biased Data under Cox ModelBIOMETRICS, Issue 2 2010Jing Qin Summary Length-biased time-to-event data are commonly encountered in applications ranging from epidemiological cohort studies or cancer prevention trials to studies of labor economy. A longstanding statistical problem is how to assess the association of risk factors with survival in the target population given the observed length-biased data. In this article, we demonstrate how to estimate these effects under the semiparametric Cox proportional hazards model. The structure of the Cox model is changed under length-biased sampling in general. Although the existing partial likelihood approach for left-truncated data can be used to estimate covariate effects, it may not be efficient for analyzing length-biased data. We propose two estimating equation approaches for estimating the covariate coefficients under the Cox model. We use the modern stochastic process and martingale theory to develop the asymptotic properties of the estimators. We evaluate the empirical performance and efficiency of the two methods through extensive simulation studies. We use data from a dementia study to illustrate the proposed methodology, and demonstrate the computational algorithms for point estimates, which can be directly linked to the existing functions in S-PLUS or R. [source] Regression Analysis with a Misclassified Covariate from a Current Status Observation SchemeBIOMETRICS, Issue 2 2010Leilei Zeng Summary Naive use of misclassified covariates leads to inconsistent estimators of covariate effects in regression models. A variety of methods have been proposed to address this problem including likelihood, pseudo-likelihood, estimating equation methods, and Bayesian methods, with all of these methods typically requiring either internal or external validation samples or replication studies. We consider a problem arising from a series of orthopedic studies in which interest lies in examining the effect of a short-term serological response and other covariates on the risk of developing a longer term thrombotic condition called deep vein thrombosis. The serological response is an indicator of whether the patient developed antibodies following exposure to an antithrombotic drug, but the seroconversion status of patients is only available at the time of a blood sample taken upon the discharge from hospital. The seroconversion time is therefore subject to a current status observation scheme, or Case I interval censoring, and subjects tested before seroconversion are misclassified as nonseroconverters. We develop a likelihood-based approach for fitting regression models that accounts for misclassification of the seroconversion status due to early testing using parametric and nonparametric estimates of the seroconversion time distribution. The method is shown to reduce the bias resulting from naive analyses in simulation studies and an application to the data from the orthopedic studies provides further illustration. [source] Bayesian Inference in Semiparametric Mixed Models for Longitudinal DataBIOMETRICS, Issue 1 2010Yisheng Li Summary We consider Bayesian inference in semiparametric mixed models (SPMMs) for longitudinal data. SPMMs are a class of models that use a nonparametric function to model a time effect, a parametric function to model other covariate effects, and parametric or nonparametric random effects to account for the within-subject correlation. We model the nonparametric function using a Bayesian formulation of a cubic smoothing spline, and the random effect distribution using a normal distribution and alternatively a nonparametric Dirichlet process (DP) prior. When the random effect distribution is assumed to be normal, we propose a uniform shrinkage prior (USP) for the variance components and the smoothing parameter. When the random effect distribution is modeled nonparametrically, we use a DP prior with a normal base measure and propose a USP for the hyperparameters of the DP base measure. We argue that the commonly assumed DP prior implies a nonzero mean of the random effect distribution, even when a base measure with mean zero is specified. This implies weak identifiability for the fixed effects, and can therefore lead to biased estimators and poor inference for the regression coefficients and the spline estimator of the nonparametric function. We propose an adjustment using a postprocessing technique. We show that under mild conditions the posterior is proper under the proposed USP, a flat prior for the fixed effect parameters, and an improper prior for the residual variance. We illustrate the proposed approach using a longitudinal hormone dataset, and carry out extensive simulation studies to compare its finite sample performance with existing methods. [source] Partly Functional Temporal Process Regression with Semiparametric Profile Estimating FunctionsBIOMETRICS, Issue 2 2009Jun Yan Summary Marginal mean models of temporal processes in event time data analysis are gaining more attention for their milder assumptions than the traditional intensity models. Recent work on fully functional temporal process regression (TPR) offers great flexibility by allowing all the regression coefficients to be nonparametrically time varying. The existing estimation procedure, however, prevents successive goodness-of-fit test for covariate coefficients in comparing a sequence of nested models. This article proposes a partly functional TPR model in the line of marginal mean models. Some covariate effects are time independent while others are completely unspecified in time. This class of models is very rich, including the fully functional model and the semiparametric model as special cases. To estimate the parameters, we propose semiparametric profile estimating equations, which are solved via an iterative algorithm, starting at a consistent estimate from a fully functional model in the existing work. No smoothing is needed, in contrast to other varying-coefficient methods. The weak convergence of the resultant estimators are developed using the empirical process theory. Successive tests of time-varying effects and backward model selection procedure can then be carried out. The practical usefulness of the methodology is demonstrated through a simulation study and a real example of recurrent exacerbation among cystic fibrosis patients. [source] Statistical Methods for Analysis of Radiation Effects with Tumor and Dose Location-Specific Information with Application to the WECARE Study of Asynchronous Contralateral Breast CancerBIOMETRICS, Issue 2 2009Bryan Langholz Summary Methods for the analysis of individually matched case-control studies with location-specific radiation dose and tumor location information are described. These include likelihood methods for analyses that just use cases with precise location of tumor information and methods that also include cases with imprecise tumor location information. The theory establishes that each of these likelihood based methods estimates the same radiation rate ratio parameters, within the context of the appropriate model for location and subject level covariate effects. The underlying assumptions are characterized and the potential strengths and limitations of each method are described. The methods are illustrated and compared using the WECARE study of radiation and asynchronous contralateral breast cancer. [source] Smoothing Spline-Based Score Tests for Proportional Hazards ModelsBIOMETRICS, Issue 3 2006Jiang Lin Summary We propose "score-type" tests for the proportional hazards assumption and for covariate effects in the Cox model using the natural smoothing spline representation of the corresponding nonparametric functions of time or covariate. The tests are based on the penalized partial likelihood and are derived by viewing the inverse of the smoothing parameter as a variance component and testing an equivalent null hypothesis that the variance component is zero. We show that the tests have a size close to the nominal level and good power against general alternatives, and we apply them to data from a cancer clinical trial. [source] Latent Transition Regression for Mixed OutcomesBIOMETRICS, Issue 3 2003Diana L. Miglioretti Summary. Health status is a complex outcome, often characterized by multiple measures. When assessing changes in health status over time, multiple measures are typically collected longitudinally. Analytic challenges posed by these multivariate longitudinal data are further complicated when the outcomes are combinations of continuous, categorical, and count data. To address these challenges, we propose a fully Bayesian latent transition regression approach for jointly analyzing a mixture of longitudinal outcomes from any distribution. Health status is assumed to be a categorical latent variable, and the multiple outcomes are treated as surrogate measures of the latent health state, observed with error. Using this approach, both baseline latent health state prevalences and the probabilities of transitioning between the health states over time are modeled as functions of covariates. The observed outcomes are related to the latent health states through regression models that include subject-specific effects to account for residual correlation among repeated measures over time, and covariate effects to account for differential measurement of the latent health states. We illustrate our approach with data from a longitudinal study of back pain. [source] Effects of prenatal cocaine exposure on special education in school-aged childrenCHILD: CARE, HEALTH AND DEVELOPMENT, Issue 5 2008Richard Reading Effects of prenatal cocaine exposure on special education in school-aged children . LevineT. P., LiuJ., DasA., LesterB., LagasseL., ShankaranS., BadaH. S., BauerC. R. & HigginsR. ( 2008 ) Pediatrics . Published online . DOI: 10.1542/peds.2007-2826 . Objective The objective of this study was to evaluate the effects of prenatal cocaine exposure on special education at age 7 with adjustment for covariates. Methods As part of the prospective, longitudinal, multi-site study of children with prenatal cocaine exposure (Maternal Lifestyle Study), school records were reviewed for 943 children at 7 years to determine involvement in special education outcomes: (1) individualized education plan; (2) special education conditions; (3) support services; (4) special education classes; and (5) speech and language services. Logistic regression was used to examine the effect of prenatal cocaine exposure on these outcomes with environmental, maternal and infant medical variables as covariates, as well as with and without low child IQ. Results Complete data for each analysis model were available for 737,916 children. When controlling for covariates including low child IQ, prenatal cocaine exposure had a significant effect on individualized education plan. When low child IQ was not included in the model, prenatal cocaine exposure had a significant effect on support services. Male gender, low birthweight, white race and low child IQ also predicted individualized education plan. Low birthweight and low child IQ were significant in all models. White race was also significant in speech and language services. Other covariate effects were model specific. When included in the models, low child IQ accounted for more of the variance and changed the significance of other covariates. Conclusions Prenatal cocaine exposure increased the likelihood of receiving an individualized education plan and support services, with adjustment for covariates. Low birthweight and low child IQ increased the likelihood of all outcomes. The finding that white children were more likely to get an individualized education plan and speech and language services could indicate a greater advantage in getting educational resources for this population. [source] Cortisol levels and measures of body composition in middle-aged and older menCLINICAL ENDOCRINOLOGY, Issue 1 2007Thomas G. Travison Summary Introduction, Similarities in the symptomatic expressions of excess adiposity and hypercortisolaemic conditions suggest that elevated glucocorticoid exposure may influence the pathogenesis of obesity. Circulating cortisol levels are not typically elevated in obese subjects, but data from large prospective samples are rare. We undertook an analysis to determine both cross-sectional and longitudinal associations between body composition and serum cortisol concentrations in a randomly chosen group of 999 community-dwelling men, aged 40,79 years. Methods, Data were obtained from the two follow-up waves of the Massachusetts Male Ageing Study (T2: 1995,97; T3: 2002,04). Partial correlation and multivariate regression analyses were used to estimate cross-sectional (T2) and longitudinal associations between serum cortisol concentrations and a range of measures of subjects' body composition, including weight, body mass index (BMI), waist circumference (WC), waist-to-hip girth ratio (WHR), and percentage body fat (measured by bioelectrical impedance at T3); similar analyses were conducted to assess the association between change (T2 to T3) in serum cortisol and simultaneous change in body composition parameters. Results, We observed weak negative associations between cortisol concentrations and all body composition parameters, with the exception of percentage body fat. Longitudinal results demonstrated similar relationships but associations were of lesser magnitude. T2 cortisol concentrations were not associated with change in body composition over time, whereas T2 body size was positively associated with longitudinal changes in cortisol concentrations, providing limited evidence that weight change drives changes in cortisol concentrations, rather than vice versa. Results were unchanged when age and other covariate effects were controlled. Conclusions, Circulating cortisol concentrations are somewhat lower in obese than in nonobese community-dwelling men. There is some evidence that excess adiposity presages increases in cortisol concentrations, rather than the reverse. However, this observation should be greeted with caution, as age-related weight loss , and not gain , was associated with simultaneous increases in serum cortisol concentrations. [source] Case,Cohort Analysis with Accelerated Failure Time ModelBIOMETRICS, Issue 1 2009Lan Kong Summary In a case,cohort design, covariates are assembled only for a subcohort that is randomly selected from the entire cohort and any additional cases outside the subcohort. This design is appealing for large cohort studies of rare disease, especially when the exposures of interest are expensive to ascertain for all the subjects. We propose statistical methods for analyzing the case,cohort data with a semiparametric accelerated failure time model that interprets the covariates effects as to accelerate or decelerate the time to failure. Asymptotic properties of the proposed estimators are developed. The finite sample properties of case,cohort estimator and its relative efficiency to full cohort estimator are assessed via simulation studies. A real example from a study of cardiovascular disease is provided to illustrate the estimating procedure. [source] | |||||||||||||||||||||||||||||||