Longitudinal Data (longitudinal + data)

Distribution by Scientific Domains
Distribution within Mathematics and Statistics

Terms modified by Longitudinal Data

  • longitudinal data analysis
  • longitudinal data set

  • Selected Abstracts


    Pulak Ghosh
    Summary We extend the standard multivariate mixed model by incorporating a smooth time effect and relaxing distributional assumptions. We propose a semiparametric Bayesian approach to multivariate longitudinal data using a mixture of Polya trees prior distribution. Usually, the distribution of random effects in a longitudinal data model is assumed to be Gaussian. However, the normality assumption may be suspect, particularly if the estimated longitudinal trajectory parameters exhibit multi-modality and skewness. In this paper we propose a mixture of Polya trees prior density to address the limitations of the parametric random effects distribution. We illustrate the methodology by analysing data from a recent HIV-AIDS study. [source]

    A Two-Factor Model for Predicting When a Couple Will Divorce: Exploratory Analyses Using 14-Year Longitudinal Data,

    FAMILY PROCESS, Issue 1 2002
    John Mordechai Gottman Ph.D.
    This article examines 14-year longitudinal data and attempts to create a post hoc model that uses Time-1 data to "predict" the length of time the marriage will last. The sample consists of the 21 couples (of 79 studied) who divorced over a 14-year period. A two-factor model is proposed. One factor is the amount of unregulated volatile positive and negative affect in the marriage, and this factor predicts a short marriage length for the divorcing couples. A second factor is called "neutral affective style," and this factor predicts a long marriage length for the divorcing couples. This model is compared to a Time-1 model of ailing marriage in which Time-1 marital satisfaction is used to predict the timing of divorce. [source]

    Nonparametric Varying-Coefficient Models for the Analysis of Longitudinal Data

    Colin O. Wu
    Summary Longitudinal methods have been widely used in biomedicine and epidemiology to study the patterns of time-varying variables, such as disease progression or trends of health status. Data sets of longitudinal studies usually involve repeatedly measured outcomes and covariates on a set of randomly chosen subjects over time. An important goal of statistical analyses is to evaluate the effects of the covariates, which may or may not depend on time, on the outcomes of interest. Because fully parametric models may be subject to model misspecification and completely unstructured nonparametric models may suffer from the drawbacks of "curse of dimensionality", the varying-coefficient models are a class of structural nonparametric models which are particularly useful in longitudinal analyses. In this article, we present several important nonparametric estimation and inference methods for this class of models, demonstrate the advantages, limitations and practical implementations of these methods in different longitudinal settings, and discuss some potential directions of further research in this area. Applications of these methods are illustrated through two epidemiological examples. Résumé Modèles non-paramétriques à coefficients variables pour l'analyse de données longitudinales Les méthodes longitudinales ont été largement utilisées en biomédecine et en épidémiologie pour étudier les modèles de variables variant dans le temps, du type progression de maladie ou tendances détat de santé. Les ensembles de données d'études longitudinales comprennent généralement des ésultats de mesures répétées et des covariables sur un ensemble de sujets choisis au hasard dans le temps. Un objectif important des analyses statistiques consisteàévaluer les effets des covariables, qui peuvent ou non dépendre du temps, sur les résultats d'intérêt. Du fait que des modèles entièrement paramétriques peuvent faire l'objet d'erreur de spécification de modèle et que des modèles non-paramétriques totalement non-structurés peuvent souffrir des inconvénients de la «malédiction de dimensionnalité», les modèles à coefficients variables sont une classe de modèles structurels non-paramétriques particulièrement utiles dans les analyses longitudinales. Dans cet article, on présente plusieurs estimations non-paramétriques importantes, ainsi que des méthodes d'inférence pour cette classe de modéles, on démontre les avantages, limites et mises en ,uvre pratiques de ces méthodes dans différents contextes longitudinaux et l'on traite de certaines directions possibles pour de plus amples recherches dans ce domaine. Des applications de ces méthodes sont illustrées à travers deux exemples épidémiologiques. [source]

    Negligible Analgesic Tolerance Seen with Extended Release Oxymorphone: A Post Hoc Analysis of Open-Label Longitudinal Data

    PAIN MEDICINE, Issue 8 2010
    R. Norman Harden MD
    Abstract Objective., To examine the development of analgesic tolerance in patients on oxymorphone extended-release (OxymER). Design.,Post hoc analysis of data from a previously conducted prospective 1 year multi-center open-label extension study in which patients were able to titrate as needed. Patients., Sample of 153 hip and knee osteoarthritis (OA) subjects on OxymER. Primary analyses were limited to study completers (n = 62) due to the large amount of missing data for the noncompleters (n = 91). Outcome Measures., Main outcome measures included OxymER doses (pill counts) and pain intensity ratings using a visual analog scale at monthly visits. Results., There were significant dose increases from weeks 1 to 2 and 2 to 6 (P < 0.05). Doses stabilized around week 6, suggesting the completion of what we defined as "titration." Both doses and pain ratings were stable when this titration phase was excluded from the analysis (P = 0.751; P = 0.056, respectively). Only 28% of the patients had any dose changes following this titration. While there was a significantly greater dose at week 52 compared with week 10 (P = 0.010), the increase in dose became insignificant after excluding four subjects who required two dose increases (P = 0.103). Conclusions., The results showed that most of the titration/dose stabilization changes occurred within the first 10 weeks. A minority (28%) of subjects required dosage increases after this (defined) titration period. Pain reports stabilized statistically after 2 weeks. The findings of this post hoc analysis suggest a lack of opioid tolerance in the majority (72%) of these OA patients who completed this study following a defined titration period on OxymER. Summary., This post hoc analysis of oxymorphone ER consumption in osteoarthritis pain vs pain report showed that most dose changes occurred during an initial "titration period" as defined. Following this titration few subjects increased dose and analgesia remained stable. These findings suggest a lack of longitudinal opioid tolerance in the majority of those OA subjects who completed the trial. [source]

    Rethinking Historical Reproductive Change: Insights from Longitudinal Data for a Spanish Town

    David Sven Reher
    A set of linked reproductive histories taken from the Spanish town of Aranjuez between 1871 and 1950 is used to address key issues regarding reproductive change during the demographic transition. These include the role of child survival as a stimulus for reproductive change, the use of stopping and/or spacing strategies to achieve reproductive goals, and the timing of change. Straightforward demographic measures are used and robust results are achieved. Initial strategies of fertility limitation are shown to exist but are inefficient, are mostly visible during the latter part of the reproductive period, are designed mostly to protect families from the effects of increases in child survival, and are based almost entirely on stopping behavior. As mortality decline accelerates, strategies become much more efficient, are visible at the outset of married life, include spacing behavior, and eventually lead to important declines in completed family size. The results of this study have implications for our understanding of the demographic transition both in historical Europe and in other regions of the world. [source]

    Inference Methods for the Conditional Logistic Regression Model with Longitudinal Data

    Radu V. Craiu
    No abstract is available for this article. [source]

    Book Review: Modelling Longitudinal Data.

    By R. Weiss
    No abstract is available for this article. [source]

    Bayesian Inference in Semiparametric Mixed Models for Longitudinal Data

    BIOMETRICS, Issue 1 2010
    Yisheng 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]

    Estimation in Semiparametric Transition Measurement Error Models for Longitudinal Data

    BIOMETRICS, Issue 3 2009
    Wenqin Pan
    Summary We consider semiparametric transition measurement error models for longitudinal data, where one of the covariates is measured with error in transition models, and no distributional assumption is made for the underlying unobserved covariate. An estimating equation approach based on the pseudo conditional score method is proposed. We show the resulting estimators of the regression coefficients are consistent and asymptotically normal. We also discuss the issue of efficiency loss. Simulation studies are conducted to examine the finite-sample performance of our estimators. The longitudinal AIDS Costs and Services Utilization Survey data are analyzed for illustration. [source]

    Joint Modeling and Analysis of Longitudinal Data with Informative Observation Times

    BIOMETRICS, Issue 2 2009
    Yu Liang
    Summary In analysis of longitudinal data, it is often assumed that observation times are predetermined and are the same across study subjects. Such an assumption, however, is often violated in practice. As a result, the observation times may be highly irregular. It is well known that if the sampling scheme is correlated with the outcome values, the usual statistical analysis may yield bias. In this article, we propose joint modeling and analysis of longitudinal data with possibly informative observation times via latent variables. A two-step estimation procedure is developed for parameter estimation. We show that the resulting estimators are consistent and asymptotically normal, and that the asymptotic variance can be consistently estimated using the bootstrap method. Simulation studies and a real data analysis demonstrate that our method performs well with realistic sample sizes and is appropriate for practical use. [source]

    Mixed-Effect Hybrid Models for Longitudinal Data with Nonignorable Dropout

    BIOMETRICS, Issue 2 2009
    Ying Yuan
    Summary Selection models and pattern-mixture models are often used to deal with nonignorable dropout in longitudinal studies. These two classes of models are based on different factorizations of the joint distribution of the outcome process and the dropout process. We consider a new class of models, called mixed-effect hybrid models (MEHMs), where the joint distribution of the outcome process and dropout process is factorized into the marginal distribution of random effects, the dropout process conditional on random effects, and the outcome process conditional on dropout patterns and random effects. MEHMs combine features of selection models and pattern-mixture models: they directly model the missingness process as in selection models, and enjoy the computational simplicity of pattern-mixture models. The MEHM provides a generalization of shared-parameter models (SPMs) by relaxing the conditional independence assumption between the measurement process and the dropout process given random effects. Because SPMs are nested within MEHMs, likelihood ratio tests can be constructed to evaluate the conditional independence assumption of SPMs. We use data from a pediatric AIDS clinical trial to illustrate the models. [source]

    Median Regression Models for Longitudinal Data with Dropouts

    BIOMETRICS, Issue 2 2009
    Grace Y. Yi
    Summary Recently, median regression models have received increasing attention. When continuous responses follow a distribution that is quite different from a normal distribution, usual mean regression models may fail to produce efficient estimators whereas median regression models may perform satisfactorily. In this article, we discuss using median regression models to deal with longitudinal data with dropouts. Weighted estimating equations are proposed to estimate the median regression parameters for incomplete longitudinal data, where the weights are determined by modeling the dropout process. Consistency and the asymptotic distribution of the resultant estimators are established. The proposed method is used to analyze a longitudinal data set arising from a controlled trial of HIV disease (Volberding et al., 1990, The New England Journal of Medicine322, 941,949). Simulation studies are conducted to assess the performance of the proposed method under various situations. An extension to estimation of the association parameters is outlined. [source]

    Robustified Maximum Likelihood Estimation in Generalized Partial Linear Mixed Model for Longitudinal Data

    BIOMETRICS, Issue 1 2009
    Guo You Qin
    Summary In this article, we study the robust estimation of both mean and variance components in generalized partial linear mixed models based on the construction of robustified likelihood function. Under some regularity conditions, the asymptotic properties of the proposed robust estimators are shown. Some simulations are carried out to investigate the performance of the proposed robust estimators. Just as expected, the proposed robust estimators perform better than those resulting from robust estimating equations involving conditional expectation like Sinha (2004, Journal of the American Statistical Association99, 451,460) and Qin and Zhu (2007, Journal of Multivariate Analysis98, 1658,1683). In the end, the proposed robust method is illustrated by the analysis of a real data set. [source]

    Discriminant Analysis for Longitudinal Data with Multiple Continuous Responses and Possibly Missing Data

    BIOMETRICS, Issue 1 2009
    Guillermo Marshall
    Summary Multiple outcomes are often used to properly characterize an effect of interest. This article discusses model-based statistical methods for the classification of units into one of two or more groups where, for each unit, repeated measurements over time are obtained on each outcome. We relate the observed outcomes using multivariate nonlinear mixed-effects models to describe evolutions in different groups. Due to its flexibility, the random-effects approach for the joint modeling of multiple outcomes can be used to estimate population parameters for a discriminant model that classifies units into distinct predefined groups or populations. Parameter estimation is done via the expectation-maximization algorithm with a linear approximation step. We conduct a simulation study that sheds light on the effect that the linear approximation has on classification results. We present an example using data from a study in 161 pregnant women in Santiago, Chile, where the main interest is to predict normal versus abnormal pregnancy outcomes. [source]

    Conditional Generalized Estimating Equations for the Analysis of Clustered and Longitudinal Data

    BIOMETRICS, Issue 3 2008
    Sylvie Goetgeluk
    Summary A common and important problem in clustered sampling designs is that the effect of within-cluster exposures (i.e., exposures that vary within clusters) on outcome may be confounded by both measured and unmeasured cluster-level factors (i.e., measurements that do not vary within clusters). When some of these are ill/not accounted for, estimation of this effect through population-averaged models or random-effects models may introduce bias. We accommodate this by developing a general theory for the analysis of clustered data, which enables consistent and asymptotically normal estimation of the effects of within-cluster exposures in the presence of cluster-level confounders. Semiparametric efficient estimators are obtained by solving so-called conditional generalized estimating equations. We compare this approach with a popular proposal by Neuhaus and Kalbfleisch (1998, Biometrics54, 638,645) who separate the exposure effect into a within- and a between-cluster component within a random intercept model. We find that the latter approach yields consistent and efficient estimators when the model is linear, but is less flexible in terms of model specification. Under nonlinear models, this approach may yield inconsistent and inefficient estimators, though with little bias in most practical settings. [source]

    Modeling Longitudinal Data with Nonparametric Multiplicative Random Effects Jointly with Survival Data

    BIOMETRICS, Issue 2 2008
    Jimin Ding
    Summary In clinical studies, longitudinal biomarkers are often used to monitor disease progression and failure time. Joint modeling of longitudinal and survival data has certain advantages and has emerged as an effective way to mutually enhance information. Typically, a parametric longitudinal model is assumed to facilitate the likelihood approach. However, the choice of a proper parametric model turns out to be more elusive than models for standard longitudinal studies in which no survival endpoint occurs. In this article, we propose a nonparametric multiplicative random effects model for the longitudinal process, which has many applications and leads to a flexible yet parsimonious nonparametric random effects model. A proportional hazards model is then used to link the biomarkers and event time. We use B-splines to represent the nonparametric longitudinal process, and select the number of knots and degrees based on a version of the Akaike information criterion (AIC). Unknown model parameters are estimated through maximizing the observed joint likelihood, which is iteratively maximized by the Monte Carlo Expectation Maximization (MCEM) algorithm. Due to the simplicity of the model structure, the proposed approach has good numerical stability and compares well with the competing parametric longitudinal approaches. The new approach is illustrated with primary biliary cirrhosis (PBC) data, aiming to capture nonlinear patterns of serum bilirubin time courses and their relationship with survival time of PBC patients. [source]

    Mixtures of Varying Coefficient Models for Longitudinal Data with Discrete or Continuous Nonignorable Dropout

    BIOMETRICS, Issue 4 2004
    Joseph W. Hogan
    Summary The analysis of longitudinal repeated measures data is frequently complicated by missing data due to informative dropout. We describe a mixture model for joint distribution for longitudinal repeated measures, where the dropout distribution may be continuous and the dependence between response and dropout is semiparametric. Specifically, we assume that responses follow a varying coefficient random effects model conditional on dropout time, where the regression coefficients depend on dropout time through unspecified nonparametric functions that are estimated using step functions when dropout time is discrete (e.g., for panel data) and using smoothing splines when dropout time is continuous. Inference under the proposed semiparametric model is hence more robust than the parametric conditional linear model. The unconditional distribution of the repeated measures is a mixture over the dropout distribution. We show that estimation in the semiparametric varying coefficient mixture model can proceed by fitting a parametric mixed effects model and can be carried out on standard software platforms such as SAS. The model is used to analyze data from a recent AIDS clinical trial and its performance is evaluated using simulations. [source]

    Small-Sample Inference for Incomplete Longitudinal Data with Truncation and Censoring in Tumor Xenograft Models

    BIOMETRICS, Issue 3 2002
    Ming Tan
    Summary. In cancer drug development, demonstrating activity in xenograft models, where mice are grafted with human cancer cells, is an important step in bringing a promising compound to humans. A key outcome variable is the tumor volume measured in a given period of time for groups of mice given different doses of a single or combination anticancer regimen. However, a mouse may die before the end of a study or may be sacrificed when its tumor volume quadruples, and its tumor may be suppressed for some time and then grow back. Thus, incomplete repeated measurements arise. The incompleteness or missingness is also caused by drastic tumor shrinkage (<0.01 cm3) or random truncation. Because of the small sample sizes in these models, asymptotic inferences are usually not appropriate. We propose two parametric test procedures based on the EM algorithm and the Bayesian method to compare treatment effects among different groups while accounting for informative censoring. A real xenograft study on a new antitumor agent, temozolomide, combined with irinotecan is analyzed using the proposed methods. [source]

    Dynamic Conditionally Linear Mixed Models for Longitudinal Data

    BIOMETRICS, Issue 1 2002
    M. Pourahmadi
    Summary. We develop a new class of models, dynamic conditionally linear mixed models, for longitudinal data by decomposing the within-subject covariance matrix using a special Cholesky decomposition. Here ,dynamic' means using past responses as covariates and ,conditional linearity' means that parameters entering the model linearly may be random, but nonlinear parameters are nonrandom. This setup offers several advantages and is surprisingly similar to models obtained from the first-order linearization method applied to nonlinear mixed models. First, it allows for flexible and computationally tractable models that include a wide array of covariance structures; these structures may depend on covariates and hence may differ across subjects. This class of models includes, e.g., all standard linear mixed models, antedependence models, and Vonesh-Carter models. Second, it guarantees the fitted marginal covariance matrix of the data is positive definite. We develop methods for Bayesian inference and motivate the usefulness of these models using a series of longitudinal depression studies for which the features of these new models are well suited. [source]

    Testing Measurement Invariance Using Item Response Theory in Longitudinal Data: An Introduction

    Roger E. Millsap
    Abstract, Item response theory (IRT) consists of a set of mathematical models for the probabilities of various responses to test items as a function of item and person characteristics. In longitudinal data, changes in measured variables can only be interpreted if important psychometric features of the measured variables are assumed invariant across time. Measurement invariance is invariance in the relation of a measure to the latent variable underlying it. Measurement invariance in longitudinal studies concerns invariance over time, and IRT provides a useful approach to investigating longitudinal measurement invariance. Commonly used IRT models are described, along with the representation of measurement invariance in IRT. The use of IRT for investigating invariance is then described, along with practical considerations in using IRT for this purpose. Conceptual issues, rather than technical details, are emphasized throughout. [source]

    Intergenerational linkages in antisocial behaviour

    Terence P. Thornberry
    Background,A life-course perspective was used to examine whether a parent's adolescent antisocial behaviour increases the chances of his or her child being involved in antisocial behaviour and, if so, the extent to which different aspects of parenting mediate this relationship. Aim,It was hypothesised that there will be significant levels of intergenerational continuity in antisocial behaviour when parents have ongoing contact with the child, and that stress from parenting and ineffective parenting styles will mediate this relationship. Method,Longitudinal data from the Rochester Intergenerational Study were used to test these issues in structural equation models for fathers and for mothers. Results,Parental antisocial behaviour is significantly related to child antisocial behaviour for mothers and for fathers who have frequent contact with the child, but not for fathers with infrequent contact. For mothers, the impact of adolescent antisocial behaviour on the child's antisocial behaviour is primarily mediated through parenting stress and effective parenting. For high-contact fathers there are multiple mediating pathways that help explain the impact of their adolescent antisocial behaviour on their child's behaviour. Conclusions,The roots of antisocial behaviour extend back at least to the parent's adolescence, and parenting interventions need to consider these long-term processes. Copyright © 2009 John Wiley & Sons, Ltd. [source]

    Stratified analyses for selecting appropriate target patients with diabetic peripheral neuropathy for long-term treatment with an aldose reductase inhibitor, epalrestat

    DIABETIC MEDICINE, Issue 7 2008
    N Hotta
    Abstract Aims The long-term efficacy of epalrestat, an aldose reductase inhibitor, in improving subjective symptoms and nerve function was comprehensively assessed to identify patients with diabetic peripheral neuropathy who responded to epalrestat treatment. Methods Stratified analyses were conducted on data from patients in the Aldose Reductase Inhibitor,Diabetes Complications Trial (ADCT). The ADCT included patients with diabetic peripheral neuropathy, median motor nerve conduction velocity , 40 m/s and with glycated haemoglobin (HbA1c) , 9.0%. Longitudinal data on HbA1c and subjective symptoms of the patients for 3 years were analysed (epalrestat n = 231, control subjects n = 273). Stratified analyses based on background variables (glycaemic control, grades of retinopathy or proteinuria) were performed to examine the relationship between subjective symptoms and nerve function. Multiple logistic regression analyses were conducted. Results Stratified subgroup analyses revealed significantly better efficacy of epalrestat in patients with good glycaemic control and less severe diabetic complications. In the control group, no improvement in nerve function was seen regardless of whether symptomatic benefit was obtained. In the epalrestat group, nerve function deteriorated less or improved in patients whose symptoms improved. The odds ratio of the efficacy of epalrestat vs. control subjects was approximately 2 : 1 (4 : 1 in patients with HbA1c , 7.0%). Conclusion Our results suggest that epalrestat, an aldose reductase inhibitor, will provide a clinically significant means of preventing and treating diabetic neuropathy if used in appropriate patients. [source]

    Community alcohol outlet density and underage drinking

    ADDICTION, Issue 2 2010
    Meng-Jinn Chen
    ABSTRACT Aim This study examined how community alcohol outlet density may be associated with drinking among youths. Methods Longitudinal data were collected from 1091 adolescents (aged 14,16 at baseline) recruited from 50 zip codes in California with varying levels of alcohol outlet density and median household income. Hierarchical linear models were used to examine the associations between zip code alcohol outlet density and frequency rates of general alcohol use and excessive drinking, taking into account zip code median household income and individual-level variables (age, gender, race/ethnicity, personal income, mobility and perceived drinking by parents and peers). Findings When all other factors were controlled, higher initial levels of drinking and excessive drinking were observed among youths residing in zip codes with higher alcohol outlet densities. Growth in drinking and excessive drinking was, on average, more rapid in zip codes with lower alcohol outlet densities. The relation of zip code alcohol outlet density with drinking appeared to be mitigated by having friends with access to a car. Conclusion Alcohol outlet density may play a significant role in initiation of underage drinking during early teenage, especially when youths have limited mobility. Youth who reside in areas with low alcohol outlet density may overcome geographic constraints through social networks that increase their mobility and the ability to seek alcohol and drinking opportunities beyond the local community. [source]

    Effects of Human Capital and Long-Term Human Resources Development and Utilization on Employment Growth of Small-Scale Businesses: A Causal Analysis1

    Andreas Rauch
    The purpose of this study was to explore how three different human resource variables affect employment growth of small-scale enterprises: human capital of business owners, human capital of employees, and human resource development and utilization. The literature suggests different models of how these human resource variables affect business outcomes. Longitudinal data from 119 German business owners provided support for a main effect model indicating that owners' human capital as well as employee human resource development and utilization affect employment growth. Moreover, human resources development and utilization was most effective when the human capital of employees was high. We conclude that human resources are important factors predicting growth of small-scale enterprises. [source]

    Scottish smoke-free legislation and trends in smoking cessation

    ADDICTION, Issue 11 2008
    Freya J. I. Fowkes
    ABSTRACT Aim To investigate trends in smoking cessation before and after the introduction of Scottish smoke-free legislation and to assess the perceived influence of the legislation on giving up smoking and perceptions of the legislation in smokers. Design, setting and participants Longitudinal data on smoking cessation were obtained from 1998 to 2007 on a cohort of 3350 Scottish adults aged between 50 and 75 years at baseline. All members of the cohort were participating in a clinical trial of aspirin in people at moderately increased risk of cardiovascular events. A subgroup of 474 participants who had smoked in the year prior to the introduction of legislation in March 2006 also completed a questionnaire on the influence and perceptions of the smoke-free legislation following its introduction. Measurements Smoking status was recorded yearly, including dates of quitting and restarting. Participants who gave up smoking for at least 3 months were recorded as having quit smoking. The questionnaire included scales on whether the smoke-free legislation had helped/influenced cessation, made the individual think about/prompt them to quit and perceptions of the legislation. Findings The odds of smokers quitting annually increased throughout the 7-year period prior to introduction of the smoke-free legislation to 2 years afterwards (odds ratio 1.09, 95% confidence interval 1.05,1.12, P < 0.001). During 2006, the pattern of quarterly quitting rates changed, with an increase in quit rates (to 5.1%) in the 3-month period prior to introduction of the legislation (January,March 2006). Socio-economic status was not related to smoking cessation. In the subgroup completing the questionnaire (n = 474), 57 quit smoking between June 2005 and May 2007 and 43.9% of these said that the smoke-free legislation had helped them to quit. Most (>70%) smokers were positive about the legislation, especially those from more affluent compared with more deprived communities (P = 0.01). Conclusions The Scottish smoke-free legislation was associated with an increase in the rate of smoking cessation in the 3-month period immediately prior to its introduction. Overall quit rates in the year the legislation was introduced and the subsequent year were consistent with a gradual increase in quit rates prior to introduction of the legislation. Socio-economic status was not related to smoking cessation, but individuals from more affluent communities were more positive about the legislation. [source]

    Alcohol-specific rules, personality and adolescents' alcohol use: a longitudinal person,environment study

    ADDICTION, Issue 7 2007
    Haske Van Der Vorst
    ABSTRACT Aims To examine the bi-directional associations between providing alcohol-specific rules and adolescents' alcohol use. Further, to explore person,environment interactions, we tested whether Big Five personality traits moderate the assumed association between providing alcohol-specific rules and adolescents' alcohol use. Design Longitudinal data (three waves in 2 years) from 428 families, consisting of both parents and two adolescents (aged 13,16 years) were used for the analyses. Analyses were conducted on four samples: a group of older adolescents and a group of younger adolescents who already consumed alcohol, and a group of older and younger adolescents who were not drinking at baseline measurement. Findings In general, results of structural equation modelling showed that providing clear alcohol-specific rules lowers the likelihood of drinking initiation, regardless of the age of the youngsters. Once adolescents have established a drinking pattern, the impact of parental alcohol-specific rules declined or even disappeared. Finally, the Big Five personality traits did not moderate the association between providing alcohol-specific rules and adolescents' alcohol involvement. Conclusions In sum, in particular during the initiation phase of drinking, parents could prevent the drinking of their offspring, regardless of the age or personality of their youngsters, by providing clear alcohol-specific rules. [source]

    Bilateral medial temporal lobe damage does not affect lexical or grammatical processing: Evidence from amnesic patient H.M.

    HIPPOCAMPUS, Issue 4 2001
    Elizabeth A. Kensinger
    Abstract In the most extensive investigation to date of language in global amnesia, we acquired data from experimental measures and examined longitudinal data from standardized tests, to determine whether language function was preserved in the amnesic patient H.M. The experimental measures indicated that H.M. performed normally on tests of lexical memory and grammatical function, relative to age- and education-matched control participants. Longitudinal data from four Wechsler subtests (Information, Comprehension, Similarities, and Vocabulary), that H.M. had taken 20 times between 1953 (preoperatively) and 2000, indicated consistent performance across time, and provided no evidence of a lexical memory decrement. We conclude that medial temporal lobe structures are not critical for retention and use of already acquired lexical information or for grammatical processing. They are, however, required for acquisition of lexical information, as evidenced in previous studies revealing H.M.'s profound impairment at learning new words. Hippocampus 2001;11:347,360. © 2001 Wiley-Liss, Inc. [source]

    Intellectual abilities and white matter microstructure in development: A diffusion tensor imaging study

    HUMAN BRAIN MAPPING, Issue 10 2010
    Christian K. Tamnes
    Abstract Higher-order cognitive functions are supported by distributed networks of multiple interconnected cortical and subcortical regions. Efficient cognitive processing depends on fast communication between these regions, so the integrity of the connections between them is of great importance. It is known that white matter (WM) development is a slow process, continuing into adulthood. While the significance of cortical maturation for intellectual development is described, less is known about the relationships between cognitive functions and maturation of WM connectivity. In this cross-sectional study, we investigated the associations between intellectual abilities and development of diffusion tensor imaging (DTI) derived measures of WM microstructure in 168 right-handed participants aged 8,30 years. Independently of age and sex, both verbal and performance abilities were positively related to fractional anisotropy (FA) and negatively related to mean diffusivity (MD) and radial diffusivity (RD), predominantly in the left hemisphere. Further, verbal, but not performance abilities, were associated with developmental differences in DTI indices in widespread regions in both hemispheres. Regional analyses showed relations with both FA and RD bilaterally in the anterior thalamic radiation and the cortico-spinal tract and in the right superior longitudinal fasciculus. In these regions, our results suggest that participants with high verbal abilities may show accelerated WM development in late childhood and a subsequent earlier developmental plateau, in contrast to a steadier and prolonged development in participants with average verbal abilities. Longitudinal data are needed to validate these interpretations. The results provide insight into the neurobiological underpinnings of intellectual development. Hum Brain Mapp, 2010. © 2010 Wiley-Liss, Inc. [source]

    Night eating syndrome in young adult women: Prevalence and correlates

    Ruth H. Striegel-Moore PhD
    Abstract Objective The current study examined the prevalence and clinical significance of night eating syndrome (NES) in a community cohort of Black and White women. Method We assessed 682 Black and 659 White women for NES, eating disorders, and psychiatric symptomatology. Results The prevalence was 1.6% (22 of 1,341; Blacks [n = 20]; Whites [n = 2]). Comparisons between identified Black women and the remaining Black participants revealed no significant differences in obesity, psychiatric comorbidity, or self-reported psychiatric distress. Comorbidity with eating disorders as outlined in the 4th ed. of the Diagnostic and Statistical Manual of Mental Disorders (Washington, DC: American Psychiatric Association) was low (n = 1 [4.5%]). Black NES women were significantly less likely than Black non-NES women to be overweight and significantly more likely to have two or more children. Discussion NES was rare in this sample of young women. Low comorbidity of NES with other eating disorders suggests that NES may be distinct from the DSM-IV recognized eating disorders. Longitudinal data are needed to determine the long-term health implications of this behavioral pattern. © 2005 by Wiley Periodicals, Inc. [source]

    Self-esteem, academic self-concept, and aggression at school

    Laramie D. Taylor
    The present study explores the relation between academic self-concept, self-esteem, and aggression at school. Longitudinal data from a racially diverse sample of middle-school students were analyzed to explore how academic self-concept influenced the likelihood of aggressing at school and whether high self-concept exerted a different pattern of influence when threatened. Data include self-reported academic self-concept, school-reported academic performance, and parent-reported school discipline. Results suggest that, in general, students with low self-concept in achievement domains are more likely to aggress at school than those with high self-concept. However, there is a small sample of youth who, when they receive contradictory information that threatens their reported self-concept, do aggress. Global self-esteem was not found to be predictive of aggression. These results are discussed in the context of recent debates on whether self-esteem is a predictor of aggression and the use of a more proximal vs. general self-measure in examining the self-esteem and aggression relation. Aggr. Behav. 32:1,7, 2007. © 2006 Wiley-Liss; Inc. [source]