bluuebunny/crossref_metadata_2025 huggingface dataset:
Use of Summary Measures to Adjust for Informative Missingness in Repeated Measures Data with Random Effects
Authors: Wu Margaret C. , Follmann Dean A.
Published: May 2004

Summary. We discuss how to apply the conditional informative missing model of Wu and Bailey (1989, Biometrics 45, 939–955) to the setting where the probability of missing a visit depends on the random effects of the primary response in a time‐dependent fashion. This includes the case where the probability of missing a visit depends on the true value of the primary response. Summary measures for missingness that are weighted sums of the indicators of missed visits are derived for these situations. These summary measures are then incorporated as covariates in a random effects model for the primary response. This approach is illustrated by analyzing data collected from a trial of heroin addicts where missed visits are informative about drug test results. Simulations of realistic experiments indicate that these time‐dependent summary measures also work well under a variety of informative censoring models. These summary measures can achieve large reductions in estimation bias and mean squared errors relative to those obtained by using other summary measures.
Semiparametric Methods for Multiple Exposure Mismeasurement and a Bivariate Outcome in HIV Vaccine Trials
Authors: Golm Gregory T. , Elizabeth Halloran M. , Longini Ira M.
Published: May 2004

Summary.Exposure to infection information is important for estimating vaccine efficacy, but it is difficult to collect and prone to missingness and mismeasurement. We discuss study designs that collect detailed exposure information from only a small subset of participants while collecting crude exposure information from all participants and treat estimation of vaccine efficacy in the missing data/measurement error framework. We extend the discordant partner design for HIV vaccine trials of Golm, Halloran, and Longini (1998,Statistics in Medicine,17, 2335–2352.) to the more complex augmented trial design of Longini, Datta, and Halloran (1996,Journal of Acquired Immune Deficiency Syndromes and Human Retrovirology13, 440–447) and Datta, Halloran, and Longini (1998,Statistics in Medicine17, 185–200). The model for this design includes three exposure covariates and both univariate and bivariate outcomes. We adapt recently developed semiparametric missing data methods of Reilly and Pepe (1995,Biometrika82, 299–314), Carroll and Wand (1991,Journal of the Royal Statistical Society, Series B53, 573–585), and Pepe and Fleming (1991,Journal of the American Statistical Association86, 108–113) to the augmented vaccine trial design. We demonstrate with simulated HIV vaccine trial data the improvements in bias and efficiency when combining the different levels of exposure information to estimate vaccine efficacy for reducing both susceptibility and infectiousness. We show that the semiparametric methods estimate both efficacy parameters without bias when the good exposure information is either missing completely at random or missing at random. The pseudolikelihood method of Carroll and Wand (1991) and Pepe and Fleming (1991) was the more efficient of the two semiparametric methods.
An Estimation Method for the Semiparametric Mixed Effects Model
Authors: Tao Huageng , Palta Mari , Yandell Brian S. , Newton Michael A.
Published: May 2004

Summary.A semiparametric mixed effects regression model is proposed for the analysis of clustered or longitudinal data with continuous, ordinal, or binary outcome. The common assumption of Gaussian random effects is relaxed by using a predictive recursion method (Newton and Zhang, 1999) to provide a nonparametric smooth density estimate. A new strategy is introduced to accelerate the algorithm. Parameter estimates are obtained by maximizing the marginal profile likelihood by Powell's conjugate direction search method. Monte Carlo results are presented to show that the method can improve the mean squared error of the fixed effects estimators when the random effects distribution is not Gaussian. The usefulness of visualizing the random effects density itself is illustrated in the analysis of data from the Wisconsin Sleep Survey. The proposed estimation procedure is computationally feasible for quite large data sets.
Estimating Intraclass Correlation for Binary Data
Authors: Ridout Martin S. , Demétrio Clarice G. B. , Firth David
Published: May 2004

Summary.This paper reviews many different estimators of intraclass correlation that have been proposed for binary data and compares them in an extensive simulation study. Some of the estimators are very specific, while others result from general methods such as pseudo‐likelihood and extended quasi‐likelihood estimation. The simulation study identifies several useful estimators, one of which does not seem to have been considered previously for binary data. Estimators based on extended quasi‐likelihood are found to have a substantial bias in some circumstances.
Response Surfaces for Overdispersion in the Study of the Conditions for Fish Eggs Hatching
Authors: Lindsey J. K.
Published: May 2004

Summary.Response surface methodology, originally developed for determining optimal conditions in industrial experiments, was early adapted to experiments in marine ecology. However, these involved studying the shape of the complete response surface, not only detecting the optimum, and often had counts or durations as the response variable. Thus, nonlinear, nonnormal response models were required. For counts, binomial and beta‐binomial models have been used, the latter because of substantial over dispersion. In closely controlled experiments, over dispersion among units held under the same conditions might indicate that some mishap has occurred in conducting the study. One possible check is to model the dispersion as a second response surface. This procedure is used to show that over dispersion in fish egg hatching experiments has a biological explanation in that it occurs only under suboptimal hatching conditions.
Discriminant Analysis of Event‐Related Potential Curves Using Smoothed Principal Components
Authors: Do Kim‐Anh , Kirk Katherine
Published: May 2004

Summary.Principal component analysis enhanced by the use of smoothing is used in conjunction with discriminant analysis techniques to devise a statistical classification method for the analysis of event‐related potential data. A training set of premedication potentials collected from adolescents with attention‐deficit hyperactive disorder (ADHD) is used to predict whether adolescents from an independent subject group will respond to long‐term medication. Comparison of outcome prediction rates demonstrates that this method, which uses information from the whole ERP curve, is superior to the classification technique currently used by clinicians, which is based on a single ERP curve feature. The need to administer an initial dose of medication to classify patients is also eliminated.
Likelihood Methods for Incomplete Longitudinal Binary Responses with Incomplete Categorical Covariates
Authors: Lipsitz Stuart R. , Ibrahim Joseph G. , Fitzmaurice Garrett M.
Published: May 2004

Summary.We consider longitudinal studies in which the outcome observed over time is binary and the covariates of interest are categorical. With no missing responses or covariates, one specifies a multinomial model for the responses given the covariates and uses maximum likelihood to estimate the parameters. Unfortunately, incomplete data in the responses and covariates are a common occurrence in longitudinal studies. Here we assume the missing data are missing at random (Rubin, 1976,Biometrika63, 581–592). Since all of the missing data (responses and covariates) are categorical, a useful technique for obtaining maximum likelihood parameter estimates is the EM algorithm by the method of weights proposed in Ibrahim (1990,Journal of the American Statistical Association85, 765–769). In using the EM algorithm with missing responses and covariates, one specifies the joint distribution of the responses and covariates. Here we consider the parameters of the covariate distribution as a nuisance. In data sets where the percentage of missing data is high, the estimates of the nuisance parameters can lead to highly unstable estimates of the parameters of interest. We propose a conditional model for the covariate distribution that has several modeling advantages for the EM algorithm and provides a reduction in the number of nuisance parameters, thus providing more stable estimates in finite samples.
Approximate Monte Carlo Conditional Inference in Exponential Families
Authors: Kolassa John E. , Tanner Martin A.
Published: May 2004

Summary.This article presents an algorithm for approximate frequentist conditional inference on two or more parameters for any regression model in the Generalized Linear Model (GLIM) family. We thereby extend highly accurate inference beyond the cases of logistic regression and contingency tables implimented in commercially available software. The method makes use of the double saddlepoint approximations of Skovgaard (1987,Journal of Applied Probability24, 875–887) and Jensen (1992,Biometrika79, 693–703) to the conditional cumulative distribution function of a sufficient statistic given the remaining sufficient statistics. This approximation is then used in conjunction with noniterative Monte Carlo methods to generate a sample from a distribution that approximates the joint distribution of the sufficient statistics associated with the parameters of interest conditional on the observed values of the sufficient statistics associated with the nuisance parameters. This algorithm is an alternate approach to that presented by Kolassa and Tanner (1994,Journal of the American Statistical Association89, 697–702), in which a Markov chain is generated whose equilibrium distribution under certain regularity conditions approximates the joint distribution of interest. In Kolassa and Tanner (1994), the Gibbs sampler was used in conjunction with these univariate conditional distribution function approximations. The method of this paper does not require the construction and simulation of a Markov chain, thus avoiding the need to develop regularity conditions under which the algorithm converges and the need for the data analyst to check convergence of the particular chain. Examples involving logistic and truncated Poisson regression are presented.
Robustness of the Latent Variable Model for Correlated Binary Data
Authors: Tan Ming , Qu Yinsheng , Sunil Rao J.
Published: May 2004

Summary.The marginal regression model offers a useful alternative to conditional approaches to analyzing binary data (Liang, Zeger, and Qaqish, 1992,Journal of the Royal Statistical Society, Series B54, 3–40). Instead of modelling the binary data directly as do Liang and Zeger (1986,Biometrika73, 13–22), the parametric marginal regression model developed by Qu et al. (1992,Biometrics48, 1095–1102) assumes that there is an underlying multivariate normal vector that gives rise to the observed correlated binary outcomes. Although this parametric approach provides a flexible way to model different within‐cluster correlation structures and does not restrict the parameter space, it is of interest to know how robust the parameter estimates are with respect to choices of the latent distribution. We first extend the latent modelling to include multivariatet‐distributed latent vectors and assess the robustness in this class of distributions. Then we show through a simulation that the parameter estimates are robust with respect to the latent distribution even if latent distribution is skewed. In addtion to this empirical evidence for robustness, we show through the iterative algorithm that the robustness of the regression coefficents with respect to misspecifications of covariance structure in Liang and Zeger's model in fact indicates robustness with respect to underlying distributional assumptions of the latent vector in the latent variable model.
A Group Sequential Test for Survival Trials: An Alternative to Rank‐Based Procedures
Authors: Li Zhengqing
Published: May 2004

Summary.A method of interim monitoring is described for survival trials in which the proportional hazards assumption may not hold. This method extends the test statistics based on the cumulative weighted difference in the Kaplan‐Meier estimates (Pepe and Fleming, 1989,Biometrics45, 497–507) to the sequential setting. Therefore, it provides a useful alternative to the group sequential linear rank tests. With an appropriate weight function, the test statistic itself provides an estimator for the cumulative weighted difference in survival probabilities, which is an interpretable measure for the treatment difference, especially when the proportional hazards model fails. The method is illustrated based on the design of a real trial. The operating characteristics are studied through a small simulation.
The Utility of Mixed‐Form Likelihoods
Authors: Cook Richard J. , Farewell Vernon T.
Published: May 2004

Summary.We highlight a feature of likelihood‐based methods that provides flexibility in model formulation and inference. In particular, overall likelihoods that consist of likelihood contributions with different forms are considered. The particular forms may be predetermined by design criteria or may be selected based on features of the data. Inferences based on such mixed‐form likelihoods are valid provided standard regularity conditions hold and the parameters of interest have the same interpretation in the various forms. The advantages of constructing overall likelihoods in this way are illustrated by applications involving the analysis of 2 × 2 ×Ktables and left‐censored water quality data.
The Use of Mixed Logit Models to Reflect Heterogeneity in Capture‐Recapture Studies
Authors: Coull Brent A. , Agresti Alan
Published: May 2004

Summary. We examine issues in estimating population size N with capture‐recapture models when there is variable catchability among subjects. We focus on a logistic‐normal mixed model, for which the logit of the probability of capture is an additive function of a random subject and a fixed sampling occasion parameter. When the probability of capture is small or the degree of heterogeneity is large, the log‐likelihood surface is relatively flat and it is difficult to obtain much information about N. We also discuss a latent class model and a log‐linear model that account for heterogeneity and show that the log‐linear model has greater scope. Models assuming homogeneity provide much narrower intervals for N but are usually highly overly optimistic, the actual coverage probability being much lower than the nominal level.
Analyzingk(2 × 2) Tables Under Cluster Sampling
Authors: Begg Melissa Dowd
Published: May 2004

Summary.In many data analytic applications, such as ophthalmologic, longitudinal, or periodontal studies, multiple observations are recorded over several sites (or timepoints) within the same subject, bringing about dependence between measurements. This correlation, in turn, precludes the use of standard statistical methods that assume independence between outcome measurements. For example, the Mantel‐Haenszel statistic, used to assess association between a binary outcome and a binary exposure while adjusting for a categorical covariate, does not follow the usual chi‐squared distribution under the null hypothesis when there is correlation between observations. A modified Mantel‐Haenszel procedure, which makes adjustment for dependence, is proposed. No particular correlation structure is assumed for responses within a cluster. This closed‐form adjustment stems from Liang and Zeger's (1986,Biometrika73, 13–22) generalized estimating equations approach for clustered data. The difference between this tabular (i.e., noniterative) technique and many earlier tabular methods is that the current method allows for consideration of site‐specific exposure and covariate information. An example from a periodontal research study illustrates application of the method.
Regression Modeling of Ordinal Data with Nonzero Baselines
Authors: Xie Minge , Simpson Douglas G.
Published: May 2004

Summary.This paper develops regression models for ordinal data with nonzero control response probabilities. The models are especially useful in dose‐response studies where the spontaneous or natural response rate is nonnegligible and the dosage is logarithmic. These models generalize Abbott's formula, which has been commonly used to model binary data with nonzero background observations. We describe a biologically plausible latent structure and develop an EM algorithm for fitting the models. The EM algorithm can be implemented using standard software for ordinal regression. A toxicology data set where the proposed model fits the data but a more conventional model fails is used to illustrate the methodology.
Nonparametric Estimation of a Delay Distribution Based on Left‐Censored and Right‐Truncated Data
Authors: Cui Jisheng
Published: May 2004

Summary.This paper proposes a nonparametric method for estimating a delay distribution based on left‐censored and right‐truncated data. A variance‐covariance estimator is provided. The method is applied to the Australian AIDS data in which some data are left censored and some data are not left censored. This situation arises with AIDS case‐reporting data in Australia because reporting delays were recorded only from November 1990 rather than from the beginning of the epidemic there. It is shown that inclusion of the left‐censored data, as opposed to analyzing only the uncensored data, improves the precision of the estimate.
A Study of Deleterious Gene Structure in Plants Using Markov Chain Monte Carlo
Authors: Lee Jae Kyun , Lascoux Martin , Newton Michael A. , Nrdheim Erik V.
Published: May 2004

Summary.The characteristics of deleterious genes have been of great interest in both theory and practice in genetics. Because of the complex genetic mechanism of these deleterious genes, most current studies try to estimate the overall magnitude of mortality effects on a population, which is characterized classically by the number of lethal equivalents. This number is a combination of several parameters, each of which has a distinct biological effect on genetic mortality. In conservation and breeding programs, it is important to be able to distinguish among different combinations of these parameters that lead to the same number of lethal equivalents, such as a large number of mildly deleterious genes or a few lethal genes. The ability to distinguish such parameter combinations requires more than one generation of mating. We propose a model for survival data from a two‐generation mating experiment on the plant speciesBrassica rapa, and we enable inference with Markov chain Monte Carlo. This computational strategy is effective because a vast amount of missing genotype information must be accounted for. In addition to the lethal equivalents, the two‐generation data provide separate information on the average intensity of mortality and the average number of deleterious genes carried by an individual. In our Markov chain Monte Carlo algorithm, we use a vector proposal distribution to overcome inefficiency of a single‐site Gibbs sampler. Information about environmental effects is obtained from an outcrossing experiment conducted in parallel with the two‐generation mating experiments.
A Bayesian Approach to Ordering Gene Markers
Authors: George A. W. , Mengersen K. L. , Davis G. P.
Published: May 2004

Summary.A technique is presented whereby a marker map can be constructed using resource family data with an entire class of missing data. The focus is on a half‐sib design where there is only information on a single parent and its progeny. A Bayesian approach is utilised with solutions obtained via a Markov chain Monte Carlo algorithm. Features of the approach include the capacity to determine parameters for the ungenotyped dam population, the ability to incorporate published information and its reliability, and the production of posterior densities and the consequent deduction of a wide range of inferences. These features are demonstrated through the analysis of simulated and experimental data.
Symmetric Tests and Confidence Intervals for Survival Probabilities and Quantiles of Censored Survival Data
Authors: Barber Stuart , Jennison Christopher
Published: May 2004

Summary.We describe existing tests and introduce two new tests concerning the value of a survival function. These tests may be used to construct a confidence interval for the survival probability at a given time or for a quantile of the survival distribution. Simulation studies show that error rates can differ substantially from their nominal values, particularly at survival probabilities close to zero or one. We recommend our new constrained bootstrap test for its good overall performance.
Finite Mixture Modeling with Mixture Outcomes Using the EM Algorithm
Authors: Muthén Bengt , Shedden Kerby
Published: May 2004

Summary. This paper discusses the analysis of an extended finite mixture model where the latent classes corresponding to the mixture components for one set of observed variables influence a second set of observed variables. The research is motivated by a repeated measurement study using a random coefficient model to assess the influence of latent growth trajectory class membership on the probability of a binary disease outcome. More generally, this model can be seen as a combination of latent class modeling and conventional mixture modeling. The EM algorithm is used for estimation. As an illustration, a random‐coefficient growth model for the prediction of alcohol dependence from three latent classes of heavy alcohol use trajectories among young adults is analyzed.
Generalized Estimating Equations for Ordinal Categorical Data: Arbitrary Patterns of Missing Responses and Missingness in a Key Covariate
Authors: Toledano Alicia Y. , Gatsonis Constantine
Published: May 2004

Summary.We propose methods for regression analysis of repeatedly measured ordinal categorical data when there is nonmonotone missingness in these responses and when a key covariate is missing depending on observables. The methods use ordinal regression models in conjunction with generalized estimating equations (GEEs). We extend the GEE methodology to accommodate arbitrary patterns of missingness in the responses when this missingness is independent of the unobserved responses. We further extend the methodology to provide correction for possible bias when missingness in knowledge of a key covariate may depend on observables. The approach is illustrated with the analysis of data from a study in diagnostic oncology in which multiple correlated receiver operating characteristic curves are estimated and corrected for possible verification bias when the true disease status is missing depending on observables.
Modeling Random Effects for Censored Data by a Multivariate Normal Regression Model
Authors: Klein John P. , Pelz Corey , Zhang Mei‐jie
Published: May 2004

Summary.A normal distribution regression model with a frailty‐like factor to account for statistical dependence between the observed survival times is introduced. This model, as opposed to standard hazard‐based frailty models, has survival times that, conditional on the shared random effect, have an accelerated failure time representation. The dependence properties of this model are discussed and maximum likelihood estimation of the model's parameters is considered. A number of examples are considered to illustrate the approach. The estimated degree of dependence is comparable to other models, but the present approach has the advantage that the interpretation of the random effect is simpler than in the frailty model.
Summarizing the Motion of Self‐Propelled Cells: Applications to Sperm Motility
Authors: Dunson D. B. , Weinberg C. R. , Perreault S. D. , Chapin R. E.
Published: May 2004

Summary. Proper characterization of the motion of spermatozoa is an important prerequisite for interpreting differences in sperm motility that might arise from exposure to toxicants. Patterns of sperm movement can be extremely complex. On the basis of an exponential model that relates the discretely approximated curvilinear velocity to the tracking rate, we develop a statistic that indexes the predictability of the path for individual sperm. We summarize the path of each sperm using this and two other statistics: (1) the path displacement velocity and (2) linearity of movement. We apply the method to a set of rat sperm tracks representative of both normal and abnormal motion characteristics.
The Knox Method and Other Tests for Space‐Time Interaction
Authors: Kulldorff Martin , Hjalmars Ulf
Published: May 2004

Summary. The Knox method, as well as other tests for space‐time interaction, are biased when there are geographical population shifts, i.e., when there are different percent population growths in different regions. In this paper, the size of the population shift bias is investigated for the Knox test, and it is shown that it can be a considerable problem. A Monte Carlo method for constructing unbiased space‐time interaction tests is then presented and illustrated on the Knox test as well as for a combined Knox test. Practical implications are discussed in terms of the interpretation of past results and the design of future studies.
Reduced Sensitivity to Hidden Bias at Upper Quantiles in Observational Studies with Dilated Treatment Effects
Authors: Rosenbaum Paul R.
Published: May 2004

Summary.When a treatment has a dilated effect, with larger effects when responses are higher, there can be much less sensitivity to bias at upper quantiles than at lower quantiles; i.e., small, plausible hidden biases might explain the ostensible effect of the treatment for many subjects, and yet only quite large hidden biases could explain the effect on a few subjects having dramatically elevated responses. An example concerning kidney function of cadmium workers is discussed in detail. In that example, the treatment effect is far from additive: It is plausibly zero at the lower quartile of responses to control, and it is large and fairly insensitive to bias at the upper quartile.
Robust Regression for Clustered Data with Application to Binary Responses
Authors: Preisser John S. , Qaqish Bahjat F.
Published: May 2004

Summary.Generalized estimating equations (GEE) can be highly influenced by the presence of unusual data points. A generalization of the GEE procedure, which yields parameter estimates and fitted values that are resistant to influential data, is introduced. Resistant generalized estimating equations (REGEE) include weights in the estimating equations to downweight influential observations or clusters. Influential observations are downweighted according to their leverage or residual in an example of correlated binary regression applied to 137 urinary incontinent elderly patients from 38 medical practices.
A Global Goodness‐of‐Fit Statistic for Cox Regression Models
Authors: Parzen Michael , Lipsitz Stuart R.
Published: May 2004

Summary.In this paper, a global goodness‐of‐fit test statistic for a Cox regression model, which has an approximate chi‐squared distribution when the model has been correctly specified, is proposed. Our goodness‐of‐fit statistic is global and has power to detect if interactions or higher order powers of covariates in the model are needed. The proposed statistic is similar to the Hosmer and Lemeshow (1980,Communications in StatisticsA10, 1043–1069) goodness‐of‐fit statistic for binary data as well as Schoenfeld's (1980,Biometrtka67, 145–153) statistic for the Cox model. The methods are illustrated using data from a Mayo Clinic trial in primary billiary cirrhosis of the liver (Fleming and Harrington, 1991,Counting Processes and Survival Analysis), in which the outcome is the time until liver transplantation or death. The are 17 possible covariates. Two Cox proportional hazards models are fit to the data, and the proposed goodness‐of‐fit statistic is applied to the fitted models.
On Logit Confidence Intervals for the Odds Ratio with Small Samples
Authors: Agresti Alan
Published: May 2004

Summary.Unless the true association is very strong, simple large‐sample confidence intervals for the odds ratio based on the delta method perform well even for small samples. Such intervals include the Woolf logit interval and the related Gart interval based on adding .5 before computing the log odds ratio estimate and its standard error. The Gart interval smooths the observed counts toward the model of equiprobability, but one obtains better coverage probabilities by smoothing toward the independence model and by extending the interval in the appropriate direction when a cell count is zero.
Bayesian Monitoring of Event Rates with Censored Data
Authors: Follmann Dean A. , Albert Paul S.
Published: May 2004

Summary. A Bayesian approach to monitoring event rates with censored data is proposed. A Dirichlet prior for discrete time event probabilities is blended with discrete survival times to provide a posterior distribution that is a mixture of Dirichlets. Approximation of the posterior distribution via data augmentation is discussed. Practical issues involved in implementing the procedure are discussed and illustrated with a simulation of the single arm Cord Blood Transplantation Study where 6‐month survival is monitored.
Exact Tests of Goodness of Fit of Log‐Linear Models for Rates
Authors: McDonald John W. , Smith Peter W. F. , Forster Jonathan J.
Published: May 2004

Summary.We propose Metropolis–Hastings sampling methods for estimating the exact conditional p‐value for tests of goodness of fit of log‐linear models for mortality rates and standardized mortality ratios. We focus on two‐way tables, where the required conditional distribution is a multivariate noncentral hypergeometric distribution with known noncentrality parameter. Two examples are presented: a 2 × 3 table, where the exact results, obtained by enumeration, are available for comparison, and a 9 × 7 table, where Monte Carlo methods provide the only feasible approach for exact inference.
Mixed Effects Models with Censored Data with Application to HIV RNA Levels
Authors: Hughes James P.
Published: May 2004

Summary.Mixed effects models are often used for estimating fixed effects and variance components in longitudinal studies of continuous data. When the outcome being modelled is a laboratory measurement, however, it may be subject to lower and upper detection limits (i.e., censoring). In this paper, the usual EM estimation procedure for mixed effects models is modified to account for left and/or right censoring.
Multivariate Survival Analysis with Positive Stable Frailties
Authors: Qiou Zuqiang , Ravishanker Nalini , Dey Dipak K.
Published: May 2004

Summary.In this paper, we describe Bayesian modeling of dependent multivariate survival data using positive stable frailty distributions. A flexible baseline hazard formulation using a piecewise exponential model with a correlated prior process is used. The estimation of the stable law parameter together with the parameters of the (conditional) proportional hazards model is facilitated by a modified Gibbs sampling procedure. The methodology is illustrated on kidney infection data (McGilchrist and Aisbett, 1991).
Predictive Margins with Survey Data
Authors: Graubard Barry I. , Korn Edward L.
Published: May 2004

Summary. In the analysis of covariance, the display of adjusted treatment means allows one to compare mean (treatment) group outcomes controlling for different covariate distributions in the groups. Predictive margins are a generalization of adjusted treatment means to nonlinear models. The predictive margin for group r represents the average predicted response if everyone in the sample had been in group r. This paper discusses the use of predictive margins with complex survey data, where an important consideration is the choice of covariate distribution used to standardize the predictive margin. It is suggested that the textbook formula for the standard error of an adjusted treatment mean from the analysis of covariance may be inappropriate for applications involving survey data. Applications are given using data from the 1992 National Health Interview Survey (NHIS) and the Epidemiologic Followup Study to the first National Health and Nutrition Examination Survey (NHANES I).
Changes in values cognition after rating values‐associated feelings
Authors: HIROSE HARUJI
Published: May 2004

Abstract: This study investigated values change as an outcome of assessing feelings elicited by contemplating the actualization of the same values. The subjects were divided into an experimental and a control group. They completed three tests in a single session. In the pretest, all subjects were asked to rate the importance of 20 values items on a 4‐point scale. Subjects in the experimental group were then asked to rate the degree of pleasure they would feel in actualizing each of the values, while those in the control group rated the degree of inequality they considered women suffer in the actualization of each of the values. The post‐test consisted of the same items as the pretest. The subjects in the experimental group completed a second post‐test 3 months later. It was found that the experimental procedure produced significant change in the ratings of 15 values at the post‐test, whereas the control procedure produced change in only five. The results suggest that rating one's emotional response to a value‐related situation can change one's cognition of that value. At the post‐test 3 months later, only 3‐value ratings remained changed.
Institutional changes in fisheries governance: the case of the saimaa ringed seal, Phoca hispida saimensis, conservation
Authors: Tonder M. , Salmi P.
Published: May 2004

Institutional and paradigmatic changes in Finnish fisheries and environmental governance in the light of the protection of the Saimaa ringed seal, Phoca hispida saimensis Nordq., are discussed. The analysis is based on written statements by stakeholder institutions in response to a proposal by the Ministry of Agriculture and Forestry to restrict fishing in the breeding areas of the ringed seal. The controversy about the conservation of the Saimaa ringed seal is a revealing example of transition of an environmental conflict into a larger scale criticism of ongoing transformations in society related to paradigmatic changes of fisheries governance institutions. In addition to changed emphasis in different basic world outlooks, the contexts of the paradigms have changed. The policy restricting fishing by the authorities to support seal conservation is located between the paradigms of conservation and rationalisation. The Government has aligned itself with modern conservation ideas, but at the same time aroused strong resistance from the local level of fisheries governance. The local and regional stakeholders emphasised private water ownership, local knowledge and already established local agreements for seal conservation.
The role of catchment scale environmental management in freshwater fish conservation
Authors: Collares‐Pereira M. J. , Cowx I. G.
Published: May 2004

Freshwater fishes are threatened by a wide array of factors, but anthropogenic disturbance, especially species introductions and translocations, impoundment of rivers, pollution, habitat degradation and overexploitation, seem to underlie the decline and extinction of many species. Although actions have been taken to address some precarious situations, traditional methods of conservation management, for example, regulation of exploitation, designation of nature reserves, captive breeding programmes for stock enhancement, do not seem to be as effective for freshwater fishes as for other animal groups. This paper summarises the present threats to freshwater fishes, identifies the underlying issues and reviews the problems relating to current management practices. It uses a case study from an Iberian highly endangered cyprinid fish –Anaecypris hispanica (Steindachner) – to highlight the options for action to protect the freshwater fish natural heritage for future generations. It is concluded that if conducted in a comprehensive manner, involving the wider public and all stakeholders, fish conservation management will confer wider environmental benefits and protect biodiversity for future generations.
Angling impact on conservation of Spanish stream‐dwelling brown trout Salmo trutta
Authors: Almodóvar A. , Nicola G. G.
Published: May 2004

Spanish brown trout, Salmo trutta L., populations are currently overexploited as a result of unsuitable management activities, and their genetic uniqueness is threatened by introgression of foreign genes because of stocking. In this study, the status and management of trout fisheries were reviewed and the effects of fishery management on abundance, production and life history of trout in Spain assessed. Angling exploitation has reduced the mean age, the age diversity and number of trout exceeding the minimum size in exploited sections. Likewise, exploited areas show a general decrease in overall abundance parameters and production, as well as a depletion of the breeding stock and population fecundity. Current minimum size limit control reduces the spawning chances in fast‐growing populations because of higher susceptibility to angling harvest. The effects of fishery management on population dynamics, production and life‐history characteristics exhibit different patterns among Spanish rivers, and seem to depend on the environmental and biological characteristics of the populations. The current declining trend of brown trout could be reduced by river‐specific management and alternative fishing regulations.
A review of 47 cases of unerupted maxillary incisors
Authors: Betts A. , Camilleri G.E.
Published: May 2004

Objectives.To study the prevalence of aetiological factors associated with unerupted maxillary incisors and to follow the outcome of treatment in a study of 47 cases.

Design. A retrospective study.

Setting. The Dental Department, St Luke's Hospital, Malta, the School Dental Clinic of Malta and the private practices of two orthodontists in Malta.

Sample and methods. Forty‐seven patients with a total of 53 unerupted maxillary incisors were classified according to the aetiological factors causing non‐eruption. The relative prevalence of the various aetiologies were ascertained, and the outcome after treatment was recorded to assess the efficacy of the treatment methods being used.

Results. The most common cause of lack of eruption was the presence of supernumerary teeth (47% of patients). The other 53% of cases were distributed more or less equally between the remaining aetiological factors, which were odontomes (9%), dilacerations (9%), tooth germ malposition (12%), crowding (4%), one case of a calcifying odontogenic cyst (2%) and one case of trauma to the preceding deciduous tooth (2%). The aetiology of 15% of cases could not be ascertained. Once supernumerary teeth were removed, maxillary incisors usually erupted successfully with the help of conventional treatment methods such as surgical exposure and orthodontics. A relatively large number of incisors that failed to erupt due to other aetiological factors had to be extracted.

Conclusions. Maxillary incisors that fail to erupt due to the presence of supernumerary teeth have a better prognosis than unerupted incisors with less common aetiologies.
Incontinentia Pigmenti: seven cases with dental manifestations
Authors: Macey‐Dare L.V. , Goodman J.R.
Published: May 2004

Incontinentia Pigmenti (Bloch‐Sulzberger syndrome) is an uncommon genodermatosis that usually affects female infants. The condition is characterized by four cutaneous stages and is frequently associated with dental, ocular, central nervous system and structural anomalies. A large case series of seven patients, all female, who presented to the Department of Paediatric Dentistry at the Eastman Dental Hospital over the last 16 years is reported. The dental features of these cases were typical and included missing teeth, microdontia and delayed eruption. In two of the seven cases, both maxillary canines were palatally impacted.
Cortico‐motoneurone excitability in patients with obstructive sleep apnoea
Authors: Civardi Carlo , Naldi Paola , Cantello Roberto
Published: May 2004

A disordered neuromotor control of pharynx muscles may play a role in the genesis of obstructive sleep apnoea syndrome (OSAS). This raises the possibility of a dysfunction of projections descending from the cortex to segmental nuclei. With single pulse transcranial magnetic stimulation (TMS) we studied the physiology of the corticospinal projection to hand muscles in seven OSAS patients. At first, we compared them with nine age‐ and sex‐matched normal controls in the wake state. The only abnormality was a lengthening of the central silent period (P < 0.001). This supports a steady imbalance of motor cortical interneurone activities towards a state of enhanced inhibition. Then we looked at changes of the motor‐evoked potential (MEP) size and latency, according to whether patients were awake, or in a non‐rapid eye movement (REM) 2 sleep stage, or during a typical apnoea. During non‐REM 2 sleep, the average MEP amplitude was significantly (P < 0.05) smaller than in the awake state. The MEP latency was, in turn, significantly longer (P < 0.05). During apnoeas, the MEP size decreased, and the latency increased further (P < 0.05), indicating an extra depression of the cortico‐motoneuronal activity. All TMS changes were detected outside the pharyngeal district, suggesting a widespread dysfunction of the cortico‐motoneuronal system in the OSAS, which is more evident during apnoeas.
The relationship of nightmare frequency and nightmare distress to well‐being
Authors: Blagrove Mark , Farmer Laura , Williams Elvira
Published: May 2004

Nightmares can be defined as very disturbing dreams, the events or emotions of which cause the dreamer to wake up. In contrast, unpleasant dreams can be defined in terms of a negative emotional rating of a dream, irrespective of whether or not the emotions or events of the dream woke the dreamer. This study addresses whether frequency of unpleasant dreams is a better index of low well‐being than is frequency of nightmares. A total of 147 participants reported their nightmare frequency retrospectively and then kept a log of all dreams, including nightmares, for 2 weeks, and rated each dream for pleasantness/unpleasantness. Anxiety, depression, neuroticism, and acute stress were found to be associated with nightmare distress (ND) (the trait‐like general level of distress in waking‐life caused by having nightmares) and prospective frequency of unpleasant dreams, and less so with the mean emotional tone of all dreams, or retrospective or prospective nightmare frequency. Correlations between low well‐being and retrospective nightmare frequency became insignificant when trait ND was controlled for, but correlations with prospective unpleasant dream frequency were maintained. The reporting of nightmares may thus be confounded and modulated by trait ND: such confounding does not occur for the reporting of unpleasant dreams in general. Thus there may be attributional components to deciding that one has been awoken by a dream, which can affect estimated nightmare frequency and its relationship with well‐being. Underestimation of nightmare frequency by the retrospective questionnaire compared with logs was found to be a function of mean dream unpleasantness and ND.
The effects of bright‐light therapy on actigraphical measured sleep last for several weeks post‐treatment. A study in a nursing home population
Authors: Fetveit Arne , Bjorvatn Bjørn
Published: May 2004

We investigated the time‐course of nocturnal actigraphic measures, following the termination of bright‐light therapy for sleep disturbances in demented nursing home patients. From an earlier study, 11 nursing home patients (86 ± 9 years, Mini‐Mental Status Examination score 12 ± 4) with actigraphically measured sleep efficiency <85%, were recruited to morning bright‐light treatment (6000–8000 lux) 2 h per day for 14 days. Actigraphic measures were registered at pretreatment, treatment and at four monthly post‐treatment periods. Each actigraphic recording period consisted of seven consecutive days. Sleep improved substantially with treatment; sleep efficiency increased from 73% to 86% and total nocturnal wake time was reduced by nearly 2 h. During the 16 weeks post‐treatment period, actigraphic measures gradually returned to pretreatment levels. Sleep efficiency remained significantly higher than the pretreatment level 4 weeks after treatment termination. Sleep onset latency remained significantly reduced up until 12 weeks post‐treatment. This study supports previous findings of beneficial effects of bright‐light therapy for sleep disturbances in demented nursing home patients. Furthermore, these results are the first to suggest that post‐treatment effects of short‐term bright‐light therapy may last longer than previously assumed.
Spontaneous eyelid movements (ELMS) during sleep are related to dream recall on awakening
Authors: Conduit Russell , Crewther Sheila Gillard , Coleman Grahame
Published: May 2004

The present study aimed to test whether spontaneous eyelid movements (ELMs) during stage 2 and rapid eye movement (REM) sleep are related to more frequent and vivid reports of visual mentation on awakening. Participants were awakened 15 s after an ELM was observed during ongoing REM and stage 2 sleep and immediately asked for a mentation report and to rate the visual vividness of any imagery they could remember. These reports were compared with control reports collected after a period of ELM quiescence before awakening (noELM). Significantly greater frequencies of imagery reports were collected after ELM awakenings compared with noELM awakenings from stage 2, but not REM sleep. When imagery was reported, imagery ratings were not significantly different between ELM and noELM conditions, regardless of sleep stage. The average amount of electroencephalogram (EEG) arousal 15 s after stage 2 awakenings was significantly higher in the ELM compared with noELM conditions. In addition, within the stage 2 ELM condition, EEG arousal was significantly higher when visual imagery was reported compared with reports without imagery; suggesting that the observed increase in imagery reporting from the stage 2 ELM condition could have been mediated by the level of brain arousal. Such arousal possibly provides better conditions to attend and recall previous mental activity from NREM sleep. However, there was no ELM/arousal effect within REM sleep, possibly because this state is already at maximum sleeping levels of arousal, attention and resulting dream recall.