bluuebunny/crossref_metadata_2025 huggingface dataset:
Loss of Geminin induces rereplication in the presence of functional p53
Authors: Melixetian Marina , Ballabeni Andrea , Masiero Laura , Gasparini Patrizia , Zamponi Raffaella , Bartek Jiri , Lukas Jiri , Helin Kristian
Published: May 2004

Strict regulation of DNA replication is essential to ensure proper duplication and segregation of chromosomes during the cell cycle, as its deregulation can lead to genomic instability and cancer. Thus, eukaryotic organisms have evolved multiple mechanisms to restrict DNA replication to once per cell cycle. Here, we show that inactivation of Geminin, an inhibitor of origin licensing, leads to rereplication in human normal and tumor cells within the same cell cycle. We found a CHK1-dependent checkpoint to be activated in rereplicating cells accompanied by formation of γH2AX and RAD51 nuclear foci. Abrogation of the checkpoint leads to abortive mitosis and death of rereplicated cells. In addition, we demonstrate that the induction of rereplication is dependent on the replication initiation factors CDT1 and CDC6, and independent of the functional status of p53. These data show that Geminin is required for maintaining genomic stability in human cells.
A direct simulation method for particle‐fluid systems
Authors: Cook Benjamin K. , Noble David R. , Williams John R.
Published: May 2004

A coupled numerical method for the direct simulation of particle‐fluid systems is formulated and implemented. The Navier‐Stokes equations governing fluid flow are solved using the lattice Boltzmann method, while the equations of motion governing particles are solved with the discrete element method. Particle‐fluid coupling is realized through an immersed moving boundary condition. Particle forcing mechanisms represented in the model to at least the first‐order include static and dynamic fluid‐induced forces, and intergranular forces including particle collisions, static contacts, and cementation. The coupling scheme is validated through a comparison of simulation results with the analytical solution of cylindrical Couette flow. Simulation results for the fluid‐induced erosive failure of a cemented particulate constriction are presented to demonstrate the capability of the method.
Selecting a suitable time step for discrete element simulations that use the central difference time integration scheme
Authors: O'Sullivan Catherine , Bray Jonathan D.
Published: May 2004

The distinct element method as proposed by Cundall and Strack uses the computationally efficient, explicit, central difference time integration scheme. A limitation of this scheme is that it is only conditionally stable, so small time steps must be used. Some researchers have proposed using an implicit time integration scheme to avoid the stability issues arising from the explicit time integrator typically used in these simulations. However, these schemes are computationally expensive and can require a significant number of iterations to form the stiffness matrix that is compatible with the contact state at the end of each time step. In this paper, a new, simple approach for calculating the critical time increment in explicit discrete element simulations is proposed. Using this approach, it is shown that the critical time increment is a function of the current contact conditions. Considering both two‐ and three‐dimensional scenarios, the proposed refined estimates of the critical time step indicate that the earlier recommendations contained in the literature can be unconservative, in that they often overestimate the actual critical time step. A three‐dimensional simulation of a problem with a known analytical solution illustrates the potential for erroneous results to be obtained from discrete element simulations, if the time‐increment exceeds the critical time step for stable analysis.
The corporate governance of professional football clubs
Authors: Hamil Sean , Holt Matthew , Michie Jonathan , Oughton Christine , Shailer Lee
Published: May 2004

Professional football clubs in England face serious financial and operational difficulties and challenges. Our survey reveals that less than a quarter of football clubs responding had an internal audit committee. Even where clubs had an audit committee, almost one third of those clubs report there being no regular board review of risk assessment reports. The need to undertake risk assessment is now accepted as part of good corporate governance. The collapse of the ITV Digital agreement, which led to Football League clubs losing significant revenue, forcing some into administration, simply illustrates the reasoning behind the practice (following the Turnbull Report). Football clubs (and the companies that own them) need improved corporate governance practice, financial planning and risk assessment procedures; 76 percent of clubs responded that they would benefit from a guide to good corporate governance and 80 percent that they would find advice on Company Law useful.
Flexible Parametric Measurement Error Models
Authors: Carroll Raymond J. , Roeder Kathryn , Wasserman Larry
Published: May 2004

Summary.Inferences in measurement error models can be sensitive to modeling assumptions. Specifically, if the model is incorrect, the estimates can be inconsistent. To reduce sensitivity to modeling assumptions and yet still retain the efficiency of parametric inference, we propose using flexible parametric models that can accommodate departures from standard parametric models. We use mixtures of normals for this purpose. We study two cases in detail: a linear errors‐in‐variables model and a change‐point Berkson model.
Hypothesis Testing Under Mixture Models: Application to Genetic Linkage Analysis
Authors: Liang Kung‐Yee , Rathouz Paul J.
Published: May 2004

Summary. In this paper we propose a new class of statistics to test a simple hypothesis against a family of alternatives characterized by a mixture model. Unlike the likelihood ratio statistic, whose large sample distribution is still unknown in this situation, these new statistics have a simple asymptotic distribution to which to refer under the null hypothesis. Simulation results suggest that it has adequate power in detecting the alternatives. Its application to genetic linkage analysis in the presence of the genetic heterogeneity that motivated this work is emphasized.
A General Maximum Likelihood Analysis of Variance Components in Generalized Linear Models
Authors: Aitkin Murray
Published: May 2004

Summary.This paper describes an EM algorithm for nonparametric maximum likelihood (ML) estimation in generalized linear models with variance component structure. The algorithm provides an alternative analysis to approximate MQL and PQL analyses (McGilchrist and Aisbett, 1991,Biometrical Journal33, 131–141; Breslow and Clayton, 1993;Journal of the American Statistical Association88, 9–25; McGilchrist, 1994,Journal of the Royal Statistical Society, Series B56, 61–69; Goldstein, 1995,Multilevel Statistical Models) and to GEE analyses (Liang and Zeger, 1986,Biometrika73, 13–22). The algorithm, first given by Hinde and Wood (1987, inLongitudinal Data Analysis, 110–126), is a generalization of that for random effect models for overdispersion in generalized linear models, described in Aitkin (1996,Statistics and Computing6, 251–262). The algorithm is initially derived as a form of Gaussian quadrature assuming a normal mixing distribution, but with only slight variation it can be used for a completely unknown mixing distribution, giving a straightforward method for the fully nonparametric ML estimation of this distribution. This is of value because the ML estimates of the GLM parameters can be sensitive to the specification of a parametric form for the mixing distribution. The nonparametric analysis can be extended straightforwardly to general random parameter models, with full NPML estimation of the joint distribution of the random parameters. This can produce substantial computational saving compared with full numerical integration over a specified parametric distribution for the random parameters. A simple method is described for obtaining correct standard errors for parameter estimates when using the EM algorithm. Several examples are discussed involving simple variance component and longitudinal models, and small‐area estimation.
Maximum Likelihood Analysis for Heteroscedastic One‐Way Random Effects ANOVA in Interlaboratory Studies
Authors: Vangel Mark G. , Rukhin Andrew L.
Published: May 2004

Summary.This article presents results for the maximum likelihood analysis of several groups of measurements made on the same quantity. Following Cochran (1937,Journal of the Royal Statistical Society4(Supple), 102–118; 1954,Biometrics10, 101–129; 1980, inProceedings of the 25th Conference on the Design of Experiments in Army Research, Development and Testing, 21–33) and others, this problem is formulated as a one‐way unbalanced random‐effects ANOVA with unequal within‐group variances. A reparametrization of the likelihood leads to simplified computations, easier identification and interpretation of multimodality of the likelihood, and (through a non‐informative‐prior Bayesian approach) approximate confidence regions for the mean and between‐group variance.
A Partial Ranking Method for Identifying Repeated Inclusion of Individuals in Anonymized HIV Infection Reports
Authors: Greenhalgh David , Doyle Murray T. , Mortimer Janet
Published: May 2004

Summary. Diagnoses of HIV infection are reported to the Public Health Laboratory Service (PHLS) AIDS Centre under a voluntary surveillance scheme. Names are not held in the data set, but the date of birth of the individual concerned is usually available. This paper describes a statistical method for identifying whether there are likely to be individuals repeatedly represented in the resulting data set, which is considered by birth year. A partial ordering method is used that is especially useful for years where the number of birth years in the sample is too small for x2 tests to be used. At the 5% level, one of the five birth years tested in the data supplied to us by the PHLS shows evidence of more replication than would be expected from independent random sampling from the population. The results are compared with an alternative maximum‐likelihood‐based test that reaches the same conclusions. Maximum likelihood methods are further used to estimate the percentage of overcounting of individuals in the sample at 2.7%.
Adaptive Regression Splines in the Cox Model
Authors: LeBlanc Michael , Crowley John
Published: May 2004

Summary.We develop a method for constructing adaptive regression spline models for the exploration of survival data. The method combines Cox's (1972,Journal of the Royal Statistical Society, Series B34, 187–200) regression model with a weighted least‐squares version of the multivariate adaptive regression spline (MARS) technique of Friedman (1991,Annals of Statistics19, 1–141) to adaptively select the knots and covariates. The new technique can automatically fit models with terms that represent nonlinear effects and interactions among covariates. Applications based on simulated data and data from a clinical trial for myeloma are presented. Results from the myeloma application identified several important prognostic variables, including a possible nonmonotone relationship with survival in one laboratory variable. Results are compared to those from the adaptive hazard regression (HARE) method of Kooperberg, Stone, and Truong (1995,Journal of the American Statistical Association90, 78–94).
Estimating the Size of a Transient Population
Authors: Routledge R. D. , Smith G. E. J. , Sun L. , Dawe N. , Nygren E. , Sedinger J. S.
Published: May 2004

Summary. Migratory populations often stop over for short periods of time at predictable sites along their migration routes. These staging areas can be heavily used and are potentially critical to the survival of the migrants. This paper presents a method for estimating the number of individuals using such an area and their average residence time. The estimator is based on daily population estimates and records of repeat sightings of identifiable individuals. Its application is illustrated with observations on a population of migrating birds, some of which could be identified from bands that were readable from a distance.
Local EM Estimation of the Hazard Function for Interval‐Censored Data
Authors: Betensky Rebecca A. , Lindsey Jane C. , Ryan Louise M. , Wand M. P.
Published: May 2004

Summary.We propose a smooth hazard estimator for interval‐censored survival data using the method of local likelihood. The model is fit using a local EM algorithm. The estimator is more descriptive than traditional empirical estimates in regions of concentrated information and takes on a parametric flavor in regions of sparse information. We derive two different standard error estimates for the smooth curve, one based on asymptotic theory and the other on the bootstrap. We illustrate the local EM method for times to breast cosmesis deterioration (Finkelstein, 1986,Biometrics42, 845–854) and for times to HIV‐1 infection for individuals with hemophilia (Kroner et al., 1994,Journal of AIDS7, 279–286). Our hazard estimates for each of these data sets show interesting structures that would not be found using a standard parametric hazard model or empirical survivorship estimates.
Poisson Regression with Missing Durations of Exposure
Authors: Ryan Louise M. , Lin D. Y. , Arbogast P. , Siscovick D. S. , Lemaitre R. N.
Published: May 2004

Summary. In this paper, we develop Poisson‐type regression methods that require the durations of exposure be measured only on a possibly nonrandom subset of the cohort members. These methods can be used to make inferences about the incidence density during exposure as well as the ratio of incidence densities during exposure versus not during exposure. Numerical studies demonstrate that the proposed methods yield reliable results in practical settings. We describe an application to a population‐based case‐control study assessing the transient increase in the risk of primary cardiac arrest during leisure‐time physical activity.
Maximally Selected x2 Statistics for k× 2 Tables
Authors: Betensky Rebecca A. , Rabinowitz Daniel
Published: May 2004

Summary. It is common in epidemiologic analyses to summarize continuous outcomes as falling above or below a threshold. With such a dichotomized outcome, the usual x2 statistics for association or trend can be used to test for equality of proportions across strata of the study population. However, if the threshold is chosen to maximize the test statistic, the nominal x2 reference distributions are incorrect. In this paper, the asymptotic distributions of maximally selected x2 statistics for association and for trend for the k× 2 table are derived. The methodology is illustrated with data from an AIDS clinical trial. The results of simulation experiments that assess the accuracy of the asymptotic distributions in moderate sample sizes are also reported.
Optimal Diversity‐Dependent Contributions of Genotypes to Mixtures
Authors: Wei Run‐Peng , Yeh Francis C.
Published: May 2004

Summary. Deployment of genotypes to a production population decisively depends on the measure of diversity, a consideration that parallels genetic gain in the management of genotype mixtures. Optimal diversity‐dependent deployment has been developed in this study for a family of diversity indices in the genetical and ecological context. The optimal solution at given diversity was expressed as the relationship between genotype contributions and their genetic performances, which maximized genetic gain. Numerical calculations were performed by using genotypes generated from normal order statistics. An optimal deployment in one situation could be nonoptimum in another. Classical uniform deployment, where superior genotypes equally contribute to the mixtures, was the limit of optimal deployment. Comparisons were made between optimal and uniform deployment and between optimal and nonoptimal deployment where geno‐types contributed proportionally to the mixtures in accordance to their genetic superiority. The superiority in gain of optimal deployment over that of uniform deployment increased as the difference between the diversity measure under optimal deployment and the contributing number (N) of genotypes under uniform deployment became large and as the diversity measure and N under optimal deployment increased.The superiority over nonoptimal deployment increased rapidly at low diversity, reaching a maximum somewhere at diversity between 1 and N. Scale of superiority depended on the similitude between optimum and nonoptimum deployment; the larger the distinctiveness, the greater the superiority.
A Multiplicative‐Epistatic Model for Analyzing Interspecific Differences in Outcrossing Species
Authors: Rongling Wu , Li Bailian
Published: May 2004

Summary.Epistasis may play an important role in evolution and speciation. Under multiplicative interactions between different loci, an analytical model is proposed to estimate genetic parameters at the individual locus level that contribute to interspecific differences in outcrossing species. The multiplicative epistasis model, inferred from a number of animal and plant experiments, suggests that genotypes at a pair of loci have genotypic values equal to the product of genotypic values at the two different loci. By considering the genetic property of outcrossing species (i.e., high polymorphisms) in the multilevel family structure analysis for an intra‐ and interspecific factorial mating design, a method is developed to provide estimates for allele frequencies and additive and dominant effects at individual loci in each of the two parental populations, the genotypic values of newly formed heterozygotes through species combination each with one allele from a parental population and the second from the other parental population, and the numbers of genetic factors that lead to species differentiation. Use of clones offers a tremendous power to test the adequacy of the model. However, the utilization of the model with species that cannot be cloned is also discussed. An example with interspecific hybrids of two forest tree species is used to demonstrate the model.
Estimation of the Size of an Open Population from Capture‐Recapture Data Using Weighted Martingale Methods
Authors: Huggins Richard M. , Yip Paul S. F.
Published: May 2004

Summary. A weighted martingale method, akin to a moving average, is proposed to allow the use of modified closed‐population methods in the estimation of the size of a smoothly changing open population when there are frequent capture occasions. We concentrate here on modifications to martingale estimating functions for model Mt, but a wide range of closed‐population estimators may be modified in this fashion. The method is motivated by and applied to weekly capture‐recapture data from the Mai Po bird sanctuary in Hong Kong. Simulations show that the weighted martingale estimator compared well with the Jolly‐Seber estimator when the conditions for the latter to be valid are met, and it performed far better when individuals were allowed to leave and reenter the population. Expressions are derived for the asymptotic bias and variance of the estimator in an appendix.
Population HIV‐1 Dynamics In Vivo: Applicable Models and Inferential Tools for Virological Data from AIDS Clinical Trials
Authors: Wu Hulin , Ding A. Adam
Published: May 2004

Summary. In this paper, we introduce a novel application of hierarchical nonlinear mixed‐effect models to HIV dynamics. We show that a simple model with a sum of exponentials can give a good fit to the observed clinical data of HIV‐1 dynamics (HIV‐1 RNA copies) after initiation of potent antiviral treatments and can also be justified by a biological compartment model for the interaction between HIV and its host cells. This kind of model enjoys both biological interpretability and mathematical simplicity after reparameterization and simplification. A model simplification procedure is proposed and illustrated through examples. We interpret and justify various simplified models based on clinical data taken during different phases of viral dynamics during antiviral treatments. We suggest the hierarchical nonlinear mixed‐effect model approach for parameter estimation and other statistical inferences. In the context of an AIDS clinical trial involving patients treated with a combination of potent antiviral agents, we show how the models may be used to draw biologically relevant interpretations from repeated HIV‐1 RNA measurements and demonstrate the potential use of the models in clinical decision‐making.
Bayesian Design and Analysis of Active Control Clinical Trials
Authors: Simon Richard
Published: May 2004

Summary. We consider the design and analysis of active control clinical trials, i.e., clinical trials comparing an experimental treatment E to a control treatment C considered to be effective. Direct comparison of E to placebo P, or no treatment, is sometimes ethically unacceptable. Much discussion of the design and analysis of such clinical trials has focused on whether the comparison of E to C should be based on a test of the null hypothesis of equivalence, on a test of a nonnull hypothesis that the difference is of some minimally medically important size δ, or on one or two‐sided confidence intervals. These approaches are essentially the same for study planning. They all suffer from arbitrariness in specifying the size of the difference δ that must be excluded. We propose an alternative Bayesian approach to the design and analysis of active control trials. We derive the posterior probability that E is superior to P or that E is at least K% as good as C and that C is more effective than P. We also derive approximations for use with logistic and proportional hazard models. Selection of prior distributions is discussed, and results are illustrated using data from an active control trial of a drug for the treatment of unstable angina.
Fitting Semi‐Markov Models to Interval‐Censored Data with Unknown Initiation Times
Authors: Satten Glen A. , Sternberg Maya R.
Published: May 2004

Summary.In a semi‐Markov model, the hazard of making a transition between stages depends on the time spent in the current stage but is independent of time spent in other stages. If the initiation time (time of entry into the network) is not known for some persons and if transition time data are interval censored (i.e., if transition times are not known exactly but are known only to have occurred in some interval), then the length of time these persons spent in any stage is not known. We show how a semi‐Markov model can still be fit to interval‐censored data with missing initiation times. For the special case of models in which all persons enter the network at the same initial stage and proceed through the same succession of stages to a unique absorbing stage, we present discrete‐time nonparametric maximum likelihood estimators of the waiting‐time distributions for this type of data.
Discrete‐Time Nonparametric Estimation for Semi‐Markov Models of Chain‐of‐Events Data Subject to Interval Censoring and Truncation
Authors: Sternberg Maya R. , Satten Glen A.
Published: May 2004

Summary.Chain‐of‐events data are longitudinal observations on a succession of events that can only occur in a prescribed order. One goal in an analysis of this type of data is to determine the distribution of times between the successive events. This is difficult when individuals are observed periodically rather than continuously because the event times are then interval censored. Chain‐of‐events data may also be subject to truncation when individuals can only be observed if a certain event in the chain (e.g., the final event) has occurred. We provide a nonparametric approach to estimate the distributions of times between successive events in discrete time for data such as these under the semi‐Markov assumption that the times between events are independent. This method uses a self‐consistency algorithm that extends Turnbull's algorithm (1976,Journal of the Royal Statistical Society, Series B38, 290–295). The quantities required to carry out the algorithm can be calculated recursively for improved computational efficiency. Two examples using data from studies involving HIV disease are used to illustrate our methods.
New Residuals for Cox Regression and Their Application to Outlier Screening
Authors: Nardi Alessandra , Schemper Michael
Published: May 2004

Summary.The identification of individuals who ‘died far too early’ or ‘lived far too long’ as compared to their survival probabilities from a Cox regression can lead to the detection of new prognostic factors. Methods to identify outliers are generally based on residuals. For Cox regression, only deviance residuals have been considered for this purpose, but we show that these residuals are not very suitable. Instead, we develop and propose two new types of residuals: the suggested log‐odds and normal deviate residuals are simple and intuitively appealing and their theoretical properties and empirical performance make them very suitable for outlier identification. Finally, various practical aspects of screening for individuals with outlying survival times are discussed by means of a cancer study example.
Locally Efficient Estimation of the Quality‐Adjusted Lifetime Distribution with Right‐Censored Data and Covariates
Authors: van der Laan Mark J. , Hubbard Alan
Published: May 2004

Summary. Zhao and Tsiatis (1997) consider the problem of estimation of the distribution of the quality adjusted lifetime when the chronological survival time is subject to right censoring. The quality‐adjusted lifetime is typically defined as a weighted sum of the times spent in certain states up until death or some other failure time. They propose an estimator and establish the relevant asymptotics under the assumption of independent censoring. In this paper we extend the data structure with a covariate process observed until the end of follow‐up and identify the optimal estimation problem. Because of the curse of dimensionality, no globally efficient nonparametric estimators, which have a good practical performance at moderate sample sizes, exist. Given a correctly specified model for the hazard of censoring conditional on the observed quality‐of‐life and covariate processes, we propose a closed‐form one‐step estimator of the distribution of the quality‐adjusted lifetime whose asymptotic variance attains the efficiency bound if we can correctly specify a lower‐dimensional working model for the conditional distribution of quality‐adjusted lifetime given the observed quality‐of‐life and covariate processes. The estimator remains consistent and asymptotically normal even if this latter submodel is misspecified. The practical performance of the estimators is illustrated with a simulation study. We also extend our proposed one‐step estimator to the case where treatment assignment is confounded by observed risk factors so that this estimator can be used to test a treatment effect in an observational study.
Modeling Heterogeneity in Nest Survival Data
Authors: Natarajan Ranjini , McCulloch Charles E.
Published: May 2004

Summary. Current statistical methods for estimating nest survival rates assume that nests are identical in their propensity to succeed. However, there are several biological reasons to question this assumption. For example, experience of the nest builder, number of nest helpers, genetic fitness of individuals, and site effects may contribute to an inherent disparity between nests with respect to their daily mortality rates. Ignoring such heterogeneity can lead to incorrect survival estimates. Our results show that constant survival models can seriously underestimate overall survival in the presence of heterogeneity. This paper presents a flexible random‐effects approach to model heterogeneous nest survival data. We illustrate our methods through data on redwing blackbirds.
From Quantal Counts to Mechanisms and Systems: The Past, Present, and Future of Biometrics in Environmental Toxicology
Authors: Bailer A. John , Piegorsch Walter W.
Published: May 2004

Summary.As appreciation for human impact on the environment has developed, so have the experimental systems and associated statistical tools that quantify this impact. Toxicological study in particular has grown in its complexity and its need for advanced statistical support. Within this perspective, we describe statistical practice in environmental toxicology and risk assessment. We present two case studies, one from mammalian toxicology and one from aquatic toxicology, that highlight the evolution of statistical practice in environmental toxicology.
Comparisons of Predictive Values of Binary Medical Diagnostic Tests for Paired Designs
Authors: Leisenring Wendy , Alono Todd , Pepe Margaret Sullivan
Published: May 2004

Summary. Positive and negative predictive values of a diagnostic test are key clinically relevant measures of test accuracy. Surprisingly, statistical methods for comparing tests with regard to these parameters have not been available for the most common study design in which each test is applied to each study individual. In this paper, we propose a statistic for comparing the predictive values of two diagnostic tests using this paired study design. The proposed statistic is a score statistic derived from a marginal regression model and bears some relation to McNemar's statistic. As McNemar's statistic can be used to compare sensitivities and specificities of diagnostic tests, parameters that condition on disease status, our statistic can be considered as an analog of McNemar's test for the problem of comparing predictive values, parameters that condition on test outcome. We report on the results of a simulation study designed to examine the properties of this test under a variety of conditions. The method is illustrated with data from a study of methods for diagnosis of coronary artery disease.
An Interpretation for the ROC Curve and Inference Using GLM Procedures
Authors: Pepe Margaret Sullivan
Published: May 2004

Summary. The accuracy of a medical diagnostic test is often summarized in a receiver operating characteristic (ROC) curve. This paper puts forth an interpretation for each point on the ROC curve as being a conditional probability of a test result from a random diseased subject exceeding that from a random non diseased subject. This interpretation gives rise to new methods for making inference about ROC curves. It is shown that inference can be achieved with binary regression techniques applied to indicator variables constructed from pairs of test results, one component of the pair being from a diseased subject and the other from a non diseased subject. Within the generalized linear model (GLM) binary regression framework, ROC curves can be estimated, and we highlight a new semiparametric estimator. Covariate effects can also be evaluated with the GLM models. The methodology is applied to a pancreatic cancer dataset where we use the regression framework to compare two different serum biomarkers. Asymptotic distribution theory is developed to facilitate inference and to provide insight into factors influencing variability of estimated model parameters.
Hierarchical Bayes Estimation of Hunting Success Rates with Spatial Correlations
Authors: He Zhuoqiong , Sun Dongchu
Published: May 2004

Summary.A Bayesian hierarchical generalized linear model is used to estimate hunting success rates at the subarea level for postseason harvest surveys. The model includes fixed week effects, random geographic effects, and spatial correlations between neighboring subareas. The computation is done by Gibbs sampling and adaptive rejection sampling techniques. The method is illustrated using data from the Missouri Turkey Hunting Survey in the spring of 1996. Bayesian model selection methods are used to demonstrate that there are significant week differences and spatial correlations of hunting success rates among counties. The Bayesian estimates are also shown to be quite robust in terms of changes of hyperparameters.
Correcting for Measurement Error in Individual‐Level Covariates in Nonlinear Mixed Effects Models
Authors: KO Hyejin , Davidian Marie
Published: May 2004

Summary. The nonlinear mixed effects model is used to represent data in pharmacokinetics, viral dynamics, and other areas where an objective is to elucidate associations among individual‐specific model parameters and covariates; however, covariates may be measured with error. For additive measurement error, we show substitution of mismeasured covariates for true covariates may lead to biased estimators for fixed effects and random effects covariance parameters, while regression calibration may eliminate bias in fixed effects but fail to correct that in covariance parameters. We develop methods to take account of measurement error that correct this bias and may be implemented with standard software, and we demonstrate their utility via simulation and application to data from a study of HIV dynamics.
Conditional and Unconditional Categorical Regression Models with Missing Covariates
Authors: Satten Glen A. , Carroll Raymond J.
Published: May 2004

Summary. We consider methods for analyzing categorical regression models when some covariates (Z) are completely observed but other covariates (X) are missing for some subjects. When data on X are missing at random (i.e., when the probability that X is observed does not depend on the value of X itself), we present a likelihood approach for the observed data that allows the same nuisance parameters to be eliminated in a conditional analysis as when data are complete. An example of a matched casecontrol study is used to demonstrate our approach.
Optimal Complete Block Designs to Adjust for Interplot Competition with a Covariance Analysis
Authors: David Olivier , Monod Hervé , Amoussou Joël
Published: May 2004

Summary. Optimal complete block designs are given for variety trials with interplot competition. In the paper, competition is modeled using height differences between neighboring varieties. When variety heights are known at the design stage, a simulated annealing algorithm is proposed. The optimal designs tend to reduce height differences between neighboring varieties. When variety heights are not known in advance, neighbor‐balanced designs at distances 1 and 2 are shown to have optimality properties.
Estimating the Mahalanobis Distance from Mixed Continuous and Discrete Data
Authors: Bedrick Edward J. , Lapidus Jodi , Powell Joseph F.
Published: May 2004

Summary. We present a method for estimating the Mahalanobis distance between two multivariate normal populations when a subset of the measurements is observed as ordered categorical responses. Asymptotic properties of the proposed estimator are developed. Two examples are discussed.
A Chi‐square Goodness‐of‐Fit Analysis of Dependent Resource election Data
Authors: Dasgupta Nairanjana , Alldredge J. Richard
Published: May 2004

Summary. The χ2 goodness‐of‐fit test is commonly used for testing if animals use resources in proportion to availability. This method assumes independence of resource selection among animals. In reality, this assumption is violated if animals display antisocial or gregarious behavior. Data from a study of sharp tailed grouse in eastern Washington suggested some dependency among observations. Realizing that this dependency can have a great influence on inference for resource selection data, we develop a technique to incorporate information on dependent observations through a simple adjustment of the usual goodness‐of‐fit statistic. We also demonstrate how confidence intervals on proportional use may be modified for dependent observations. Simulation is used to compare our method to other methods.
Estimation of Exploitation Rate by Combining Mark–Recapture Procedures with a Creel Survey
Authors: Dahiya Ram C. , Saha Shampa , Lakkis Hassan
Published: May 2004

Summary. Government agencies are usually interested in estimating the annual exploitation rate of a fish population for recreational and commercial fisheries. In this paper, we propose a model‐based approach for the estimation of the exploitation rate. Our approach combines mark‐recapture procedures with a creel survey (which is a type of angler survey). We consider moment estimation of the exploitation rate. We also consider estimation of the exploitation rate by stratifying the space‐time units of the fisheries.
Prediction from an Integrated Regression Equation: A Forestry Application
Authors: Gregoire Timothy G. , Schabenberger Oliver , Kong Fanzhi
Published: May 2004

Summary. Models to depict the tapering of a tree bole abound in the literature of forest science, and such models are widely used in forestry practice. One important use is the integration of a taper equation to predict the volume of the tree bole. The statistical properties of volume prediction from an integrated taper equation have been obscure. Based on the statistical characteristics of a taper model for the bole's cross‐sectional area, we derive the first two moments of the volume predictor and the prediction error. Bias from the integration is nil. The importance of a reasonable model of the error structure is demonstrated.
Capture–Recapture When Time and Behavioral Response Affect Capture Probabilities
Authors: Chao Anne , Chu Wenten , Hsu Chiu‐Hsieh
Published: May 2004

Summary. We consider a capture–recapture model in which capture probabilities vary with time and with behavioral response. Two inference procedures are developed under the assumption that recapture probabilities bear a constant relationship to initial capture probabilities. These two procedures are the maximum likelihood method (both unconditional and conditional types are discussed) and an approach based on optimal estimating functions. The population size estimators derived from the two procedures are shown to be asymptotically equivalent when population size is large enough. The performance and relative merits of various population size estimators for finite cases are discussed. The bootstrap method is suggested for constructing a variance estimator and confidence interval. An example of the deer mouse analyzed in Otis et al. (1978, Wildlife Monographs62, 93) is given for illustration.
Unified Maximum Likelihood Estimates for Closed Capture–Recapture Models Using Mixtures
Authors: Pledger Shirley
Published: May 2004

Summary.Agresti (1994,Biometrics50, 494–500) and Norris and Pollock (1996a,Biometrics52, 639–649) suggested using methods of finite mixtures to partition the animals in a closed capture‐recapture experiment into two or more groups with relatively homogeneous capture probabilities. This enabled them to fit the models Mh, Mbh(Norris and Pollock), and Mth(Agresti) of Otis et al. (1978,Wildlife Monographs62, 1–135). In this article, finite mixture partitions of animals and/or samples are used to give a unified linear‐logistic framework for fitting all eight models of Otis et al. by maximum likelihood. Likelihood ratio tests are available for model comparisons. For many data sets, a simple dichotomy of animals is enough to substantially correct for heterogeneity‐induced bias in the estimation of population size, although there is the option of fitting more than two groups if the data warrant it.
The Analysis of Group Truncated Binary Data with Random Effects: Injury Severity in Motor Vehicle Accidents
Authors: Barry S. C. , O'Neill T. J.
Published: May 2004

Summary. The analysis of group truncated binary data has been previously considered by O'Neill and Barry (1995b, Biometrics51, 533–541), where the analysis assumed that responses within each group were independent. In this paper, we consider the analysis of such data when there is group‐level heterogeneity. A generalized linear mixed model is hypothesized to model the response and maximum likelihood estimates are derived for the truncated case. A score test is derived to test for heterogeneity. Finally, the method is applied to a set of traffic accident data.
Increased Power with Modified Forms of the Levene (Med) Test for Heterogeneity of Variance
Authors: Hines W. G. S. , Hines R. J. O'Hara
Published: May 2004

Summary. While the conventional Levene (Med) test is a widely used and robust test for detecting heterogeneity of variance, it does not take notice of either the linear dependencies among the residuals involved or the possibility of a mean (or median)‐variance relationship. This paper explores the substantial improvements in power possible by investigating the benefits both of removing such linear dependencies (structural zeros) and of modeling (even roughly) such relationships.
Trim and Fill: A Simple Funnel‐Plot–Based Method of Testing and Adjusting for Publication Bias in Meta‐Analysis
Authors: Duval Sue , Tweedie Richard
Published: May 2004

Summary. We study recently developed nonparametric methods for estimating the number of missing studies that might exist in a meta‐analysis and the effect that these studies might have had on its outcome. These are simple rank‐based data augmentation techniques, which formalize the use of funnel plots. We show that they provide effective and relatively powerful tests for evaluating the existence of such publication bias. After adjusting for missing studies, we find that the point estimate of the overall effect size is approximately correct and coverage of the effect size confidence intervals is substantially improved, in many cases recovering the nominal confidence levels entirely. We illustrate the trim and fill method on existing meta‐analyses of studies in clinical trials and psychometrics.
A Mixture Model for Longitudinal Data with Application to Assessment of Noncompliance
Authors: Pauler Donna K. , Laird Nan M.
Published: May 2004

Summary. In clinical trials of a self‐administered drug, repeated measures of a laboratory marker, which is affected by study medication and collected in all treatment arms, can provide valuable information on population and individual summaries of compliance. In this paper, we introduce a general finite mixture of nonlinear hierarchical models that allows estimates of component membership probabilities and random effect distributions for longitudinal data arising from multiple subpopulations, such as from noncomplying and complying subgroups in clinical trials. We outline a sampling strategy for fitting these models, which consists of a sequence of Gibbs, Metropolis‐Hastings, and reversible jump steps, where the latter is required for switching between component models of different dimensions. Our model is applied to identify noncomplying subjects in the placebo arm of a clinical trial assessing the effectiveness of zidovudine (AZT) in the treatment of patients with HIV, where noncompliance was defined as initiation of AZT during the trial without the investigators' knowledge. We fit a hierarchical nonlinear change‐point model for increases in the marker MCV (mean corpuscular volume of erythrocytes) for subjects who noncomply and a constant mean random effects model for those who comply. As part of our fully Bayesian analysis, we assess the sensitivity of conclusions to prior and modeling assumptions and demonstrate how external information and covariates can be incorporated to distinguish subgroups.
Residuals for Proportional Hazards Models with Interval‐Censored Survival Data
Authors: Farrington C. P.
Published: May 2004

Summary.We develop diagnostic tools for use with proportional hazards models for interval‐censored survival data. We propose counterparts t o the Cox‐Snell, Lagakos (or martingale), deviance, and Schoenfeld residuals. Many of the properties of these residuals carry over to the interval‐censored case. In particular, the interval‐censored versions of the Lagakos and Schoenfeld residuals may be derived as components of suitable score statistics. The Lagakos residuals may be used to check regression relationships, while the Schoenfeld residuals can help to detect nonproportional hazards in semiparametric models. The methods apply to parametric models and to the semiparametric model with discrete observation times.
Marginal Models for Longitudinal Continuous Proportional Data
Authors: Song Peter Xue‐Kun , Tan Ming
Published: May 2004

Summary.Continuous proportional data arise when the response of interest is a percentage between zero and one, e.g., the percentage of decrease in renal function at different follow‐up times from the baseline. In this paper, we propose methods to directly model the marginal means of the longitudinal proportional responses using the simplex distribution of Barndorff‐Nielsen and Jørgensen that takes into account the fact that such responses are percentages restricted between zero and one and may as well have large dispersion. Parameters in such a marginal model are estimated using an extended version of the generalized estimating equations where the score vector is a nonlinear function of the observed response. The method is illustrated with an ophthalmology study on the use of intraocular gas in retinal repair surgeries.
Bootstrap Confidence Intervals for Adaptive Cluster Sampling
Authors: Christman Mary C. , Pontius Jeffrey S.
Published: May 2004

Summary.Consider a collection of spatially clustered objects where the clusters are geographically rare. Of interest is estimation of the total number of objects on the site from a sample of plots of equal size. Under these spatial conditions, adaptive cluster sampling of plots is generally useful in improving efficiency in estimation over simple random sampling without replacement (SRSWOR). In adaptive cluster sampling, when a sampled plot meets some predefined condition, neighboring plots are added to the sample. When populations are rare and clustered, the usual unbiased estimators based on small samples are often highly skewed and discrete in distribution. Thus, confidence intervals based on asymptotic normal theory may not be appropriate. We investigated several nonparametric bootstrap methods for constructing confidence intervals under adaptive cluster sampling. To perform bootstrapping, we transformed the initial sample in order to include the information from the adaptive portion of the sample yet maintain a fixed sample size. In general, coverages of bootstrap percentile methods were closer to nominal coverage than the normal approximation.
Using Conditional Logistic Regression to Fit Proportional Odds Models to Interval Censored Data
Authors: Rabinowitz Daniel , Betensky Rebecca A. , Tsiatis Anastasios A.
Published: May 2004

Summary.An easily implemented approach to fitting the proportional odds regression model to interval‐censored data is presented. The approach is based on using conditional logistic regression routines in standard statistical packages. Using conditional logistic regression allows the practitioner to sidestep complications that attend estimation of the baseline odds ratio function. The approach is applicable both for interval‐censored data in settings in which examinations continue regardless of whether the event of interest has occurred and for current status data. The methodology is illustrated through an application to data from an AIDS study of the effect of treatment with ZDV + ddC versus ZDV alone on 50% drop in CD4 cell count from baseline level. Simulations are presented to assess the accuracy of the procedure.