bluuebunny/crossref_metadata_2025 huggingface dataset:
The Plausibility of Defensive Projection: A Cognitive Analysis
Authors: Miceli Maria , Castelfranchi Cristiano
Published: August 2003

This paper provides an analysis of the cognitive processes implied in the ego defense known as projection. Projection is first placed in the context of the general cognitive processes of attribution and ascription. Then we address defensive projection, and identify its distinctive features. In particular, whereas general projection consists in the ascription of one's own mental attitudes to others, defensive projection implies one's rejection (via denial or repression) of the ascribed mental states, and ascription is a means for supporting this rejection. We try to understand how and why ascription is functional to rejection. Then, we examine a few types of defensive projection, and identify the main components and steps in the projective process. Finally, we draw some general conclusions.
Vygotsky's Non‐classical Dialectical Metapsychology
Authors: Robbins Dorothy
Published: August 2003

The approach taken here is to begin to understand the focus from abstract to concrete in learning to master the principles of methodology, which are different from Western methods and procedures. This methodology is opposed to the empiricist approach of establishing rules and procedures from the concrete to the abstract. The initial discussion revolves around an explanation of the use of metaphor, metatheory, and psychology understood as a non‐classical science. There is then a discussion on dialectics, dialectical synthesis, and metafacts. The core of the article is dedicated to a metatheoretical description Vygotsky uses as an abstract outline of possible guidelines in better understanding how to implement a model for non‐classical psychology, using the example of a table being reflected in a mirror. A represents the actual table, while a represents the object being reflected in the mirror, and X represents the processes that take place in the mirror (i.e., the reflection of a table is the refraction of light beams). Therefore, Vygotsky concludes that both A and X are real processes and that a is their apparent, i.e., unreal result. The reflection does not exist, but both the table and the light exist. The result is that the objective existence of X and A independent of a is a dogma of materialistic psychology. There is a discussion on the interfunctional relationships of importance in such a model, and the role of consciousness, and language. The focus of this metatheoretical framework supports Vygotsky's intentions of reaching individual inner freedom and freedom of action, together with Spinoza's social determinism.
The Duality of Mobilisation—Following the Rise and Fall of an Alibi‐Story on its Way to Court1
Authors: Scheffer Thomas
Published: August 2003

This article suggests a discourse analysis suitable for multi‐dimensional processes. The exemplar in focus is a single narrative that travelled a long way through an English criminal pre‐trial to the finalising Crown Court‐hearing. The following case study asks how this story was mobilised by the defence to challenge the prosecution's case. The resulting sequential analysis of the story's career profits a good deal from Laboratory Studies. Like ethnographies in Science and Technology Studies, the analysis involves an extended production process—and the productivities of the artefacts produced. It shows, furthermore, how the mobilisation of the alibi is at some points threatened by emerging potentialities to undermine it. All in all, the resulting micro‐history on the story's career hinges on as well as exceeds common preferences in social constructivism: for collaborative and local accomplishments, for structural and antagonistic oppositions, and for success stories.
Modeling alternative zoning strategies in forest management
Authors: Krcmar Emina , Vertinsky Ilan , Van Kooten G. Cornelis
Published: August 2003

To satisfy public demands for environmental values, forest companies are facing the prospect of a reduction in wood supply and increases in costs. Some Canadian provincial governments have proposed intensifying silviculture in special zones dedicated to timber production as the means for pushing out the forest possibility frontiers. In this paper, we compare the traditional two‐zone land allocation framework which includes ecological reserves and integrated forest management zones with the triad — a three‐zone scheme which adds a zone dedicated to intensive timber production. We compare the solutions of the mixed‐integer linear programs formulated under both land‐allocation frameworks. We explore through sensitivity analysis the conditions under which the triad regime can offset the impact on timber production from increased environmental demands. We show that under the realistic conditions characteristic to Coastal British Columbia, higher environmental demands may be satisfied under the triad regime without increasing the financial burdens on the industry or reducing its wood supply. This occurs, however, only if regulatory constraints in timber production zone are flexible.
Estimating Vaccine Effects on Transmission of Infection from Household Outbreak Data
Authors: Becker Niels G. , Britton Tom , O'Neill Philip D.
Published: August 2003

Summary. This article is concerned with a method for making inferences about various measures of vaccine efficacy. These measures describe reductions in susceptibility and in the potential to transmit infection. The method uses data on household outbreaks; it is based on a model that allows for transmission of infection both from within a household and from the outside. The use of household data is motivated by the hope that these are informative about vaccine‐induced reduction of the potential to transmit infection, as household outbreaks contain some information about the possible source of infection. For illustration, the method is applied to observed data on household outbreaks of smallpox. These data are of the form needed and the number of households is of a size that can be managed in a vaccine trial. It is found that vaccine effects, such as the mean reduction in susceptibility and the mean reduction in the potential to infect others, per infectious contact, can be estimated with precision. However, a more specific parameter reflecting the reduction in infectivity for individuals partially responding to vaccination is not estimated well in the application. An evaluation of the method using artificial data shows that this parameter can be estimated with greater precision when we have outbreak data on a large number of small households.
Bayesian Shrinkage Estimation of the Relative Abundance of mRNA Transcripts Using SAGE
Authors: Morris Jeffrey S. , Baggerly Keith A. , Coombes Kevin R.
Published: August 2003

Summary.  Serial analysis of gene expression (SAGE) is a technology for quantifying gene expression in biological tissue that yields count data that can be modeled by a multinomial distribution with two characteristics: skewness in the relative frequencies and small sample size relative to the dimension. As a result of these characteristics, a given SAGE sample may fail to capture a large number of expressed mRNA species present in the tissue. Empirical estimators of mRNA species' relative abundance effectively ignore these missing species, and as a result tend to overestimate the abundance of the scarce observed species comprising a vast majority of the total. We have developed a new Bayesian estimation procedure that quantifies our prior information about these characteristics, yielding a nonlinear shrinkage estimator with efficiency advantages over the MLE. Our prior is mixture of Dirichlets, whereby species are stochastically partitioned into abundant and scarce classes, each with its own multivariate prior. Simulation studies reveal our estimator has lower integrated mean squared error (IMSE) than the MLE for the SAGE scenarios simulated, and yields relative abundance profiles closer in Euclidean distance to the truth for all samples simulated. We apply our method to a SAGE library of normal colon tissue, and discuss its implications for assessing differential expression.
Dose‐Finding with Two Agents in Phase I Oncology Trials
Authors: Thall Peter F. , Millikan Randall E. , Mueller Peter , Lee Sang‐Joon
Published: August 2003

Summary. We propose an adaptive two‐stage Bayesian design for finding one or more acceptable dose combinations of two cytotoxic agents used together in a Phase I clinical trial. The method requires that each of the two agents has been studied previously as a single agent, which is almost invariably the case in practice. A parametric model is assumed for the probability of toxicity as a function of the two doses. Informative priors for parameters characterizing the single‐agent toxicity probability curves are either elicited from the physician(s) planning the trial or obtained from historical data, and vague priors are assumed for parameters characterizing two‐agent interactions. A method for eliciting the single‐agent parameter priors is described. The design is applied to a trial of gemcitabine and cyclophosphamide, and a simulation study is presented.
Nonparametric Comparison of Two Survival‐Time Distributions in the Presence of Dependent Censoring
Authors: DiRienzo A. G.
Published: August 2003

Summary.  When testing the null hypothesis that treatment arm‐specific survival‐time distributions are equal, the log‐rank test is asymptotically valid when the distribution of time to censoring is conditionally independent of randomized treatment group given survival time. We introduce a test of the null hypothesis for use when the distribution of time to censoring depends on treatment group and survival time. This test does not make any assumptions regarding independence of censoring time and survival time. Asymptotic validity of this test only requires a consistent estimate of the conditional probability that the survival event is observed given both treatment group and that the survival event occurred before the time of analysis. However, by not making unverifiable assumptions about the data‐generating mechanism, there exists a set of possible values of corresponding sample‐mean estimates of these probabilities that are consistent with the observed data. Over this subset of the unit square, the proposed test can be calculated and a rejection region identified. A decision on the null that considers uncertainty because of censoring that may depend on treatment group and survival time can then be directly made. We also present a generalized log‐rank test that enables us to provide conditions under which the ordinary log‐rank test is asymptotically valid. This generalized test can also be used for testing the null hypothesis when the distribution of censoring depends on treatment group and survival time. However, use of this test requires semiparametric modeling assumptions. A simulation study and an example using a recent AIDS clinical trial are provided.
Estimating the Accuracy of Polymerase Chain Reaction–Based Tests Using Endpoint Dilution
Authors: Hughes James P. , Totten Patricia
Published: August 2003

Summary. Polymerase chain reaction (PCR)–based tests for various microorganisms or target DNA sequences are generally acknowledged to be highly “sensitive,” yet the concept of sensitivity is ill‐defined in the literature on these tests. We propose that sensitivity should be expressed as a function of the number of target DNA molecules in the sample (or specificity, when the target number is 0). However, estimating this “sensitivity curve” is problematic, since it is difficult to construct samples with a fixed number of targets. Nonetheless, using serially diluted replicate aliquots of a known concentration of the target DNA sequence, we show that it is possible to disentangle random variations in the number of target DNA molecules from the underlying test sensitivity. We develop parametric, nonparametric, and semiparametric (spline‐based) models for the sensitivity curve. The methods are compared on a new test forM. genitalium.
Abundance Estimation of Diving Animals by the Double‐Platform Line Transect Method
Authors: Okamura Hiroshi , Kitakado Toshihide , Hiramatsu Kazuhiko , Mori Mitsuyo
Published: August 2003

Summary.  In conventional line transect theory, it is assumed that all animals on the line are detected. This article introduces an extended and generalized hazard probability model without the need for such an assumption. The proposed method needs a survey design with independent observers having the same visual region and assumes an explicit distinction of simultaneous and delayed duplicates. It can take account of random heterogeneity caused by surfacing behavior as well as systematic heterogeneity by covariate effects. Furthermore, it can be easily extended to cases in which data from incompletely independent observers are available. The abundance estimate is based on the Horvitz‐Thompson estimator in unequal detectability sampling scheme. Simulation studies suggest that the proposed method has good performance. The method is applied to a real data set on Antarctic minke whales in the illustration.
A Bayesian Approach for Joint Modeling of Cluster Size and Subunit‐Specific Outcomes
Authors: Dunson David B. , Chen Zhen , Harry Jean
Published: August 2003

Summary.  In applications that involve clustered data, such as longitudinal studies and developmental toxicity experiments, the number of subunits within a cluster is often correlated with outcomes measured on the individual subunits. Analyses that ignore this dependency can produce biased inferences. This article proposes a Bayesian framework for jointly modeling cluster size and multiple categorical and continuous outcomes measured on each subunit. We use a continuation ratio probit model for the cluster size and underlying normal regression models for each of the subunit‐specific outcomes. Dependency between cluster size and the different outcomes is accommodated through a latent variable structure. The form of the model facilitates posterior computation via a simple and computationally efficient Gibbs sampler. The approach is illustrated with an application to developmental toxicity data, and other applications, to joint modeling of longitudinal and event time data, are discussed.
Sensitivity Analysis for the Assessment of Causal Vaccine Effects on Viral Load in HIV Vaccine Trials
Authors: Gilbert Peter B. , Bosch Ronald J. , Hudgens Michael G.
Published: August 2003

Summary.  Vaccines with limited ability to prevent HIV infection may positively impact the HIV/AIDS pandemic by preventing secondary transmission and disease in vaccine recipients who become infected. To evaluate the impact of vaccination on secondary transmission and disease, efficacy trials assess vaccine effects on HIV viral load and other surrogate endpoints measured after infection. A standard test that compares the distribution of viral load between the infected subgroups of vaccine and placebo recipients does not assess a causal effect of vaccine, because the comparison groups are selected after randomization. To address this problem, we formulate clinically relevant causal estimands using the principal stratification framework developed by Frangakis and Rubin (2002, Biometrics58, 21–29), and propose a class of logistic selection bias models whose members identify the estimands. Given a selection model in the class, procedures are developed for testing and estimation of the causal effect of vaccination on viral load in the principal stratum of subjects who would be infected regardless of randomization assignment. We show how the procedures can be used for a sensitivity analysis that quantifies how the causal effect of vaccination varies with the presumed magnitude of selection bias.
Identification of Differentially Expressed Genes in High‐Density Oligonucleotide Arrays Accounting for the Quantification Limits of the Technology
Authors: Tadesse Mahlet G. , Ibrahim Joseph G. , Mutter George L.
Published: August 2003

Summary. In DNA microarray analysis, there is often interest in isolating a few genes that best discriminate between tissue types. This is especially important in cancer, where different clinicopathologic groups are known to vary in their outcomes and response to therapy. The identification of a small subset of gene expression patterns distinctive for tumor subtypes can help design treatment strategies and improve diagnosis. Toward this goal, we propose a methodology for the analysis of high‐density oligonucleotide arrays. The gene expression measures are modeled as censored data to account for the quantification limits of the technology, and two gene selection criteria based on contrasts from an analysis of covariance (ANCOVA) model are presented. The model is formulated in a hierarchical Bayesian framework, which in addition to making the fit of the model straightforward and computationally efficient, allows us to borrow strength across genes. The elicitation of hierarchical priors, as well as issues related to parameter identifiability and posterior propriety, are discussed in detail. We examine the performance of our proposed method on simulated data, then present a detailed case study of an endometrial cancer dataset.
Estimation of Spontaneous Mutation Rates
Authors: Natarajan Loki , Berry Charles C. , Gasche Christoph
Published: August 2003

Summary. Spontaneous or randomly occurring mutations play a key role in cancer progression. Estimation of the mutation rate of cancer cells can provide useful information about the disease. To ascertain these mutation rates, we need mathematical models that describe the distribution of mutant cells. In this investigation, we develop a discrete time stochastic model for a mutational birth process. We assume that mutations occur concurrently with mitosis so that when a nonmutant parent cell splits into two progeny, one of these daughter cells could carry a mutation. We propose an estimator for the mutation rate and investigate its statistical properties via theory and simulations. A salient feature of this estimator is the ease with which it can be computed. The methods developed herein are applied to a human colorectal cancer cell line and compared to existing continuous time models.
Models for Yeast Prions
Authors: Morgan B. J. T. , Ridout M. S. , Ruddock L. W.
Published: August 2003

Summary. The cytoplasmic heritable determinant [PSI+] of the yeastSaccharomyces cerevisiaeexhibits prion‐like properties. The properties of yeast prions are studied in the hope that this will enhance the understanding of mammalian prions, which cause mad‐cow, Creutzfeldt‐Jakob, and related neurodegenerative diseases. When host cells divide, the yeast prions distribute themselves without loss over the daughter cells. Experimental data provide information on how the proportion of cells with prions decreases over time when prion replication is inhibited. One feature of scientific interest is the unknown mean number,n0, of prions assumed to be present in the cells at the start of the experiment. We develop several stochastic models and by fitting them to the data, we obtain substantially larger estimates ofn0compared with a previous analysis. An interesting feature of a model with constant cell generation times is that the predicted proportion of cells with prions varies over time as a sequence of linked hyperbolic curves. Avenues for future research are outlined, which relax simplifying assumptions made in the models. We make several recommendations for the design of future experiments.
Hazard Regression for Interval‐Censored Data with Penalized Spline
Authors: Cai Tianxi , Betensky Rebecca A.
Published: August 2003

Summary. This article introduces a new approach for estimating the hazard function for possibly interval‐ and right‐censored survival data. We weakly parameterize the log‐hazard function with a piecewise‐linear spline and provide a smoothed estimate of the hazard function by maximizing the penalized likelihood through a mixed model–based approach. We also provide a method to estimate the amount of smoothing from the data. We illustrate our approach with two well‐known interval‐censored data sets. Extensive numerical studies are conducted to evaluate the efficacy of the new procedure.
A New Method for Choosing Sample Size for Confidence Interval–Based Inferences
Authors: Jiroutek Michael R. , Muller Keith E. , Kupper Lawrence L. , Stewart Paul W.
Published: August 2003

Summary.  Scientists often need to test hypotheses and construct corresponding confidence intervals. In designing a study to test a particular null hypothesis, traditional methods lead to a sample size large enough to provide sufficient statistical power. In contrast, traditional methods based on constructing a confidence interval lead to a sample size likely to control the width of the interval. With either approach, a sample size so large as to waste resources or introduce ethical concerns is undesirable. This work was motivated by the concern that existing sample size methods often make it difficult for scientists to achieve their actual goals. We focus on situations which involve a fixed, unknown scalar parameter representing the true state of nature. The width of the confidence interval is defined as the difference between the (random) upper and lower bounds. An event width is said to occur if the observed confidence interval width is less than a fixed constant chosen a priori. An event validity is said to occur if the parameter of interest is contained between the observed upper and lower confidence interval bounds. An event rejection is said to occur if the confidence interval excludes the null value of the parameter. In our opinion, scientists often implicitly seek to have all three occur: width, validity, and rejection. New results illustrate that neglecting rejection or width (and less so validity) often provides a sample size with a low probability of the simultaneous occurrence of all three events. We recommend considering all three events simultaneously when choosing a criterion for determining a sample size. We provide new theoretical results for any scalar (mean) parameter in a general linear model with Gaussian errors and fixed predictors. Convenient computational forms are included, as well as numerical examples to illustrate our methods.
Bayesian Optimal Designs for Phase I Clinical Trials
Authors: Haines Linda M. , Perevozskaya Inna , Rosenberger William F.
Published: August 2003

Summary. A broad approach to the design of Phase I clinical trials for the efficient estimation of the maximum tolerated dose is presented. The method is rooted in formal optimal design theory and involves the construction of constrained Bayesianc‐ andD‐optimal designs. The imposed constraint incorporates the optimal design points and their weights and ensures that the probability that an administered dose exceeds the maximum acceptable dose is low. Results relating to these constrained designs for log doses on the real line are described and the associated equivalence theorem is given. The ideas are extended to more practical situations, specifically to those involving discrete doses. In particular, a Bayesian sequential optimal design scheme comprising a pilot study on a small number of patients followed by the allocation of patients to doses one at a time is developed and its properties explored by simulation.
Generalized Additive Selection Models for the Analysis of Studies with Potentially Nonignorable Missing Outcome Data
Authors: Scharfstein Daniel O. , Irizarry Rafael A.
Published: August 2003

Summary.  Rotnitzky, Robins, and Scharfstein (1998, Journal of the American Statistical Association93, 1321–1339) developed a methodology for conducting sensitivity analysis of studies in which longitudinal outcome data are subject to potentially nonignorable missingness. In their approach, they specify a class of fully parametric selection models, indexed by a non‐ or weakly identified selection bias function that indicates the degree to which missingness depends on potentially unobservable outcomes. Estimation of the parameters of interest proceeds by varying the selection bias function over a range considered plausible by subject‐matter experts. In this article, we focus on cross‐sectional, univariate outcome data and extend their approach to a class of semiparametric selection models, using generalized additive restrictions. We propose a backfitting algorithm to estimate the parameters of the generalized additive selection model. For estimation of the mean outcome, we propose three types of estimating functions: simple inverse weighted, doubly robust, and orthogonal. We present the results of a data analysis and a simulation study.
Partial AUC Estimation and Regression
Authors: Dodd Lori E. , Pepe Margaret S.
Published: August 2003

Summary. Accurate diagnosis of disease is a critical part of health care. New diagnostic and screening tests must be evaluated based on their abilities to discriminate diseased from nondiseased states. The partial area under the receiver operating characteristic (ROC) curve is a measure of diagnostic test accuracy. We present an interpretation of the partial area under the curve (AUC), which gives rise to a nonparametric estimator. This estimator is more robust than existing estimators, which make parametric assumptions. We show that the robustness is gained with only a moderate loss in efficiency. We describe a regression modeling framework for making inference about covariate effects on the partial AUC. Such models can refine knowledge about test accuracy. Model parameters can be estimated using binary regression methods. We use the regression framework to compare two prostate‐specific antigen biomarkers and to evaluate the dependence of biomarker accuracy on the time prior to clinical diagnosis of prostate cancer.
Sequential Conditional Probability Ratio Tests for Normalized Test Statistic on Information Time
Authors: Xiong Xiaoping , Tan Ming , Boyett James
Published: August 2003

Summary.  Sequential conditional probability ratio tests (SCPRTs) provide monitoring procedures with a unique property that a decision reached at early stopping is unlikely to be reversed should the trial continue to the planned end (Xiong, X., 1995, Journal of the American Statistical Association90, 1463–1473; Tan, M., Xiong, X., and Kutner, M. H., 1998, Biometrics54, 682–695). It actually provides a probability statement of a conclusion reversal, should the proposed interim analysis plan be used. To broaden its scope of applications, in this article we develop the SCPRT in terms of Brownian motion, which is applicable to most clinical trials with various endpoints. In addition, we utilize the unique structure of the SCPRT to derive a class of adaptive sequential tests that retain the significance level and power in the presence of a nuisance parameter. We illustrate the proposed methods with examples in clinical trials.
Analyzing Multiply Matched Cohort Studies with Two Different Comparison Groups: Application to Pregnancy Rates among HIV+ Women
Authors: Li Yan , Zelterman Daniel , Forsyth Brian W. C.
Published: August 2003

Summary. We develop a new statistical method to analyze multiply matched cohort studies with two different comparison groups. We employ a linear‐logistic model to describe the underlying log‐odds ratios and use a conditional likelihood approach to conduct inference. Under the assumption of homogeneous log‐odds ratios, we provide methods to construct both asymptotic and exact confidence regions of the two log‐odds ratios in a simple case. We propose a score test to evaluate the assumption of homogeneous log‐odds ratios across strata. While our methods are general, we develop them around a specific application, namely, the study of pregnancy rates in HIV‐infected women. Our analyses suggest that HIV infection is associated with a decrease in pregnancy rates and that this decrease in fertility becomes significant after accounting for illicit drug use.
A Spatiotemporal Stochastic Process Model for Species Spread
Authors: Fewster R. M.
Published: August 2003

Summary. We use a spatiotemporal Markov process to model the spread of an ecological population through its environment over time. Available habitat is divided into sites, and a parametric function of spatial variables is used to model the probability that one site is colonized from another. This allows us both to make predictions about the future spread of a population, and to determine which are the important factors governing colonizations. The model evolves in discrete time, allowing the population distribution to change seasonally in accordance with breeding patterns. Discrete time formulations are natural for ecological populations, but are problematic due to difficulties of fitting and predicting over irregular time intervals. The model described here can accommodate years of missing data and can therefore fit and predict at irregular intervals. Two methods of approximating the likelihood are described and applied to ornithological survey data for the woodlark,Lullula arborea,from Thetford Forest in the U.K.
Bayesian Deconvolution Analysis of Pulsatile Hormone Concentration Profiles
Authors: Johnson Timothy D.
Published: August 2003

Summary.  Many hormones are secreted into the circulatory system in a pulsatile manner and are cleared exponentially. The most common method of analyzing these systems is to deconvolve the hormone concentration into a secretion function and a clearance function. Accurate estimation of the model parameters depends on the number and location of the secretion pulses. To date, deconvolution analysis assumes the number and approximate location of these pulses are known a priori. In this article, we present a novel Bayesian approach to deconvolution that jointly models the number of pulses along with all other model parameters. Our method stochastically searches for the secretion pulses. This is accomplished by viewing the set of parameters that define the pulses as a point process. Pulses are determined by a birth‐death process which is embedded in Markov chain Monte Carlo algorithm. This idea originated with Stephens (2000, Annals of Statistics28, 40–74) in the context of finite mixture model density estimation, where the number of mixture components is unknown. There are several advantages that our model enjoys over the traditional frequentist approaches. These advantages are highlighted with four datasets consisting of serum concentration levels of luteinizing hormone obtained from ovariectomized ewes.
Parametric Modeling of Reaction Time Experiment Data
Authors: Braun W. John , Rousson Valentin , Simpson William A. , Prokop Jennifer
Published: August 2003

Summary.  A simple parametric model is proposed for data from a point‐process version of a reaction time experiment. It is used to statistically check for the presence and nature of nonlinear inhibition in the eye‐brain‐hand system, as well as to study the nature of the reaction time delay distribution. The model tells us that, in principle, the second‐order intensity estimate can be used to determine whether the experimental subject is systematically observing the first or the second of two flashes transmitted in short succession. Nonparametric estimates of second‐order intensity functions are used in conjunction with this model. In particular, the model allows for the computation of good bandwidths for intensity curve estimation. A parametric bootstrap can also be implemented. Our methods are illustrated with 12 runs of data from a real reaction time experiment. It is found that nonlinear inhibition is present in the eye‐brain‐hand system. However, there are insufficient data to distinguish between log‐normality and normality in the reaction time distribution, due partly to confounding with the particular kind of nonlinear inhibition present in the system.
Binomial Regression with Misclassification
Authors: Daniel Paulino Carlos , Soares Paulo , Neuhaus John
Published: August 2003

Summary.  Motivated by a study of human papillomavirus infection in women, we present a Bayesian binomial regression analysis in which the response is subject to an unconstrained misclassification process. Our iterative approach provides inferences for the parameters that describe the relationships of the covariates with the response and for the misclassification probabilities. Furthermore, our approach applies to any meaningful generalized linear model, making model selection possible. Finally, it is straightforward to extend it to multinomial settings.
Shrinkage Estimation for Functional Principal Component Scores with Application to the Population Kinetics of Plasma Folate
Authors: Yao Fang , Müller Hans‐Georg , Clifford Andrew J. , Dueker Steven R. , Follett Jennifer , Lin Yumei , Buchholz Bruce A. , Vogel John S.
Published: August 2003

Summary. We present the application of a nonparametric method to performing functional principal component analysis for functional curve data that consist of measurements of a random trajectory for a sample of subjects. This design typically consists of an irregular grid of time points on which repeated measurements are taken for a number of subjects. We introduce shrinkage estimates for the functional principal component scores that serve as the random effects in the model. Scatterplot smoothing methods are used to estimate the mean function and covariance surface of this model. We propose improved estimation in the neighborhood of and at the diagonal of the covariance surface, where the measurement errors are reflected. The presence of additive measurement errors motivates shrinkage estimates for the functional principal component scores. Shrinkage estimates are developed through best linear prediction and in a generalized version, aiming at minimizing one‐curve‐leave‐out prediction error. The estimation of individual trajectories combines data obtained from that individual as well as all other individuals. We apply our methods to new data regarding the analysis of the level of14C‐folate in plasma as a function of time since dosing of healthy adults with a small tracer dose of14C‐folic acid. A time transformation was incorporated to handle design irregularity concerning the time points on which the measurements were taken. The proposed methodology, incorporating shrinkage and data‐adaptive features, is seen to be well suited for describing population kinetics of14C‐folate‐specific activity and random effects, and can also be applied to other functional data analysis problems.
Bayesian Approaches to Joint Cure‐Rate and Longitudinal Models with Applications to Cancer Vaccine Trials
Authors: Brown Elizabeth R. , Ibrahim Joseph G.
Published: August 2003

Summary.  Complex issues arise when investigating the association between longitudinal immunologic measures and time to an event, such as time to relapse, in cancer vaccine trials. Unlike many clinical trials, we may encounter patients who are cured and no longer susceptible to the time‐to‐event endpoint. If there are cured patients in the population, there is a plateau in the survival function, S(t), after sufficient follow‐up. If we want to determine the association between the longitudinal measure and the time‐to‐event in the presence of cure, existing methods for jointly modeling longitudinal and survival data would be inappropriate, since they do not account for the plateau in the survival function. The nature of the longitudinal data in cancer vaccine trials is also unique, as many patients may not exhibit an immune response to vaccination at varying time points throughout the trial. We present a new joint model for longitudinal and survival data that accounts both for the possibility that a subject is cured and for the unique nature of the longitudinal data. An example is presented from a cancer vaccine clinical trial.
Balancing Population Size and Genetic Information in Parentage Analysis Studies
Authors: Jones Beatrix
Published: August 2003

Summary.  In parentage analysis studies, the parameters of interest typically are not the parent assignments themselves, but population parameters such as variance in fertility, self‐pollination rate, or average dispersal distances. The precision of parameter estimates is affected by two factors: the number of offspring under consideration, and the precision with which the offspring can be assigned to parents. When assignment of parents is based on genetic information, the confidence in assignments is affected by the number and polymorphism of the loci considered, and by the number of potential parents in the population. Studying larger populations may yield higher numbers of offspring, but since larger populations contain more potential parents, more (or more highly polymorphic) loci are necessary to attain a given level of confidence in the parent assignments. This article addresses how to relate the size of the population and the number of loci when designing a study. It is shown that the number of loci needed to assign all offspring unambiguously is proportional to the logarithm of the population size. In some cases, the constant of proportionality can be determined, eliminating the need for simulation‐based projections. Population‐wide measures of uncertainty in parent assignments are also introduced, and it is shown that holding uncertainty “steady” as the population size increases also requires increasing the number of loci proportional to the logarithm of the population size. Data from a study of self‐pollination are used to illustrate the techniques suggested.
Incorporating Data Received after a Sequential Trial Has Stopped into the Final Analysis: Implementation and Comparison of Methods
Authors: Sooriyarachchi Marina Roshini , Whitehead John , Tatsuru Matsushita  , Bolland Kim , Whitehead Anne
Published: August 2003

Summary. In a sequential clinical trial, accrual of data on patients often continues after the stopping criterion for the study has been met. This is termed “overrunning.” Overrunning occurs mainly when the primary response from each patient is measured after some extended observation period. The objective of this article is to compare two methods of allowing for overrunning. In particular, simulation studies are reported that assess the two procedures in terms of how well they maintain the intended type I error rate. The effect on power resulting from the incorporation of “overrunning data” using the two procedures is evaluated.
Latent Transition Regression for Mixed Outcomes
Authors: Miglioretti Diana L.
Published: August 2003

Summary.  Health status is a complex outcome, often characterized by multiple measures. When assessing changes in health status over time, multiple measures are typically collected longitudinally. Analytic challenges posed by these multivariate longitudinal data are further complicated when the outcomes are combinations of continuous, categorical, and count data. To address these challenges, we propose a fully Bayesian latent transition regression approach for jointly analyzing a mixture of longitudinal outcomes from any distribution. Health status is assumed to be a categorical latent variable, and the multiple outcomes are treated as surrogate measures of the latent health state, observed with error. Using this approach, both baseline latent health state prevalences and the probabilities of transitioning between the health states over time are modeled as functions of covariates. The observed outcomes are related to the latent health states through regression models that include subject‐specific effects to account for residual correlation among repeated measures over time, and covariate effects to account for differential measurement of the latent health states. We illustrate our approach with data from a longitudinal study of back pain.
Goodness‐of‐Fit Methods for Additive‐Risk Models in Tumorigenicity Experiments
Authors: Ghosh Debashis
Published: August 2003

Summary. In tumorigenicity experiments, a complication is that the time to event is generally not observed, so that the time to tumor is subject to interval censoring. One of the goals in these studies is to properly model the effect of dose on risk. Thus, it is important to have goodness of fit procedures available for assessing the model fit. While several estimation procedures have been developed for current‐status data, relatively little work has been done on model‐checking techniques. In this article, we propose numerical and graphical methods for the analysis of current‐status data using the additive‐risk model, primarily focusing on the situation where the monitoring times are dependent. The finite‐sample properties of the proposed methodology are examined through numerical studies. The methods are then illustrated with data from a tumorigenicity experiment.
The Evidential Value in the DNA Database Search Controversy and the Two‐Stain Problem
Authors: Meester Ronald , Sjerps Marjan
Published: August 2003

Summary. Does the evidential strength of a DNA match depend on whether the suspect was identified through database search or through other evidence (“probable cause”)? In Balding and Donnelly (1995,Journal of the Royal Statistical Society, Series A158,21–53) and elsewhere, it has been argued that the evidential strength is slightly larger in a database search case than in a probable cause case, whileStockmarr (1999,Biometrics55,671–677) reached the opposite conclusion. Both these approaches use likelihood ratios. By making an excursion to a similar problem, the two‐stain problem, we argue in this article that there are certain fundamental difficulties with the use of a likelihood ratio, which can be avoided by concentrating on the posterior odds. This approach helps resolving the above‐mentioned conflict.
Role of villus microcirculation in intestinal absorption of glucose: coupling of epithelial with endothelial transport
Authors: Pappenheimer J. R. , Michel C. C.
Published: August 2003

Capillaries in jejunal villi can absorb nutrients at rates several hundred times greater (per gram tissue) than capillaries in other tissues, including contracting skeletal muscle and brain. We here present an integrative hypothesis to account for these exceptionally large trans‐endothelial fluxes and their relation to epithelial transport. Equations are developed for estimating concentration gradients of glucose across villus capillary walls, along paracellular channels and across subjunctional lateral membranes of absorptive cells. High concentrations of glucose discharged across lateral membranes to subjunctional intercellular spaces are delivered to abluminal surfaces of villus capillaries by convection‐diffusion in intercellular channels without significant loss of concentration. Post‐junctional paracellular transport thus provides the series link between epithelial and endothelial transport and makes possible the large trans‐endothelial concentration gradients required for absorption to blood. Our analysis demonstrates that increases of villus capillary blood flow and permeability‐surface area product (PS) are essential components of absorptive mechanisms: epithelial transport of normal digestive loads could not be sustained without concomitant increases in capillary blood flow and PS. The low rates of intestinal absorption found in anaesthetised animals may be attributed to inhibition of normal villus microvascular responses to epithelial transport.
Sodium and calcium currents shape action potentials in immature mouse inner hair cells
Authors: Marcotti Walter , Johnson Stuart L. , Rüsch Alfons , Kros Corné J.
Published: August 2003

Before the onset of hearing at postnatal day 12, mouse inner hair cells (IHCs) produce spontaneous and evoked action potentials. These spikes are likely to induce neurotransmitter release onto auditory nerve fibres. Since immature IHCs express both α1D (Cav1.3) Ca2+ and Na+ currents that activate near the resting potential, we examined whether these two conductances are involved in shaping the action potentials. Both had extremely rapid activation kinetics, followed by fast and complete voltage‐dependent inactivation for the Na+ current, and slower, partially Ca2+‐dependent inactivation for the Ca2+ current. Only the Ca2+ current is necessary for spontaneous and induced action potentials, and 29 % of cells lacked a Na+ current. The Na+ current does, however, shorten the time to reach the action‐potential threshold, whereas the Ca2+ current is mainly involved, together with the K+ currents, in determining the speed and size of the spikes. Both currents increased in size up to the end of the first postnatal week. After this, the Ca2+ current reduced to about 30 % of its maximum size and persisted in mature IHCs. The Na+ current was downregulated around the onset of hearing, when the spiking is also known to disappear. Although the Na+ current was observed as early as embryonic day 16.5, its role in action‐potential generation was only evident from just after birth, when the resting membrane potential became sufficiently negative to remove a sizeable fraction of the inactivation (half inactivation was at −71 mV). The size of both currents was positively correlated with the developmental change in action‐potential frequency.
Modulation of Ca2+‐dependent Cl− channels by calcineurin in rabbit coronary arterial myocytes
Authors: Ledoux Jonathan , Greenwood Iain , Villeneuve Louis R. , Leblanc Normand
Published: August 2003

The role of the Ca2+‐dependent phosphatase calcineurin (CaN) in the modulation of Ca2+‐dependent Cl‐ channels (ClCa) was studied in freshly isolated rabbit coronary arterial myocytes. Immunocytochemical experiments showed that calmodulin‐dependent protein kinase II (CaMKII) and CaN were distributed evenly throughout the cytoplasm of coronary myocytes at rest and translocated to the plasmalemma when intracellular Ca2+ was increased. ClCa currents (ICl(Ca)) elicited by cell dialysis with fixed intracellular Ca2+ levels up to 500 nm were inhibited by 10 μm cyclosporin A (CsA), a specific inhibitor of CaN, in a voltage‐dependent manner, whereas currents evoked by 1 μm Ca2+ were not affected. Inhibition of CaN with CsA also led to a significant reduction in Ca2+ sensitivity of the channel at +50 mV; half‐maximal activation increased from 363 ± 16 nm Ca2+ in control to 515 ± 40 nm Ca2+ in the presence of CsA. Similar effects were observed on ICl(Ca) when a specific peptide fragment inhibitor of CaN (CaN‐AF, 5 μm) was dialysed into the cell via the pipette (500 nm Ca2+). Application of KN‐93 (10 μm), a specific inhibitor of CaMKII, enhanced ICl(Ca) in myocytes dialysed with 1 μm Ca2+ but produced no significant effect on this current when the cells were dialysed with 350 or 500 nm Ca2+. These results are consistent with the notion that in coronary arterial cells, the activity of ClCa is enhanced by dephosphorylation of the channel or a closely associated regulatory protein. Moreover the balance of CaN and CaMKII regulating ICl(Ca) is dependent on the level of Ca2+ used to activate ICl(Ca).