bluuebunny/crossref_metadata_2025_split huggingface dataset:
Trajectory tracking control of robot arm by using computational models of spinal cord and cerebellum
Authors: Nakayama Takayuki , Kimura Hidenori 
Published: August 2004

AbstractIn studies of robots, intended for collaborative operation with humans, there have been intensive efforts to develop and apply a muscle‐driving system with mechanically soft properties, to reduce the danger to humans. Such a muscle‐driving system has the advantage that the danger to humans in collaborative operation can be reduced, but has the disadvantage that accurate control is difficult due to the soft properties. The soft and accurate motion of the limbs in biological systems is realized by skillful control of the muscles by the control system composed of the spinal cord and the cerebellum. Consequently, inspired by the motion control mechanism in biological systems, this paper tries to develop a control system that provides accurate trajectory tracking control of the robot, by using a muscle‐driving actuator with soft mechanical properties. The proposed control system consists of the computational models of the stretch reflex system in the spinal cord, which compensates the external load torque exerted on the joint, and the cerebellum, which compensates the inertial torque with learning the internal model of the limb dynamics. Using the indirect Lyapunov method, it is shown theoretically that the posture of the robot asymptotically approaches the desired trajectory when driven by the proposed control system. A computer simulation is performed in order to demonstrate the effectiveness of the control system, and it is verified that the behavior converges satisfactorily to the desired target. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 35(11): 1–13, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10646
Confidence measure and rejection based on correctness probability of recognition candidates
Authors: Kitaoka Norihide , Akahori Ichiro , Nakagawa Seiichi 
Published: August 2004

AbstractWe propose a confidence measure expressing the correctness probability of recognition candidates, to be applied to a speech dialog system using a dialog strategy based on the degree of confidence of speech recognition candidates. We assume a function expressing the correctness probability of the feature parameters associated with the recognition candidate, and its parameters are estimated by using the square error of the correct (1) and incorrect (0) data as the evaluation function. For two feature quantities, namely, the likelihood ratio of the recognized word and syllable‐concatenated model, and the variance of the syllable duration inside a word, the correctness probability can be expressed by a sigmoid function. The correctness probability can also be expressed for a combination of these two feature parameters. Rejection experiments involving evaluation of the degree of discrimination between the correct and incorrect data show that the degree of discrimination is higher when the two feature parameters are combined than when they are used individually. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 35(11): 91–103, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.20046
Smooth centile curves for skew and kurtotic data modelled using the Box–Cox power exponential distribution
Authors: Rigby Robert A. , Stasinopoulos D. Mikis 
Published: August 2004

AbstractThe Box–Cox power exponential (BCPE) distribution, developed in this paper, provides a model for a dependent variableYexhibiting both skewness and kurtosis (leptokurtosis or platykurtosis). The distribution is defined by a power transformationYνhaving a shifted and scaled (truncated) standard power exponential distribution with parameterτ. The distribution has four parameters and is denoted BCPE (µ,σ,ν,τ). The parameters,µ,σ,νandτ, may be interpreted as relating to location (median), scale (approximate coefficient of variation), skewness (transformation to symmetry) and kurtosis (power exponential parameter), respectively.Smooth centile curves are obtained by modelling each of the four parameters of the distribution as a smooth non‐parametric function of an explanatory variable. A Fisher scoring algorithm is used to fit the non‐parametric model by maximizing a penalized likelihood. The first and expected second and cross derivatives of the likelihood, with respect toµ,σ,νandτ, required for the algorithm, are provided. The centiles of the BCPE distribution are easy to calculate, so it is highly suited to centile estimation.This application of the BCPE distribution to smooth centile estimation provides a generalization of the LMS method of the centile estimation to data exhibiting kurtosis (as well as skewness) different from that of a normal distribution and is named here the LMSP method of centile estimation. The LMSP method of centile estimation is applied to modelling the body mass index of Dutch males against age. Copyright © 2004 John Wiley & Sons, Ltd.
An analysis of clustered categorical data—application in dental health
Authors: Das Kalyan , Chattopadhyay Asis Kumar 
Published: August 2004

AbstractQuite often in medical studies multiple discrete indicators are used to measure some characters that are defined only conceptually and are difficult to measure directly. Studies of this type exhibit categorical responses of dependent nature. Analysis of such categorical data appears to be extremely difficult (intractable) particularly in the presence of risk (causal) factors. In the present article, our purpose is to develop a latent mixture regression model for analysing such multivariate categorical data. Such a mixture model accommodates correlated and overdispersed data through the incorporation of random effects. Unfortunately, a full likelihood analysis is often hampered by the need for numerical integration. Two different procedures have been considered here. Both involve intensive computations. Numerical investigation has been carried out on the basis of a survey data covering 220 individuals from medical colleges in and around Calcutta (India). The purpose of the study is to compare tooth cleaning efficiency of brushes manufactured by different companies. Copyright © 2004 John Wiley & Sons, Ltd.
Estimating vaccine efficacy from household data observed over time
Authors: Davis Xiaohong M. , Haber Michael 
Published: August 2004

AbstractEstimation of vaccine efficacy has traditionally focused on the reduction in susceptibility to infection, or the vaccine efficacy for susceptibility (VES). However, a vaccine, such as a prophylactic HIV vaccine, may also lower the infectiousness of a vaccinated person who became infected. The relative reduction in infectiousness due to vaccination is the vaccine efficacy for infectiousness (VEI). Estimation of VEI is challenging because it requires information on exposure to infection, and gathering this type of information is often expensive and difficult, or even impossible. Household studies are expected to provide more information on who is exposed to whom. In a previous paper, we developed a method for estimating VES and VEI from a household study where only the final outbreak data are available. However, the resulting estimates were quite unstable. In this work, we develop a survival model for the estimation of VES and VEI from household data where the time of infection is known for every study participant. Using stochastic simulations, we show that the proposed method significantly reduces the bias and mean square error in the estimation of both VES and VEI as compared to the method based on final outbreak data. We also show that when time‐to‐event data are available, a household study produces more robust estimators than a same‐size study of unrelated individuals. In addition, we investigate the bias in estimating VES and VEI due to misclassification of infection status when only illness data, rather than true infection data, are available. Copyright © 2004 John Wiley & Sons, Ltd.
Imputation for incomplete high‐dimensional multivariate normal data using a common factor model
Authors: Song Juwon , Belin Thomas R. 
Published: August 2004

AbstractIt is common in applied research to have large numbers of variables measured on a modest number of cases. Even with low rates of missingness on individual variables, such data sets can have a large number of incomplete cases. Here we present a new method for handling missing continuously scaled items in multivariate data, based on extracting common factors to reduce the number of covariance parameters to be estimated in a multivariate normal model. The technique is compared in several simulation settings to available‐case analysis and to a multivariate normal model with a ridge prior. The method is also illustrated on a study with over 100 variables evaluating an emergency room intervention for adolescents who attempted suicide. Copyright © 2004 John Wiley & Sons, Ltd.
Cross calibration in longitudinal studies
Authors: Ambrosius Walter T. , Hui Siu L. 
Published: August 2004

AbstractIn a long‐running longitudinal study using complex machinery to obtain measurements, it is sometimes necessary to replace the machine. This can result in lack of continuity in the measurements that can overwhelm any treatment effect or time trend. We propose a Bayesian procedure implemented using Markov chain Monte Carlo to calibrate the measurements on the old machine utilizing both person‐specific and population information. The goal is to convert the previous measurements to values that can be treated as though they were made on the new machine. This methodology is applied to a bone mineral density study where the first densitometer uses gadolinium as the energy source (Lunar DP‐3) and the second uses X‐rays (Hologic QDR‐1000W). Finally, simulation results are presented to show the superiority of the proposed method over existing methods of cross calibration. Copyright © 2004 John Wiley & Sons, Ltd.
Power analyses for longitudinal trials and other clustered designs
Authors: Tu X. M. , Kowalski J. , Zhang J. , Lynch K. G. , Crits‐Christoph P. 
Published: August 2004

AbstractExisting methods for power and sample size estimation for longitudinal and other clustered study designs have limited applications. In this paper, we review and extend existing approaches to improve these limitations. In particular, we focus on power analysis for the two most popular approaches for clustered data analysis, the generalized estimating equations and the linear mixed‐effects models. By basing the derivation of the power function on the asymptotic distribution of the model estimates, the proposed approach provides estimates of power that are consistent with the methods of inference for data analysis. The proposed methodology is illustrated with numerous examples that are motivated by real study designs. Copyright © 2004 John Wiley & Sons, Ltd.
A statistical model for high‐resolution mapping of quantitative trait loci determining HIV dynamics
Authors: Wang Zuoheng , Wu Rongling 
Published: August 2004

AbstractAre there specific genes that control the pathogenesis of HIV infection? This question, which is of fundamental importance in designing personalized strategies of gene therapy to control HIV infection, can be examined by genetic mapping approaches. In this article, we present a new statistical model for unravelling the genetic mechanisms for the dynamic change of HIV that causes AIDS by marker‐based linkage disequilibrium (LD) analyses. This new model is the extension of our functional mapping theory to integrate viral load trajectories within a genetic mapping framework. Earlier studies of HIV dynamics have led to various mathematical functions for modelling the kinetic curves of plasma virions and CD4 lymphocytes in HIV patients. Through incorporating these functions into the LD‐based mapping procedure, we can identify and map individual quantitative trait loci (or QTL) responsible for viral pathogenesis. We derive a closed‐form solution for estimating QTL allele frequency and marker‐QTL linkage disequilibrium in the context of EM algorithm and implement the simplex algorithm to estimate the mathematical parameters describing the curve shapes of HIV pathogenesis. We performed different simulation scenarios based on currently used clinical designs in AIDS/HIV research to illustrate the utility and power of our model for genetic mapping of HIV dynamics. The implications of our model for genetic and genomic research into AIDS pathogenesis are discussed. Copyright © 2004 John Wiley & Sons, Ltd.
Two‐stage adaptive strategy for superiority and non‐inferiority hypotheses in active controlled clinical trials
Authors: Shih Weichung J. , Quan Hui , Li Gang 
Published: August 2004

AbstractIn active controlled trials without a placebo arm, there are usually two study objectives: to test a superiority hypothesis that the experimental treatment is more effective than the active control therapy, and to test a non‐inferiority hypothesis that the experimental treatment is therapeutically no worse than the active control within a defined margin. For a two‐stage adaptive design, it is not necessary to give a fixed sample size calculation at the planning stage of the study when treatment effect information is often insufficient. Instead, decision and estimation of the design specifications can be made more reliably after the first stage when interim results are available. We propose the use of conditional power approach to determine the sample size and critical values for testing the superiority and non‐inferiority hypotheses for the second stage based on the observed result of the first stage. The proposed adaptive procedure preserves the overall type I error rate for both superiority and non‐inferiority, and has the flexibility of early termination of the study (for futility or efficacy) or extending the study by appropriate sample size. Copyright © 2004 John Wiley & Sons, Ltd.
Improved confidence intervals for a binomial parameter following a group sequential phase II clinical trial
Authors: Chang Myron N. 
Published: August 2004

AbstractGroup sequential procedures are widely employed in phase II clinical trials. It is often desirable to provide an interval estimate of the true response rate upon termination of a group sequential phase II clinical trial. The confidence intervals proposed by Jennison and Turnbull (Technometrics 1983; 25: 49–58) are conservative. Utilization of auxiliary statistics based on the overall disease status is proposed to reduce the discreteness of the underlying distribution. Confidence intervals generated by the proposed methods have confidence coefficients closer to the nominal level and have shorter average lengths than JT. Copyright © 2004 John Wiley & Sons, Ltd.
Regression analysis of multiple source and multiple informant data from complex survey samples
Authors: Horton Nicholas J. , Fitzmaurice Garrett M. 
Published: August 2004

AbstractIn this tutorial, we describe regression‐based methods for analysing multiple source data arising from complex sample survey designs. We use the term ‘multiple‐source’ data to encompass all cases where data are simultaneously obtained from multiple informants, or raters (e.g. self‐reports, family members, health care providers, administrators) or via different/parallel instruments, indicators or methods (e.g. symptom rating scales, standardized diagnostic interviews, or clinical diagnoses). We review regression models for analysing multiple source risk factors or multiple source outcomes and show that they can be considered special cases of generalized linear models, albeit with correlated outcomes. We show how these methods can be extended to handle the common survey features of stratification, clustering, and sampling weights. We describe how to fit regression models with multiple source reports derived from complex sample surveys using general purpose statistical software. Finally, the methods are illustrated using data from two studies: the Stirling County Study and the Eastern Connecticut Child Survey. Copyright © 2004 John Wiley & Sons, Ltd.
Modelling infectious disease transmission with complex exposure pattern and sparse outcome data
Authors: Reilly Marie , Salim Agus , Lawlor Emer , Smith Owen , Temperley Ian , Pawitan Yudi 
Published: August 2004

AbstractWe present a regression modelling framework to analyse infectious disease transmission during a time period where extensive exposure data are available, but where the outcome data are sparse. A latent variable model is used for each exposure time, allowing a straight‐forward accumulation of risk for a collection of exposures for which outcome data are available. We describe an analysis of HIV infection from blood products among a cohort of haemophiliacs in Ireland between 1980 and 1985. The analysis provides estimates of the time pattern and batch effects; we show how analytical complexity such as smoothly varying coefficients or random coefficient models can be accommodated by the model. Finally, we discuss other problems where the model is applicable. Copyright © 2004 John Wiley & Sons, Ltd.
Multiple comparisons with a control in families with both one‐sided and two‐sided hypotheses
Authors: Cheung Siu Hung , Kwong Koon Shing , Chan Wai Sum , Leung Shun Piu 
Published: August 2004

AbstractComparing several treatments with a control is a common objective of clinical studies. However, existing procedures mainly deal with particular families of inferences in which all hypotheses are either one‐ or two‐sided. In this article, we seek to develop a procedure which copes with a more general testing environment in which the family of inferences is composed of a mixture of one‐ and two‐sided hypotheses. The proposed procedure provides a more flexible and powerful tool than the existing method. The superiority of this method is also substantiated by a simulation study of average power. Selected critical values are tabulated for the implementation of the proposed procedure. Finally, we provide an illustrative example with sample data extracted from a medical experiment. Copyright © 2004 John Wiley & Sons, Ltd.
A cost‐related approach for evaluating drug development programs
Authors: Yan Eric C. , Chen Ling 
Published: August 2004

AbstractMost pharmaceutical statisticians make a contribution to drug development at the study level by designing and analysing clinical studies. There is less involvement at the drug project level, especially when it comes to making a strategic decision for a drug development program. Motivated by the assessment of drug safety, this paper presents a new decision rule on whether or not to continue developing a new drug based on a cost function. The cost function takes into account both type I and type II errors in a decision making problem. It also incorporates both the cost and benefit from a drug development program. This cost–benefit approach enables company senior management to evaluate a drug program objectively and make decisions in a quantitative and rational manner. The probabilities of making correct decisions are illustrated by using a real data example. Copyright © 2004 John Wiley & Sons, Ltd.
Improving Cox survival analysis with a neural‐Bayesian approach
Authors: Bakker Bart , Heskes Tom , Neijt Jan , Kappen Bert 
Published: August 2004

AbstractIn this article we show that traditional Cox survival analysis can be improved upon when supplemented with sensible priors and analysed within a neural Bayesian framework. We demonstrate that the Bayesian method gives more reliable predictions, in particular for relatively small data sets.The obtained posterior (the probability distribution of network parameters given the data) which in itself is intractable, can be made accessible by several approximations. We review approximations by Hybrid Markov Chain Monte Carlo sampling, a variational method and the Laplace approximation. We argue that although each Bayesian approach circumvents the shortcomings of the original Cox analysis, and therefore yields better predictive results, in practice the use of variational methods or Laplace is preferable. Since Cox survival analysis is infamous for its poor results with (too) many inputs, we use the Bayesian posterior to estimatep‐values on the inputs and to formulate an algorithm for backward elimination. We show that after removal of irrelevant inputs Bayesian methods still achieve significantly better results than classical Cox. Copyright © 2004 John Wiley & Sons, Ltd.
Correction. O. C. Ukoumunne, A comparison of confidence interval methods for the intraclass correlation coefficient in cluster randomized trials Statistics in Medicine 2002; 21: 3757–3774.
Authors: Ukoumunne Obioha C. 
Published: August 2004

AbstractThe original article to which this correction refers was published in Statistics in Medicine 2002; 21: 3757–3774.
Multiple imputation for body mass index: lessons from the Australian Longitudinal Study on Women's Health
Authors: Mishra Gita D. , Dobson Annette J. 
Published: August 2004

AbstractIn large epidemiological studies missing data can be a problem, especially if information is sought on a sensitive topic or when a composite measure is calculated from several variables each affected by missing values. Multiple imputation is the method of choice for ‘filling in’ missing data based on associations among variables. Using an example about body mass index from the Australian Longitudinal Study on Women's Health, we identify a subset of variables that are particularly useful for imputing values for the target variables. Then we illustrate two uses of multiple imputation. The first is to examine and correct for bias when data are not missing completely at random. The second is to impute missing values for an important covariate; in this case omission from the imputation process of variables to be used in the analysis may introduce bias. We conclude with several recommendations for handling issues of missing data. Copyright © 2004 John Wiley & Sons, Ltd.
Biomechanical efficiency is decreased in heart failure during low‐level steady state and maximal ramp exercise
Authors: Levy Wayne C. , Maichel Barbara A. , Steele Natalie P. , Leclerc Kenneth M. , Stratton John R. 
Published: August 2004

AbstractBackground:Previous studies of biomechanical efficiency (external work/energy input – Watt/O2 consumed) in heart failure (HF) using cardiopulmonary exercise testing (CPET) and magnetic resonance spectroscopy (MRS) have had discordant results with increased efficiency by CPET and decreased efficiency by MRS.Aims:Compare biomechanical efficiency of HF subjects and normal controls during steady state (SS=35 W for 6 min) and ramp cycle ergometer exercise. The hypothesis was that HF subjects would have impaired biomechanical efficiency that correlated with HF symptoms.Methods:Biomechanical efficiency used the actual Vo2 during exercise and recovery. Gross (Vo2 above zero), Net (Vo2 above the resting Vo2) and Work (Vo2 above the unloaded pedaling Vo2) efficiencies were calculated.Results:HF subjects had an 18% higher Vo2 during SS exercise (P=0.029). Biomechanical efficiency was reduced during SS exercise (gross −15%, P=0.019, net −15%, P=0.062, and work −35%, P=0.002). Gross Efficiency during SS exercise had the strongest correlation with HF symptoms (r=0.55). During ramp exercise gross (−26%), net (−10%) and work (−8%) biomechanical efficiency were all reduced (all P<0.05). The slope of the Vo2/Watt relationship during ramp exercise had the best correlation with HF symptoms (r=0.46).Conclusions:HF subjects have an increased O2 cost/Watt during SS and ramp exercise that correlates with HF symptoms of fatigue and breathlessness. Methods to improve biomechanical efficiency in HF subjects by exercise training or medications may improve the symptoms and the impaired exercise capacity associated with HF.