bluuebunny/crossref_metadata_2025_split huggingface dataset:
Parametric modelling of neonatal mortality in relation to size at birth
Authors: Cheung Yin Bun , Yip Paul S. F. , Karlberg Johan P. E. 
Published: January 2005

AbstractWe use a richly parameterized model to analyse the effects of shortness and thinness at birth on neonatal mortality in Swedish live‐born singletons. The model captures the hazard of neonatal mortality according to the function α×exp(−γ×t)+δ, where t represents age. Covariates are allowed to simultaneously influence the initial excess hazard, the rate of decay, and the long‐term hazard. Among term newborns, birth length for gestational age had a stronger effect on the long‐term hazard and a weaker effect on the initial excess hazard than the Ponderal index. The initial excess hazard associated with a low Ponderal index tended to decay quickly. Among preterm newborns, a higher birth length for gestational age was associated with lower initial and long‐term hazards, and with a faster rate of decay of the initial excess hazard. In contrast, the Ponderal index was not associated with the long‐term hazard. We discuss the interpretability of the model and its potential use in neonatology and medical demography. We also compare the model to a Cox model with time‐dependent covariates. Copyright © 2001 John Wiley & Sons, Ltd.
Effects of covariance model assumptions on hypothesis tests for repeated measurements: analysis of ovarian hormone data and pituitary‐pteryomaxillary distance data
Authors: Park Taesung , Park Jin‐Kyung , Davis Charles S. 
Published: January 2005

AbstractIn the analysis of repeated measurements, multivariate methods which account for the correlations among the observations from the same experimental unit are widely used. Two commonly‐used multivariate methods are the unstructured multivariate approach and the mixed model approach. The unstructured multivariate approach uses MANOVA types of models and does not require assumptions on the covariance structure. The mixed model approach uses multivariate linear models with random effects and requires covariance structure assumptions. In this paper, we describe the characteristics of tests based on these two methods of analysis and investigate the performance of these tests. We focus particularly on tests for group effects and parallelism of response profiles. Copyright © 2001 John Wiley & Sons, Ltd.
Optimal design for linear interpolation of curves
Authors: Wang Jixian 
Published: January 2005

AbstractNon‐parametric procedures are often used for the analysis of pharmacokinetic trials. Fewer design procedures are available for non‐parametric estimation than for parametric estimation. Linear interpolation is widely used for curve estimation in pharmacokinetic trials, where often only sparse sampling is feasible. Current design procedures for smoothing or local fit are not suitable as they are based on asymptotic properties and the bias of the estimate is ignored. This paper proposes optimal designs that minimize the mean squared error of linear interpolation. Optimal designs for three situations are considered. The first situation is single curve estimation based on an ordinary non‐linear model. The second is estimating several curves in a non‐linear mixed model setting using an average mean squared error as the design criterion. The third situation is destructive sampling where estimating the average curve is the main purpose. In the first situation, the design results in the best linear interpolation when the variance is constant. For the destructive sampling design, an algorithm based on approximations is proposed. This algorithm can be programmed in a common statistical package. Numerical examples are used to illustrate the design procedure. Copyright © 2001 John Wiley & Sons, Ltd.
Estimation for paired binomial data with application to radiation therapy
Authors: Taylor Jeremy M. G. , Weiss Robert E. , Li Wenzhi , Hsu Chiu‐Hsieh , Suwinski Rafal 
Published: January 2005

AbstractWe compare and contrast several different methods for estimating the effect of treatment when responses are paired binomial observations. The ratio of binomial probabilities is the parameter of interest, while the binomial probabilities are nuisance parameters which may vary between pairs. The application is a meta‐analysis of the treatment of rectal cancer, with observations in each study indicating the number of recurrences of the cancer in each of two groups, one with radiation therapy and one without. The ratio of the probabilities of recurrence in the radiation to non‐radiation groups is of substantive interest, and is modelled as a logistic or complementary log‐log function of an unknown linear combination of the covariates. The three methods we consider are maximum likelihood, a Bayesian approach and an approach based on estimating equations. For the MLE and Bayesian approach the potentially large number of nuisance parameters are estimated together with the parameters of interest, whereas for the estimating equation approach only the parameters of interest are estimated. A simulation study is performed to compare the methods and evaluate the impact of overdispersion. Copyright 2001 John Wiley & Sons, Ltd.
Data augmentation priors for Bayesian and semi‐Bayes analyses of conditional‐logistic and proportional‐hazards regression
Authors: Greenland Sander , Christensen Ronald 
Published: January 2005

AbstractData augmentation priors have a long history in Bayesian data analysis. Formulae for such priors have been derived for generalized linear models, but their accuracy depends on two approximation steps. This note presents a method for using offsets as well as scaling factors to improve the accuracy of the approximations in logistic regression. This method produces an exceptionally simple form of data augmentation that allows it to be used with any standard package for conditional‐logistic or proportional‐hazards regression to perform Bayesian and semi‐Bayes analyses of matched and survival data. The method is illustrated with an analysis of a matched case‐control study of diet and breast cancer. Copyright © 2001 John Wiley & Sons, Ltd.
An evaluation of phase I clinical trial designs in the continuous dose–response setting
Authors: Storer Barry E. 
Published: January 2005

AbstractBoth traditional phase I designs and the increasingly popular continual reassessment method (CRM) designs select an estimate of maximum tolerable dose (MTD) from among a set of prespecified dose levels. Although CRM designs use an implied dose–response model to select the next dose level, in general it is neither assumed nor necessary that this model be tied to the actual dose of a drug. In contrast, in our two‐stage design the fitting of a dose–response model after data have been collected is a necessary feature of the design, and the MTD is not constrained to be one of the prespecified dose levels. We conducted a simulation study to evaluate the performance of the two‐stage design, two likelihood‐based CRM designs, and two traditional designs in estimating the MTD in situations where one assumes that an explicit dose–response model exists. Under a wide variety of dose–response settings, we examined the bias and precision of estimates, and the fraction of estimates that were extremely high or low. We also studied the effect of adding a model fitting step at the end of a traditional design or a CRM design. The best performance was achieved using the two‐stage and CRM designs. Although the CRM designs generally had smaller bias, the two‐stage design yielded equal or somewhat smaller precision in some cases. The addition of a model‐fitting step slightly improved the precision of the CRM estimates and decreased the percentage of extreme estimates. Allowing interpolation between doses for updating during CRM did not improve overall performance. Copyright © 2001 John Wiley & Sons, Ltd.
Analysis of recurrent failure times data: should the baseline hazard be stratified
Authors: Mahé Cédric , Chevret Sylvie 
Published: January 2005

AbstractOver the past two decades, a variety of fruitful statistical methods for the analysis of recurrent events has been proposed for the estimation of covariates effect using the Cox proportional hazard model. Besides frailty modelling, two simple trends of modelling have been developed: the first one uses stratification on the rank of the event, whereas the second one, more closely related to Poisson processes theory, does not use stratification. Although they both take into account the correlation of the unit failure times, each of these approaches emphasizes a different aspect of the underlying point process and there is still an ongoing debate concerning the most appropriate method. The aim of this paper is to stress current interests and trends concerning these two approaches. For each model, main statistical methods for estimating the covariates effects are presented. Methods are illustrated and compared in two randomized clinical trials which involve recurrences of severe adverse events following chemotherapy in 938 patients with chronic lymphocytic leukaemia, and recurrences of infectious rhinitis episodes in 327 patients. The discussion, based on the previous examples and on the properties of underlying statistical inference, deals with the appropriateness of the model choice, which is closely related to the data structure. Copyright © 2001 John Wiley & Sons, Ltd.
A general goodness‐of‐fit approach for inference procedures concerning the kappa statistic
Authors: Altaye Mekibib , Donner Allan , Eliasziw Michael 
Published: January 2005

AbstractThe kappa statistic is frequently used as a measure of agreement among two or more raters. Although considerable research on statistical inferences for this statistic has been published for the case of two raters and a binary outcome, relatively little work has appeared on inference problems for the case of multiple raters and/or polytomous nominal outcome categories. In this paper we propose a new procedure for constructing inferences for the kappa statistic that may be applied to this general case. The procedure is based on a chi‐square goodness‐of‐fit test as applied to the Dirichlet multinomial model, and is a natural extension of previously proposed procedures that apply to more restricted cases. A simulation study shows that the new procedure provides confidence interval coverage levels and type I error rates close to nominal over a wide range of parameter combinations. We also present a sample size formula which may be used to determine the required number of subjects and raters for a given number of outcome categories. Copyright © 2001 John Wiley & Sons, Ltd.
Modelling variation of lower leg length growth in early life
Authors: Fong D. Y. T. , Wong S. W. S. , Karlberg J. 
Published: January 2005

AbstractWe consider the estimation of sources of variation for panel data with repeated measurements. With no repeated measurements and known measurement error, models for variation decomposition have been proposed when there are one or more types of measurements. Estimation was performed using the EM algorithm accompanied by model augmentation that demands more computational efforts. In this article we extend previous variation models and modify the estimation methods in order to estimate various variation components after eliminating the unknown effects of measurement error. Specifically, methods that dispense with model augmentation and estimation of time‐dependent covariates are considered. A set of lower leg length data from Chinese infants is analysed by using the proposed model. Interestingly, our results are consistent with the well‐accepted three‐phase (infancy–childhood–puberty) growth transition proposition for human growth. Moreover, gender effect is found to be time‐varying. Copyright © 2001 John Wiley & Sons, Ltd.
Evaluation of surrogate endpoints in randomized experiments with mixed discrete and continuous outcomes
Authors: Molenberghs Geert , Geys Helena , Buyse Marc 
Published: January 2005

AbstractA statistical definition of surrogate endpoints as well as validation criteria was first presented by Prentice. Freedman et al. supplemented these criteria with the so‐called proportion explained. Buyse and Molenberghs pointed to inadequacies of these criteria and suggested a new definition of surrogacy based on (i) the relative effect linking the overall effect of treatment on both endpoints and (ii) an individual‐level measure of agreement between both endpoints. Using data from a randomized trial, they showed how a potential surrogate endpoint can be studied using a joint model for the surrogate and the true endpoint. Whereas Buyse and Molenberghs restricted themselves to the fairly simple cases of jointly normal and jointly binary outcomes, we treat the situation where the surrogate is binary and the true endpoint is continuous, or vice versa. In addition, we consider the case of ordinal endpoints. Further, Buyse et al. extended the approach of Buyse and Molenberghs to a meta‐analytic context. We will adopt a similar approach for responses of a mixed data type. Copyright © 2001 John Wiley & Sons, Ltd.
Synthetic estimators with moderating influence: the carry‐over in cross‐over trials revisited
Authors: Longford Nicholas T. 
Published: January 2005

AbstractA general approach to combining alternative estimators of a parameter is described and applied to the analysis of 2×2 cross‐over trials in which the treatment effect is of principal interest and carry‐over is a nuisance feature. The approach requires prior information about the carry‐over. Although modest information is sufficient, more information is rewarded by greater efficiency. The combination, or synthetic, estimator exhibits a moderating influence: its mean squared error is a flat function of the carry‐over effect in the range of its feasible values, and for the largest feasible carry‐over effect it is superior to either of the originally considered estimators. An extension of the approach is outlined and two other applications in medical research discussed. Although the method can be regarded as Bayesian in spirit, we focus on its resampling (frequentist) properties. Copyright © 2001 John Wiley & Sons, Ltd.
Bayesian analysis of logistic regression with an unknown change point and covariate measurement error
Authors: Gössl Christoff , Küchenhoff Helmut 
Published: January 2005

AbstractWe discuss Bayesian estimation of a logistic regression model with an unknown threshold limiting value (TLV). In these models it is assumed that there is no effect of a covariate on the response under a certain unknown TLV. The estimation of these models in a Bayesian context by Markov chain Monte Carlo (MCMC) methods is considered with focus on the TLV. We extend the model by accounting for measurement error in the covariate. The Bayesian solution is compared with the likelihood solution proposed by Küchenhoff and Carroll using a data set concerning the relationship between dust concentration in the working place and the occurrence of chronic bronchitis. Copyright © 2001 John Wiley & Sons, Ltd.
Assessing physicians' accuracy in diagnosing paediatric patients with acute abdominal pain: measuring accuracy for multiple diseases
Authors: Obuchowski Nancy A. , Goske Marilyn J. , Applegate Kimberly E. 
Published: January 2005

AbstractIn evaluating the accuracy of diagnostic tests there are many situations where the true disease status can be one of multiple disorders. For example, when paediatric patients suffer acute abdominal pain, the underlying diagnosis could be appendicitis, intestinal obstruction, gastroenteritis, urinary tract infection, etc. In this paper we describe a format for collecting data for the case of multiple truth states. This new format allows one to make pairwise comparisons of the diagnostic accuracy between all of the different truth states. We propose a summary measure of accuracy which is a weighted average of the pairwise estimates of accuracy. Estimators are derived for the variance and covariance of the estimated summary accuracy. The small sample properties of the estimators are evaluated in a Monte Carlo simulation study. The new data collection format and summary measure were used in a paediatric acute abdominal pain study; data from this study are used to illustrate the methods. Copyright © 2001 John Wiley & Sons, Ltd.
A semi‐parametric regression analysis of CD4 cell counts
Authors: Sun Jianguo 
Published: January 2005

AbstractThis paper considers the regression analysis of CD4 cell counts, a commonly used indicator and prognostic factor of AIDS progression. For this purpose, a number of methods have been proposed and most of them are based on random effects models. We present an alternative that is based on a mean function regression model. The new method is more natural and particularly appropriate if population parameters such as treatment comparison are mainly of interest. To estimate regression parameters, we present an estimating equation‐based approach, which does not require estimation of the baseline mean function. This makes the implementation of the approach and the study of the properties of the estimated regression parameters much easier than those based on random effects models. The method is illustrated with a set of CD4 cell count data arising from an AIDS clinical trial and simulated data. Copyright © 2001 John Wiley & Sons, Ltd.
Allocation of subjects to test null relative risks smaller than one
Authors: Hoover Donald R. , Blackwelder William C. 
Published: January 2005

AbstractAllocating a proportion k′=1/(1+√r0) of subjects to an intervention is a practical approach to approximately maximize power for testing whether an intervention reduces relative risk of disease below a null ratio r0<1. Furthermore, allocating k′s, a convenient fraction close to k′, to intervention performs nearly as well; for example, allocating k′s=3/5 for 0.5⩾r0>0.33,2/3 for 0.33⩾r0>0.17 and 3/4 for 0.17⩾r0⩾0.10. Both k′ and k′s are easily calculated and invariant to alterations in disease rate estimates under null and alternative hypotheses, when r0 remains constant. In examples that we studied, allocating k′ (or k′s) subjects to intervention achieved close to the minimum possible sample size, given test size and power (equivalently, maximum power, given test size and sample size), for likelihood score tests. Compared to equal allocation, k′ and k′s reduced sample sizes by amounts ranging from ∼5.5 per cent for r0=0.50 to ∼24 per cent for r0=0.10. These sample size savings may be particularly important for large studies of prophylactic interventions such as vaccines. While k′ was derived from variance minimization for an arcsine transformation, we do not recommend the arcsine test, since its true size exceeded the nominal value. In contrast, the true size for the uncorrected score test was less than the nominal size. A skewness correction made the size of the score test very close to the nominal level and slightly increased power. We recommend using the score test, or the skewness‐corrected score test, for planing studies designed to show a ratio of proportions is less than a prespecified null ratio r0<1. Copyright © 2001 John Wiley & Sons, Ltd.
Marginal and dynamic regression models for longitudinal data
Authors: Schmid Christopher H. 
Published: January 2005

AbstractRegression models for longitudinal data often employ random effects and serial correlation to account for residual variation between and within subjects. Most of these models are marginal models, separating the mean and covariance parameters. This paper discusses the use of dynamic models in which a lagged response serves as a predictor and compares these to marginal models. Regression parameters have a different interpretation in dynamic models as they describe changes in response levels, rather than the levels themselves. Lagged predictors are also useful with longitudinal data, explicitly quantifying the effect of previous levels of risk factors. These models are explored through analysis of data from the Childhood Respiratory Study, modelling lung function (FEV1) levels as a function of age, height, sex and smoking status in children measured over a five‐year period. Copyright © 2001 John Wiley & Sons, Ltd.
Within‐subject exposure dependency in case‐crossover studies
Authors: Vines S. K. , Farrington C. P. 
Published: January 2005

AbstractIn the case‐crossover design, only cases are sampled and the hazard ratio is estimated from within‐subject comparisons of exposures at the event time and in M control periods prior to the event. We consider the effect of within‐subject dependence of exposures in successive time periods. We show that estimates obtained from the conditional logistic model are biased. This bias disappears if the distribution of exposures in the M+1 successive time intervals is exchangeable. In contrast, the Mantel–Haenszel estimator for the odds ratio is approximately unbiased provided that exposures are stationary. Suitable methods of analysis of case‐crossover designs using maximum likelihood may be derived from cohort rather than case‐control models. Copyright © 2001 John Wiley & Sons, Ltd.
Dynamic linear model and SARIMA: a comparison of their forecasting performance in epidemiology
Authors: Nobre Flávio Fonseca , Monteiro Ana Beatriz Soares , Telles Paulo Roberto , Williamson G. David 
Published: January 2005

AbstractOne goal of a public health surveillance system is to provide a reliable forecast of epidemiological time series. This paper describes a study that used data collected through a national public health surveillance system in the United States to evaluate and compare the performances of a seasonal autoregressive integrated moving average (SARIMA) and a dynamic linear model (DLM) for estimating case occurrence of two notifiable diseases. The comparison uses reported cases of malaria and hepatitis A from January 1980 to June 1995 for the United States. The residuals for both predictor models show that they were adequate tools for use in epidemiological surveillance. Qualitative aspects were considered for both models to improve the comparison of their usefulness in public health. Our comparison found that the two forecasting modelling techniques (SARIMA and DLM) are comparable when long historical data are available (at least 52 reporting periods). However, the DLM approach has some advantages, such as being more easily applied to different types of time series and not requiring a new cycle of identification and modelling when new data become available. Copyright © 2001 John Wiley & Sons, Ltd.
A statistical model for the evaluation of barrier contraceptive efficacy
Authors: Dominik Rosalie , Zhou Haibo , Cai Jianwen 
Published: January 2005

AbstractThis paper describes an approach for the analysis of barrier contraceptive efficacy trials that accounts for timing frequency of intercourse and compliance. We allow exposure variables to vary for each act of intercourse and we control for timing of each act through a specific parametric function of the day of the act relative to the last day of the follicular phase of the cycle. The model can be used to examine the level of protection provided by a barrier versus no contraceptive method even when no control group of non‐users is studied, as long as there are acts with no barrier use during the fertile window. We present results of a simulation study which examines performance of estimators and power under a variety of scenarios, including situations where an accurate benchmark for ovulation day is not available. As compared to the survival analysis approach commonly used in this setting, simulation results show that the new approach yields considerable gains in power to detect differences between the efficacy of contraceptive methods. An application to data from the FemCap® versus diaphragm trial show results consistent with previous findings suggesting superiority of the diaphragm but also provides new evidence of the per act protection provided by both methods. Copyright © 2001 John Wiley & Sons, Ltd.