bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multilevel growth curve models with covariate effects: application to recovery after stroke
Authors: Tilling Kate , Sterne Jonathan A. C. , Wolfe Charles D. A. 
Published: January 2005

AbstractIn measuring the progression of, or recovery from, a disease an individual's outcome may be assessed on a number of occasions. A model of the relationship between outcome and time since disease occurred which accounts for patient characteristics could be used to describe patterns of recovery, to predict outcome for a patient, or to evaluate health interventions. We use multilevel models to analyse such data, focusing on the choice of powers of time both for mean outcome and covariate effects. We give equations for predicted outcome and corresponding standard errors (i) based only on baseline characteristics, and (ii) by conditioning on previous outcomes for an individual. In a study of 331 stroke patients, outcome was measured approximately 0, 2,4,6 and 12 months after stroke. Patient characteristics included age, sex, and pre‐stroke handicap, together with stroke‐severity indicators (presence of limb deficit, dysphasia, dysarthria or incontinence). Of these, only the effects of age, dysphasia and presence of deficit varied with time. Conditioning on previous observations improved the accuracy of predictions. The outcome variable clearly had a skewed distribution, and the model residuals showed evidence of non‐Normality. We discuss alternative models for non‐Normal data, and show that, here, the standard (Normal errors) multilevel model gives equivalent parameter estimates and predictions to those obtained from alternative models. Copyright © 2001 John Wiley & Sons, Ltd.
Bootstrap‐type confidence intervals for quantiles of the survival distribution
Authors: Hutson Alan D. 
Published: January 2005

AbstractIn this paper we outline and illustrate an easy to program method for analytically calculating both parametric and non‐parametric bootstrap‐type confidence intervals for quantiles of the survival distribution based on right censored data. This new approach allows for the incorporation of covariates within the framework of parametric models. The procedure is based upon the notion of fractional order statistics and is carried forth using a simple beta transformation of the estimated survival function (parametric or non‐parametric). It is the only direct method currently available in the sense that all other methods are based on inverting test statistics or employing confidence intervals for other survival quantities. We illustrate that the new method has favourable coverage probabilities for median confidence intervals as compared to six other competing methods. Copyright © 2001 John Wiley & Sons, Ltd.
Methods for testing equality of means of health care costs in a paired design study
Authors: Zhou Xiao‐Hua , Li Chunming , Gao Sujuan , Tierney William M. 
Published: January 2005

AbstractIn this paper we propose five new tests for the equality of paired means of health care costs. The first two tests are the parametric tests, a Z‐score test and a likelihood ratio test, both derived under the bivariate normality assumption for the log‐transformed costs. The third test (Z‐score with jack‐knife) is a semi‐parametric Z‐score method, which only requires marginal log‐normal assumptions. The last two tests are the non‐parametric bootstrap tests: one is based on a t‐test statistic, and the other is based on Johnson's modified t‐test statistic. We conduct a simulation study to compare the performance of these tests, along with some commonly used tests when the sample size is small to moderate. The simulation results demonstrate that the commonly used paired t‐test on the log‐scale and the Wilcoxon signed rank for differences of the two original scales can yield type I error rates larger than the preset nominal levels. The commonly used paired t‐test on the original data performs well with slightly skewed data, but can yield inaccurate results when two populations have different skewness. The likelihood ratio test, the parametric and semi‐parametric Z‐score tests all have very good type I error control with the likelihood ratio test being the best. However, the semi‐parametric Z‐score test requires less distributional assumptions than the two parametric tests. The percentile‐t bootstrap test and bootstrapped Johnson's modified t‐test have better type I error control than the paired t‐test on the original‐scale and Johnson's modified t‐test, respectively. Combining with the propensity‐score method, we can also apply the proposed methods to test the mean equality of two cost outcomes in the presence of confounders. Our two applications are from health services research. In the first one, we want to know the effect of Medicaid reimbursement policy change on outpatient health care costs. The second one is to evaluate the effect of a hospitalist programme on health care costs in an observational study, and the imbalanced covariates between intervention and control patients are taken into account using a propensity score approach. Copyright © 2001 John Wiley & Sons, Ltd.
Regression analysis of group testing samples
Authors: Xie Minge 
Published: January 2005

AbstractThis paper develops a general regression methodology that relates the group testing responses to individual covariate information. It can be used to study samples from a group testing procedure and to deal with a wide range of regression problems. A detailed illustration of the methodology is provided for a group testing procedure proposed by Gastwirth and Hammick. To demonstrate the utility of the method, simulation studies are performed on an HIV antibody testing data set published by Nusbacher et al. Copyright © 2001 John Wiley & Sons, Ltd.
Multinomial analysis of smoothed HIV back‐calculation models incorporating uncertainty in the AIDS incidence
Authors: Bellocco Rino , Pagano Marcello 
Published: January 2005

AbstractBack‐calculation models, developed to reconstruct the past trend of human immunodeficiency virus (HIV) and to project future acquired immunodeficiency syndrome incidence (AIDS), are usually and unrealistically based on the assumption that the observed AIDS counts are independently distributed according to a Poisson process. In contrast, we argue that a multinomial framework is more suitable to this situation, leading to a natural covariance structure. The ill‐conditioned nature of the problem is solved by modelling the HIV parameters according to a cubic spline function to reduce the dimensionality of the parameter space and obtain smoother parameter estimates. We applied a regression spline technique which yields to a computationally stable basis incorporating the incubation period in the new design matrix. We directly incorporate the reporting delay distribution in the AIDS incidence data, leading to a more complex formulation of the variance and covariance model that is adapted to the iteratively reweighted least square (IRLS) algorithm. In this case we obtain more accurate estimates of the standard error of the HIV incidence, especially in the most recent time. Our model, which uses a cubic spline reparameterization based on a multinomial probability distribution, is applied to the AIDS epidemic data in Italy. Copyright © John Wiley & Sons, Ltd.
Some issues in resolution of diagnostic tests using an imperfect gold standard
Authors: Hawkins Douglas M. , Garrett James A. , Stephenson Betty 
Published: January 2005

AbstractAs a subject's true disease status is seldom known with certainty, it is necessary to compare the performance of new diagnostic tests with those of a currently accepted but imperfect ‘gold standard’. Errors made by the gold standard mean that the sensitivity and specificity calculated for the new test are biased, and do not correctly estimate the new method's sensitivity and specificity. The traditional approach to this problem was ‘discrepant resolution’, in which the subjects for whom the two methods disagreed were subjected to a third ‘resolver’ test. Recent work has pointed out that this does not automatically solve the problem. A sounder approach goes beyond the discordant test results and tests at least some of the subjects with concordant results with the resolver also. This leaves some issues unresolved. One is the basic question of the direction of biases in various estimators. We point out that this question does not have a simple universal answer. Another issue, if one is to test a sample of the subjects with concordant results rather than all cases, is how to compute estimates and standard errors of the measures of test performance, notably sensitivity and specificity of the test method relative to the resolver. Expressions for these standard errors are given and illustrated with a numeric example. It is shown that using just a sample of subjects with concordant results may lead to great savings in assays. The design issue of how many concordant cells to test depends on the numbers of concordants and discordants. The formulae given show how to evaluate impact of different choices for these numbers and hence settle on a design that gives the required precision of estimates. Copyright © 2001 John Wiley & Sons, Ltd.
The use of mixture models for identifying high risks in disease mapping
Authors: Militino A. F. , Ugarte M. D. , Dean C. B. 
Published: January 2005

AbstractConventional approaches for estimating risks in disease mapping or mortality studies are based on Poisson inference. Frequently, overdispersion is present and this extra variability is modelled by introducing random effects. In this paper we compare two computationally simple approaches for incorporating random effects: one based on a non‐parametric mixture model assuming that the population arises from a discrete mixture of Poisson distributions, and the second using a Poisson‐normal mixture model which allows for spatial autocorrelation. The comparison is focused on how well each of these methods identify the regions which have high risks. Such identification is important because policy makers may wish to target regions associated with such extreme risks for financial assistance while epidemiologists may wish to target such regions for further study. The Poisson‐normal mixture model is presented from both a frequentist, or empirical Bayes, and a fully Bayesian point of view. We compare results obtained with the parametric and non‐parametric models specifically in terms of detecting extreme mortality risks, using infant mortality data of British Columbia, Canada, for the period 1981–1985, breast cancer data from Sardinia, for the period 1983–1987, and Scottish lip cancer data for 1975–1980. However, we also investigate the performance of these models in a simulation study. The key finding is that discrete mixture models seem to be able to locate regions which experience high risks; normal mixture models also work well in this regard, and perform substantially better when spatial autocorrelation is present. Copyright © 2001 John Wiley & Sons, Ltd.
Simultaneous modelling of operative mortality and long‐term survival after coronary artery bypass surgery
Authors: Ghahramani M. , Dean C. B. , Spinelli J. J. 
Published: January 2005

AbstractTypical analyses of lifetime data treat the time to death or failure as the response variable and use a variety of modelling strategies such as proportional hazards or fully parametric, to investigate the relationship between the response and covariates. In certain circumstances it may be more natural to view the distribution of the response variable as consisting of two or more parts since the survival curve appears segmented. This article addresses such a scenario and we propose a model for simultaneously investigating the effects of covariates over the two segments. The model is an analogue of that proposed by Lambert for zero‐inflated Poisson regression. The application is central to the model development and is concerned with survival after coronary artery bypass surgery. Here operative mortality, defined as death within 30 days after surgery, and long‐term mortality, are viewed as distinct outcomes. For the application considered, the survivor function displays much steeper descent during the first 30 days after surgery, that is, for operative mortality, than after this period. An investigation of the effects of covariates on operative and long‐term mortality after coronary artery bypass surgery illustrates the usefulness of the proposed model. Copyright © 2001 John Wiley & Sons, Ltd.
Using the overdispersed exponential family to estimate the distribution of usual daily intakes of people aged between 18 and 28 in Taiwan
Authors: Chang Hsing‐Yi , Suchindran Chirayath M. , Pan Wen‐Harn 
Published: January 2005

AbstractAssessment of usual dietary intake of a population is essential to understand the nutrition status of the population. Methods like 24‐hour dietary recall and food records are commonly used for this task. However, within‐individual variation exists in the food record data. Methods considering measurement errors have been used to evaluate the association between nutrient intakes and diseases. Few have been devoted to estimating the distribution of usual daily intakes. This paper proposes applying the formulation of the overdispersed exponential family to estimate the distribution of usual nutrient intake and applies the adjustments developed by Liu to reduce the variance of the distribution. The proposed method has the advantages of working on the original scale of data and implementation convenience. The adjustment of the variance is carried out by dividing the variance into within‐individual variance and among‐individual variance. The adjusted variance, then, is used to estimate the distribution of the usual daily intake. Sampling weights are considered in all the steps, except the estimation of the ratio of within to among variance. The data for this study are from the Nutritional and Health Survey in Taiwan conducted between 1993 and 1996 (NAHSIT, 1993–1996). An independent external set of data for people aged between 18 and 28 years (49 males and 20 females) is used to estimate the ratio of within to among individual variance. Due to the availability of data in estimating the ratio of within to among individual variance, 430 males and 422 females in the NAHSIT sample are used to estimate the distribution of usual daily intakes for people aged between 18 and 28 in Taiwan. The distribution also allows us to estimate the proportion of people who do not meet the recommended daily nutrient allowance (RDNA). The results show that the intakes of calcium and vitamin E of this group of people fall far below RDNA. Copyright © 2001 John Wiley Sons, Ltd.
Estimating HIV incidence rates from age prevalence data in epidemic situations
Authors: Williams Brian , Gouws Eleanor , Wilkinson David , Karim Salim Abdool 
Published: January 2005

AbstractWe present a method of estimating HIV incidence rates in epidemic situations from data on age‐specific prevalence and changes in the overall prevalence over time. The method is applied to women attending antenatal clinics in Hlabisa, a rural district of KwaZulu/Natal, South Africa, where transmission of HIV is overwhelmingly through heterosexual contact. A model which gives age‐specific prevalence rates in the presence of a progressing epidemic is fitted to prevalence data for 1998 using maximum likelihood methods and used to derive the age‐specific incidence. Error estimates are obtained using a Monte Carlo procedure. Although the method is quite general some simplifying assumptions are made concerning the form of the risk function and sensitivity analyses are performed to explore the importance of these assumptions. The analysis shows that in 1998 the annual incidence of infection per susceptible woman increased from 5.4 per cent (3.3–8.5 per cent; here and elsewhere ranges give 95 per cent confidence limits) at age 15 years to 24.5 per cent (20.6–29.1 per cent) at age 22 years and declined to 1.3 per cent (0.5–2.9 per cent) at age 50 years; standardized to a uniform age distribution, the overall incidence per susceptible woman aged 15 to 59 was 11.4 per cent (10.0–13.1 per cent); per women in the population it was 8.4 per cent (7.3–9.5 per cent). Standardized to the age distribution of the female population the average incidence per woman was 9.6 per cent (8.4–11.0 per cent); standardized to the age distribution of women attending antenatal clinics, it was 11.3 per cent (9.8–13.3 per cent). The estimated incidence depends on the values used for the epidemic growth rate and the AIDS related mortality. To ensure that, for this population, errors in these two parameters change the age specific estimates of the annual incidence by less than the standard deviation of the estimates of the age specific incidence, the AIDS related mortality should be known to within ±50 per cent and the epidemic growth rate to within ±25 per cent, both of which conditions are met. In the absence of cohort studies to measure the incidence of HIV infection directly, useful estimates of the age‐specific incidence can be obtained from cross‐sectional, age‐specific prevalence data and repeat cross‐sectional data on the overall prevalence of HIV infection. Several assumptions were made because of the lack of data but sensitivity analyses show that they are unlikely to affect the overall estimates significantly. These estimates are important in assessing the magnitude of the public health problem, for designing vaccine trials and for evaluating the impact of interventions. Copyright © 2001 John Wiley & Sons, Ltd.
An improved method of evaluating drug effect in a multiple dose clinical trial
Authors: Shen Liji 
Published: January 2005

AbstractIn drug development, finding an optimal dose is normally carried out in a phase II trial. A phase III trial will then be conducted to demonstrate that the selected dose is efficacious and safe. As choosing a dose from the phase II trial which has the highest observed response rate could overestimate the true response rate of the selected dose, the data from the phase II study cannot be simply pooled with the data from the phase III study in a final analysis. Therefore, a solution to the overestimation problem needs to be found so that the information obtained from phase II dose finding clinical trials can appropriately be combined with the data in the phase III study. In this paper, the potential overestimation in a multiple dose clinical trial is assessed and a method for correcting this bias is proposed. Simulations show that stepwise over‐correction, the proposed method, is better than methods such as Bonferroni's procedure. Copyright © 2001 John Wiley & Sons, Ltd.
Sample size calculations for clustered binary data
Authors: Jung Sin‐Ho , Kang Seung‐Ho , Ahn Chul 
Published: January 2005

AbstractIn this paper we propose a sample size calculation method for testing on a binomial proportion when binary observations are dependent within clusters. In estimating the binomial proportion in clustered binary data, two weighting systems have been popular: equal weights to clusters and equal weights to units within clusters. When the number of units varies cluster by cluster, performance of these two weighting systems depends on the extent of correlation among units within each cluster. In addition to them, we will also use an optimal weighting method that minimizes the variance of the estimator. A sample size formula is derived for each of the estimators with different weighting schemes. We apply these methods to the sample size calculation for the sensitivity of a periodontal diagnostic test. Simulation studies are conducted to evaluate a finite sample performance of the three estimators. We also assess the influence of misspecified input parameter values on the calculated sample size. The optimal estimator requires equal or smaller sample sizes and is more robust to the misspecification of an input parameter than those assigning equal weights to units or clusters. Copyright © 2001 John Wiley & Sons, Ltd.
Phase specific analysis of herpes zoster associated pain data: a new statistical approach
Authors: Arani Ramin B. , Soong Seng‐Jaw , Weiss Heidi L. , Wood Martin J. , Fiddian Paul A. , Gnann John W. , Whitley Richard 
Published: January 2005

AbstractHerpes zoster or shingles is a frequent occurrence in both elderly individuals and immunocompromised hosts. The pain associated with herpes zoster is the most debilitating complication of the disease. It can be described as acute pain and post‐herpetic neuralgia or zoster associated pain (ZAP). The latter definition encompasses pain from the onset of disease through its resolution and provides a convenient analytic tool for evaluation of antiviral therapy. A heuristic examination of ZAP historical data suggests the existence of three phases of pain resolution: the acute, subacute and chronic phases. The subacute and chronic phases comprise the post‐herpetic neuralgia (PHN) stage. Common analytic methods, such as a Kaplan–Meier survival function or a Cox's model, have been used to assess the pain. However, such approaches do not adequately allow for phase comparison. Notably, in the clinical trial setting the comparison of specific treatment effects on the latter stages of pain are of the greatest medical relevance since this is the most debilitating phase of the illness. In order to incorporate the phase‐specific information in the modelling of time to cessation of ZAP, we assumed the hazard function was a stepwise constant. Utilizing the full likelihood function, we obtained the maximum likelihood estimate for the transition times (that is, change‐points), and other parameters of medical importance. The standard error of the change‐point estimates were obtained through a bootstrapping method. The asymptotic properties of the parameter estimates are also discussed. Hence, the rates of pain resolution across all phases can be examined in order to precisely define the existence of multiple phases. In addition, the covariates effect can be examined across phases and populations, thereby allowing us to translate potential efficacy of a standard therapy to different populations. These results can be utilized in the design of clinical trials or in targeting the outcome for a specific phase while controlling for the effect of other variables. Copyright © 2001 John Wiley & Sons, Ltd.
Improving the fit of bivariate smoothing splines when estimating longitudinal immunological and virological markers in HIV patients with individual antiretroviral treatment strategies
Authors: Brown Elizabeth R. , MaWhinney Samantha , Jones Richard H. , Kafadar Karen , Young Benjamin 
Published: January 2005

AbstractCD4+ lymphocyte count and HIV RNA plasma viral load are longitudinally monitored in patients with HIV infection. Because data collection intervals may be unequally spaced and these markers experience high within‐patient variability, they may be smoothed before use in subsequent models. Estimation strategies must be able to accommodate the drastic changes in viral load which may occur when an individual's treatment strategy is updated. Because these treatment changes are not regimented, these dynamics cannot be modelled using standard methods. We propose univariate and bivariate cubic smoothing splines to fit CD4+ count and viral load over time. The method is developed using state space equations, and the Kalman filter is used to calculate the log‐likelihood. Non‐linear optimization is used to obtain the maximum likelihood estimates. A modification of the Kalman filter allows non‐informative or diffuse priors at the initial observation. Since treatment changes are expected to alter the shape of the curve, we further extend the Kalman filter to permit greater flexibility in the smoothing spline at these time points. The method produces smoothed estimates of the viral load and CD4+ count curves over time. Copyright © 2001 John Wiley & Sons, Ltd.