bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysing the relationship between change in a risk factor and risk of disease
Authors: Cain Kevin C. , Kronmal Richard A. , Kosinski Andrzej S. 
Published: April 2007

AbstractThere are numerous examples in the epidemiologic literature of analyses that relate the change in a risk factor, such as serum cholesterol, to the risk of an adverse outcome, such as heart disease. Many of these analyses fit some type of regression model (such as logistic regression or the Cox model for survival time data) that includes both the change in the risk factor and the baseline value as covariates. We show that this method of adjusting for the baseline level can produce misleading results. The problem occurs when the true value of the risk factor relates to the outcome, and the measured value differs from the true value due to measurement error. We may find the observed change in the risk factor significantly related to the outcome when there is in fact no relationship between the true change and the outcome. If the question of interest is whether a person who lowers his level of the risk factor by means of drugs or lifestyle changes will thereby reduce his risk of disease, then we should consider an association due solely to measurement error as spurious. We present a method that adjusts for the measurement error in a linear regression analysis and show that an analogous adjustment applies asymptotically to logistic regression. As in other errors‐in‐variables problems, this analysis depends on knowledge of the relative variances of the random variation, the true baseline value, and the true change. Since the magnitudes of these variances are usually unknown and sometimes unknowable (the distinction between true change and measurement error being ambiguous), we recommend a sensitivity analysis that examines how the analysis results depend on the assumptions concerning the variances. The commonly used analysis method corresponds to the extreme case in which there is no measurement error. We use data from the Framingham Study and simulations to illustrate these points.
Estimating the stage‐specific numbers of HIV infection using a markov model and back‐calculation
Authors: Longini Ira M. , Byers Robert H. , Hessol Nancy A. , Tan Wai Y. 
Published: April 2007

AbstractThe back‐calculation method has been used to estimate the number of HIV infections from AIDS incidence data in a particular population. We present an extension of back calculation that provides estimates of the numbers of HIV infectives in different stages of infection. We model the staging process with a timedependent Markov process that partitions the HIV infectious period into the following progressive stages and/or substages: stage 1, infected but antibody negative; substages 2–3; antibody positive but asymptomatic; substages 4–6, pre‐AIDS symptoms and/or abnormal haematologic indicator; stage 7, clinical AIDS. We also model an eighth stage, deceased due to AIDS. The model allows for time‐dependent treatment effects that slow the rate of progression in substages 4–7. We use the estimated AIDS incubation period distribution from the Markov model in back calculation from AIDS incidence data to estimate the total number of HIV infections and the parameters of the infection probability distribution. We then use these estimates in the Markov model to estimate the stage‐specific numbers of HIV infections over the course of the epidemic in the population under study. Example calculations employ data for the HIV epidemic in San Francisco City Clinic Cohort.
Estimation of direct formulation effect under log‐normal distribution in bioavailability/bioequivalence studies
Authors: Liu Jen‐Pei , Weng Chung‐Sing 
Published: April 2007

AbstractThis paper considers estimation of a measure of relative bioavailability of a test formulation to a reference formulation, on the original scale, under the assumption of a log‐normal distribution for the data from a 2 × 2 crossover bioavailability/bioequivalence study. We propose the minimum variance unbiased estimator which is equal to the maximum likelihood estimator adjusted for bias by a correction factor. We also derive the variance of the minimum variance unbiased estimator and its unbiased estimator. We derive the biases and mean square errors of the maximum likelihood estimator, the ratio of the least squares means, and the least squares mean of individual subject ratios, with respect to this measure, and compare them in a simulation study. Both theoretical and empirical results strongly suggest that one always consider the minimum variance unbiased estimator. A numerical example illustrates the proposed estimation procedure.
On the computation of likelihood ratio and score test based confidence intervals in generalized linear models
Authors: Alho Juha M. 
Published: April 2007

AbstractNumerical procedures for calculating likelihood ratio test and score test based confidence intervals in generalized linear models are considered. Newton's method appears to have better convergence properties than the secant method in the likelihood ratio test case. However, the secant method may be easier to program for models with link functions that are not natural. Similarly, the secant method is easier to implement for the computation of score test based intervals. The practical implementation of the procedures in GLIM is illustrated.
Empirical bayes versus fully bayesian analysis of geographical variation in disease risk
Authors: Bernardinelli Luisa , Montomoli Cristina 
Published: April 2007

AbstractThis paper reviews methods for mapping geographical variation in disease incidence and mortality. Recent results in Bayesian hierarchical modelling of relative risk are discussed. Two approaches to relative risk estimation, along with the related computational procedures, are described and compared. The first is an empirical Bayes approach that uses a technique of penalized log‐likelihood maximization; the second approach is fully Bayesian, and uses an innovative stochastic simulation technique called the Gibbs sampler. We chose to map geographical variation in breast cancer and Hodgkin's disease mortality as observed in all the health care districts of Sardinia, to illustrate relevant problems, methods and techniques.
Analysis strategies for serial multivariate ultrasonographic data that are incomplete
Authors: Espeland Mark A. , Byington Robert P. , Hire Donald , Davis Vicki G. , Hartwell Tyler , Probstfield Jeffrey 
Published: April 2007

AbstractUltrasonographic measurement of intima‐media thicknesses in the carotid artery has emerged as an important non‐invasive means of assessing atherosclerosis, and has served to define primary outcome measures related to progression of arterial lesions in several large clinical trials and epidemiologic studies. It is characteristic that measurements often cannot be obtained from all sites during repeated examinations. This leads to incomplete multivariate serial data, for which the set and number of visualized sites may vary across time. We have contrasted several conditional and unconditional maximum likelihood analytical approaches, and have evaluated these with a simulation experiment based on characteristics of ultrasound measurements collected during the course of the Asymptomatic Carotid Artery Plaque Study. We examined analyses based on unweighted and generalized least squares regression in which we estimated cross‐sectional summary statistics using raw means, unconditional maximum likelihood estimates and full maximum likelihood estimates. Since the genesis of missing data is not fully clear, and since the approaches we examined are based, to some degree, on the assumption that data are missing at random, we also examined the relative impact of deviations from such an assumption on each of the approaches considered. We found that maximum likelihood based approaches increased the expected efficiency of the analysis of serial ultrasound data over ignoring missing data by up to 21 per cent.
Organization and analysis of safety data using a multivariate approach
Authors: Chuang‐Stein Christy , Mohberg Noel R. , Musselman David M. 
Published: April 2007

AbstractThe collection of safety data is an important part of clinical trials. These safety data are often described and reported in great detail with expenditure of substantial effort and energy. Because of the wide variety of data that require scrutiny from the safety perspective, however, statistical comparisons of the safety profiles of different treatments often lack focus and structure and result in situations where the comparisons for each individual item lack power and thus are inconclusive. In this paper, we propose to organize the safety data into a more manageable form by consolidating them into a number ofKclasses characterized by body systems and determined in conjunction with the underlying disease as well as the treatments involved. Within each class, we propose assignment to each patient of an overall intensity grade based on all relevant information. The consolidation of the safety data as proposed provides an informative summary for the safety profile of each treatment. The analysis of such organized data concentrates on comparison of the mean intensity grades for different treatments within theKclasses simultaneously with use of scores that reflect the acceptability of the various intensity levels to an individual. Furthermore, we demonstrate that the proposed multivariate comparison has much higher power than the univariate one to detect differences in certain cases. We provide examples to illustrate the proposed procedure.
Sixteen S‐squared over D‐squared: A relation for crude sample size estimates
Authors: Lehr Robert 
Published: April 2007

AbstractI suggest for memorization an equation for calculating approximate sample size requirements intended only for a specific set of values (80 per cent power for a two‐tailed alpha = 0.05 test) which seems to occur often in biopharmaceutical research. After presenting the formula in terms of variance estimate s2 and effect size d, I derive a few alternative forms and then discuss the accuracy of the approximation and other properties as well as examples of its use.
Sample sizes for proportional hazards survival studies with arbitrary patient entry and loss to follow‐up distributions
Authors: Yateman Nigel A. , Skene Allan M. 
Published: April 2007

AbstractIn proportional hazards survival studies, power depends on the observed number of deaths,d*. For a given choice of survival, loss, and patient entry distributions, sample sizes can be determined by equatingd* to the expected number of deaths. Approximating the survival and loss distributions with piecewise exponential distributions, and patient entry with a piecewise linear distribution, significantly reduces the computational overhead, and the expected number of deaths can be evaluated routinely. The merits of this approach are illustrated by a clinical trial of chemotherapy for large bowel cancer.
Some large‐sample distribution‐free estimators and tests for multivariate partially incomplete data from two populations
Authors: Lachin John M. 
Published: April 2007

AbstractThe most common instance of multivariate observations is the case of repeated measures over time. The two most widely used methods for the analysis ofKrepeated measures for two groups are theKdegrees of freedom (d.f.)T2MANOVAF‐test and the within‐subjects 1 degree of freedom ANOVAF‐test. Both require complete samples from normally distributed populations. In this paper, I describe alternativeKand 1 d.f. distribution‐free procedures which allow for randomly missing observations. These include a large‐sample analysis of means, the Wei and Lachin multivariate Wilcoxon test with estimates of the Mann‐Whitney parameter, and a multivariate Hodges‐Lehmann location shift estimator based on the multivariateU‐statistic of Wei and Johnson. Each of these methods provides a distribution‐freeK‐variate estimate of the magnitude of group differences which can be used as the basis for an overall test of group differences. These tests include theKd.f. omnibusT2‐like test, 1 d.f. tests of restricted hypotheses, such as the Wei‐Lachin multivariate one‐sided test of stochastic ordering, and the test of general association based on a minimum variance generalized least squares (GLS) estimate of the average group difference. I then describe covariate stratified‐adjusted GLS estimates and tests of group differences. This approach also provides tests of homogeneity (interaction) for within‐subjects and between‐subjects effects. I illustrate these analyses with an analysis of repeated cholesterol measurements in two groups of patients, stratified by sex. Such analyses provide an overall distribution‐free summary estimate and test of the treatment effect obtained by combining the group differences over both time (repeated measures) and strata.
Using follow‐up data to avoid omitted variable bias: An application to cardiovascular epidemiology
Authors: Rehm J. , Arminger G. , Kohlmeier L. 
Published: April 2007

AbstractOmitted variable bias is discussed in the context of linear models. It is shown that the effect of omitted variables can be controlled in linear models for metric dependent variables by using data from follow‐up studies. Two different models for analysing such data are proposed. In the first model the omitted variables are assumed to be uncorrelated with the explanatory variables in the model and to be constant over time. These assumptions lead to a special structure of the covariance matrix of the errors over time. Efficient estimation of the parameters in the linear model has to take this special covariance matrix of the errors into account by using appropriate generalized least squares or maximum likelihood methods. In the second model the omitted variables are assumed to be time constant. Additionally, they are allowed to be correlated with the explanatory variables, that is these variables are omitted confounders in the usual epidemiological sense. It is shown that even in this case the parameters of the linear model can be estimated consistently with ordinary least squares if a follow‐up study is available. The differences between the parameter estimates under the first and the second model may be used to construct a Hausman test for misspecification. The models, the estimation methods and the Hausman test are illustrated by the example that explores the determinants of serum cholesterol in German adoloscents of both sexes.
Using the virus challenge dose in the analysis of virus neutralization assays
Authors: Parker R. A. , Pallansch M. A. 
Published: April 2007

AbstractWe propose a new basis for adjusting the results of virus neutralization assays. These assays consist of two separate experiments performed in parallel: a virus titration experiment and a serum dilution assay. In the virus titration experiment, one estimates the amount of virus used in the assay (the virus challenge dose). In a typical virus neutralization assay, the virus challenge dose may range over an order of magnitude. In the serial dilution assay, one measures the serum neutralizing activity against a particular virus. Most standard methods use the virus titration results only to ensure that the overall experiment is acceptable; the specific results of the virus titration experiment are not used to adjust the estimate of serum neutralizing activity. Although adjustment based on calibration with reference sera could be done, this seldom occurs in practice. We use results from recent studies of the kinetics and stoichiometry of polio virus neutralization with monoclonal antibodies to develop a method to use results from the virus titration experiment to adjust the serum neutralizing activity directly. Our results also indicate that a simple ad hoc procedure can improve the accuracy of the estimated serum neutralizing activity.
Discussion of session 1: Clinical trials, survival analysis
Authors: Fisher Lloyd D. 
Published: April 2007

AbstractThe paper contains discussion of session 1 of the 1991 American Statistical Association meeting on longitudinal data analysis and subsequently submitted papers. Koch and colleagues present an application of weighted least squares to 24 hour manual blood pressure measurements. Wei reviews linear rank methods for censored survival data using the accelerated failure time model; newer testing methods are presented. The use of these approaches may depend upon the value over currently used methods; the answer is not clear. Schluchter presents a method for evaluating informatively censored longitudinal data. The approach may well be an improvement over currently published approaches; the problems addressed may be important in a minority of situations. Finally, the discussant emphasizes the need for progress in three more mundane but very important areas: (1) data base systems with syntax appropriate for longitudinal data analyses; (2) graphical methods; and (3) software, well documented and supported, to exploit the progress in longitudinal data analytic methods.
Multivariate methods for binary longitudinal data with heterogeneous correlation over time
Authors: Rosner B. 
Published: April 2007

AbstractClustered binary data occur frequently in biostatistical work. One particular application is in binary longitudinal data, where several visits are available for the same individual. Several approaches have been proposed for the analysis of clustered binary data. In Rosner, a polychotomous logistic regression model was proposed which is a generalization of the beta‐binomial distribution and allows for person‐ and visit‐specific covariates, while controlling for clustering effects. One assumption of this model is that all pairs of visits within an individual are equally correlated, which may be inappropriate if several visits are available over a long follow‐up period. In this paper, this approach is extended to allow for heterogeneous correlation over time. The total time period is divided into subintervals and a beta‐binomial mixture model is introduced to estimate odds ratios relating outcomes for pairs of visits both within a subinterval as well as in different subintervals. To include covariates, an extension of the polychotomous logistic regression model is proposed, which allows one to estimate effects of person‐, subinterval‐, and visit‐specific covariates, while controlling for clustering using the beta‐binomial mixture model. This model is applied to the analysis of respiratory symptom data in children collected over a 14‐year period in East Boston, MA, in relation to maternal and child smoking, where the unit is the child and symptom history is divided into early‐adolescent and late‐adolescent symptom experience.
Non‐linear models for the analysis of longitudinal data
Authors: Vonesh Edward F. 
Published: April 2007

AbstractGiven the importance of longitudinal studies in biomedical research, it is not surprising that considerable attention has been given to linear and generalized linear models for the analysis of longitudinal data. A great deal of attention has also been given to non‐linear models for repeated measurements, particularly in the field of pharmacokinetics. In this article, a brief overview of non‐linear models for the analysis of repeated measures is given. Particular emphasis is placed on mixed‐effects non‐linear models and on various estimation procedures proposed for such models. Several of these estimation procedures are compared via a simulation study. In addition, simulation is used to investigate the effects of model misspecification, particularly with respect to one's choice of random‐effects. A relatively straightforward measure useful in selecting an appropriate set of random effects is investigated and found to perform reasonably well.
Graphs and stochastic relaxation for hierarchical bayes modelling
Authors: Lange Nicholas 
Published: April 2007

AbstractThis expository paper describes two useful tools for the statistical analysis of processes that generate repeated measures and longitudinal data. The first tool is a graph for a visual description of dependency structures. The second tool is a stochastic relaxation method (‘Gibbs sampling’) for fitting hierarchical Bayes models. Graphs are concise and accessible summaries of stochastic models. Graphs aid communications between statistical and subject‐matter scientists, during which formulations of scientific questions are modified. An uncluttered picture of the dependency structure of a model augments effectively its corresponding formulaic description. Stochastic relaxation is a computationally intense method that allows experimentation with broader classes of models than were previously thought feasible because of analytic intractability. Stochastic relaxation is intuitive and easily described to non‐statisticians. Several sample graphs show how hierarchical Bayes models can use stochastic relaxation to obtain their fits. An example based on estimating drug shelf‐life demonstrates some uses of graphs and stochastic relaxation compared with several frequentist growth curve analyses that use restricted maximum likelihood and generalized estimating equations approaches.
A bootstrap resampling procedure for model building: Application to the cox regression model
Authors: Sauerbrei Willi , Schumacher Martin 
Published: April 2007

AbstractA common problem in the statistical analysis of clinical studies is the selection of those variables in the framework of a regression model which might influence the outcome variable. Stepwise methods have been available for a long time, but as with many other possible strategies, there is a lot of criticism of their use. Investigations of the stability of a selected model are often called for, but usually are not carried out in a systematic way. Since analytical approaches are extremely difficult, data‐dependent methods might be an useful alternative. Based on a bootstrap resampling procedure, Chen and George investigated the stability of a stepwise selection procedure in the framework of the Cox proportional hazard regression model. We extend their proposal and develop a bootstrap‐model selection procedure, combining the bootstrap method with existing selection techniques such as stepwise methods. We illustrate the proposed strategy in the process of model building by using data from two cancer clinical trials featuring two different situations commonly arising in clinical research. In a brain tumour study the adjustment for covariates in an overall treatment comparison is of primary interest calling for the selection of even ‘mild’ effects. In a prostate cancer study we concentrate on the analysis of treatment‐covariate interactions demanding that only ‘strong’ effects should be selected. Both variants of the strategy will be demonstrated analysing the clinical trials with a Cox model, but they can be applied in other types of regression with obvious and straightforward modifications.