bluuebunny/crossref_metadata_2025_split huggingface dataset:
Additivity test for composition of binomial effects in chromosome aberrations after radiation injury
Authors: Komornik J. , Mikulecky M. , Beno M. 
Published: April 2007

AbstractAdditivity of clastogenic effects of two doses of radiation to human peripheral blood lymphocytes is considered. We compare the effects of combined tritium and gamma irradiation with the sum of the effects of either irradiation administered separately. Our solution is based on combination of de Moivre and Cramèr—Slucky theorems. The results for 14 subjects where the deletion types of chromosome aberrations were counted indicate a Normal distribution of the sample additivity index, oscillating non‐significantly around zero, with one outlier towards adaptivity and another towards synergism of the effect of the two doses absorbed by the cells.
A comparison of methods for correlated ordinal measures with ophthalmic applications
Authors: Gange Stephen J. , Linton Kathryn L. P. , Scott Alastair J. , Demets David L. , Klein Ronald 
Published: April 2007

AbstractFor many clinical trials and epidemiologic investigations in the field of ophthalmology, paired ordinal data are often collected through the detailed grading of retinal photographs. One method for analysis of these data is the extension of the generalized estimating equation (GEE) methodology to multinomial data with cumulative link functions. Prior to the development of this advanced technique, however, ophthalmologists developed a method of combining the ordinal responses of both eyes of a patient into a single person‐level response on a new ordinal scale. A relationship between the regression coefficients of these two methods is derived as a function of the correlation between eyes. We investigate the applicability of this result and the relationship of the standard errors in simulation experiments and in an example from the Wisconsin Epidemiologic Study of Diabetic Retinopathy.
Multi‐state models and diabetic retinopathy
Authors: Marshall Guillermo , Jones Richard H. 
Published: April 2007

AbstractThis paper discusses the application of a multi‐state model to diabetic retinopathy under the assumption that a continuous time Markov process determines the transition times between disease stages. The multi‐state model consists of three transient states that represent the early stages of retinopathy, and one final absorbing state that represent the irreversible stage of retinopathy. By using a model with covariables, we explore the effects of factors that influence the onset, progression, and regression of diabetic retinopathy among subjects with insulin‐dependent diabetes mellitus. We can also introduce time‐dependent covariables in the model by assuming that the covariables remain constant between two observations. We can also obtain survival‐type curves from each stage of the disease and for any combination of patient risk factors.
Methods for bounding the marginal survival distribution
Authors: Dignam James J. , Weissfeld Lisa A. , Anderson Stewart J. 
Published: April 2007

AbstractFor time to event data with many potential failure types, one cannot uniquely determine the distribution of time to a specific event type, or marginal survival distribution, in the case where event types are mutually exclusive. In this paper we discuss several methods for estimating functions that bound the non‐identifiable marginal survival distribution in the competing risks problem. We compute and compare bounds for data simulated from two bivariate survival distributions. Results show that the methods provide a suitable estimate of the marginal survival probability when one has specified dependence correctly. Data from a large clinical trial for breast cancer illustrate the methods.
Construction, validation and updating of a prognostic model for kidney graft survival
Authors: van Houwelingen Hans C. , Thorogood Jane 
Published: April 2007

AbstractThe construction, validation and updating of a prognostic model for kidney graft survival is reported using data from the Eurotransplant database. First, a model is constructed for data from transplantations in the period 1984 to 1987. The model is later updated for the 1988–1990 data. The first data set was randomly split into a training set (two‐thirds of the data) and a validation set (one‐third). To prevent overfitting empirical Bayes estimation of the transplantation centre effect was employed. After that, the validation set was used for fine‐tuning by shrinkage. For updating with the 1988–1990 data parametric models were used after suitable transformation of the time axis; it appeared that survival had slightly improved. This necessitated a correction of the parameters in the exponential model. Correctness of the model was checked by extension to a Weibull model. The lack of fit was statistically significant, but practically ignorable. Recommendations are made to place less emphasis on the selection of variables and cut‐off points, and more emphasis on the fine‐tuning of the prognostic model by means of low‐dimensional parametric models in independent data sets.
Self‐modelling with random shift and scale parameters and a free‐knot spline shape function
Authors: Lindstrom Mary J. 
Published: April 2007

AbstractThe shape invariant model is a semi‐parametric approach to estimating a function relationship from clustered data (multiple observations on each of a number of individuals). The common response curve shape over individuals is estimated by adjusting for individual scaling differences while pooling shape information. In practice, the common response curve is restricted to some flexible family of functions. This paper introduces the use of a free‐knot spline shape function and reduces the number of parameters in the shape invariant model by assuming a random distribution on the parameters that control the individual scaling of the shape function. New graphical diagnostics are presented, parameter identifiability and estimation are discussed, and an example is presented.
Residual plots for the censored data linear regression model
Authors: Hillis Stephen L. 
Published: April 2007

AbstractTo be consistent, censored data linear regression estimators typically require a correctly specified linear regression function and independent and identically distributed errors. For uncensored data one can assess these model assumptions informally by examining plots of the residuals against the independent variables or fitted values. In this paper I propose plots for censored data analogous to these uncensored data residual plots. One can use such plots in the same way as their uncensored data counterparts for checking model assumptions; if the model assumptions are correct, then the plots should exhibit a random scatter. I show that the proposed plots are useful in selecting a linear regression model for the Stanford heart transplant data.
Notes on conditional confidence limits under inverse sampling
Authors: Lui Kung‐Jong 
Published: April 2007

AbstractWhen the number of subjects in a two‐by‐two table is small or moderate, we may commonly use the exact conditional distribution with all marginals fixed to derive the conditional confidence limits on the underlying parameter. Under inverse sampling, in which we continue to sample subjects until we obtain exactly a pre‐determined number of subjects falling into a specific category, this paper notes that derivation of a confidence interval, which has the coverage probability equal to or larger than a nominal 1 – α confidence level, for relative risk and relative difference in cohort studies is straightforward. This paper further finds that, when the underlying disease is rare, we can similarly apply an inverse sampling to produce an approximate 1 – α conditional confidence limits on attributable risk in case‐control studies as well. When the number of subjects is small and the test statistic derived on the basis of large sample theory is not strictly adequate for use, this paper also presents an exact hypothesis testing procedure for the above parameters in the corresponding study designs.
Practical methodology of meta‐analyses (overviews) using updated individual patient data
Authors: Stewart Lesley A. ,   Cochrane Working Group On Meta‐Analysis Using Individual Patient Data
Published: April 2007

AbstractMeta‐analyses using updated individual patient data may provide the most reliable means of combining data from similar randomized controlled trials. The benefits of this approach to systematic reviews are described. Guidance, based on the experience of several groups who have undertaken such projects, is given. This includes practical advice on initiating and maintaining collaboration, the time and resources required to undertake these usually international projects and methods of data checking and validation. Example proforma are included.
The design and analysis of randomized trials with recurrent events
Authors: Cook Richard J. 
Published: April 2007

AbstractThis paper describes a method for planning the duration of a randomized parallel group study in which the response of interest is a potentially recurrent event. At the design stage we assume patients accrue at a constant rate, we model events via a homogeneous Poisson process, and we utilize an independent exponential censoring mechanism to reflect loss to follow‐up. We derive the appropriate study duration to ensure satisfaction of power requirements for the effect size of interest under a Poisson regression model. An application to a kidney transplant study illustrates the potential savings of the Possion‐based design relative to a design based on the time to the first event. Revised design criteria are also derived to accomodate overdispersed Poisson count data. We examine the frequency properties of two non‐parametric tests recently proposed by Lawless and Nadeau for trials based on the above design criteria. In simulation studies involving homogeneous and non‐homogeneous Poisson processes they performed well with respect to their type I error rate and power. Results from supplementary simulation studies indicate that these tests are also robust to extra‐Posson variation and to clustering in the event times, making these tests attractive in their generality. We illustrate both tests by application to data from a completed kidney transplant study.
An application of decision theory to patient screening for an autologous tumour vaccine trial
Authors: Shannon William D. , Bryant John , Logan Theodore F. , Day Roger 
Published: April 2007

AbstractPatients are eligible for accrual onto a phase I autologous tumour vaccine clinical trial if their resected and dissociated tumour achieves a minimum viable cell count. Because tumour pre‐processing and cell count determination are expensive, there has been developed a screening procedure based on tumour mass to screen out those tumours unlikely to yield sufficient viable cells. If θ is the ratio of the expected benefit of an accrual onto the study to the cost of tumour pre‐processing and cell counting, then we maximize long‐run benefit by pre‐processing and counting only those tumours whose masses exceed a cutoff mc, such that Pr {sufficient tumour cells mass = mc} = 1/θ. We derive algorithms for estimating mc and evaluate them under a variety of assumptions concerning the cell count/mass relationship. These include explicit equations for mc under parametric assumptions as well as more general algorithms based on non‐parametric smoothing techniques. We show that when θ deviates substantially from 2, these methods outperform simple inverse interpolation.
The restricted cubic spline as baseline hazard in the proportional hazards model with step function time‐dependent covariables
Authors: Herndon James E. , Harrell Frank E. 
Published: April 2007

AbstractWe incorporate a cubic spline function where the tails are linearly constrained, as the baseline hazard, into the proportional hazards model. We show estimation of covariable coefficients and survival probabilities with this model to be as efficient statistically as with the Cox proportional hazards model when covariables are fixed. Examples show that the inclusion of time‐dependent covariables defined as step functions into the restricted cubic spline proportional hazards model reduces computation time by a factor of 213 over the Cox model. Advantages of the spline model also include flexibility of the hazard, smooth survival curves, and confidence limits for the survival and hazard estimates when there are time‐dependent covariables present.
Standardized estimates from categorical regression models
Authors: Joffe Marshall M. , Greenland Sander 
Published: April 2007

AbstractWe consider the problem of interpreting categorical regression models, such as the polytomous logistic model, the continuation‐ratio model, the stereotype model, and the cumulative‐odds model. We present a method to convert categorical regression coefficients into estimates of standardized fitted probabilities, probability differences and probability ratios. We use a delta‐method approach to estimate standard errors. We then present a small simulation study to compare different transforms for setting confidence limits, and provide an illustration of our approach in an observational study of drug therapy of polymyositis.
Exact logistic regression: Theory and examples
Authors: Mehta Cyrus R. , Patel Nitin R. 
Published: April 2007

AbstractWe provide an alternative to the maximum likelihood method for making inferences about the parameters of the logistic regression model. The method is based appropriate permutational distributions of sufficient statistics. It is useful for analysing small or unbalanced binary data with covariates. It also applies to small‐sample clustered binary data. We illustrate the method by analysing several biomedical data sets.
Modelling mortality and morbidity of newborns
Authors: Ritter C. , Bouckaert A. , van Lierde M. , Theunissen I. 
Published: April 2007

AbstractData of 10,514 singleton births collected over a ten year period at a single hospital are analysed and models linking birthweight and gestational age with mortality and morbidity defined by low Apgar scores are constructed and compared. Based on these models, charts of mortality and morbidity are drawn and compared with common charts of birthweight centiles. Classification rules for newborns at risk are defined by iso‐mortality contours, marginal birthweight centiles, and birthweight centiles adjusted by gestational age, respectively, and compared using receiver operating characteristic (ROC) curves. The results suggest that, as far as neonatal mortality is concerned, classification rules based on simple marginal birthweight centiles perform almost as well as iso‐mortality contours and considerably better than birthweight centiles adjusted for gestational age.
Longitudinal models for analysis of respiratory function
Authors: D'Agostino Ralph B. , Sparrow David , Weiss Scott , Rosner Bernard 
Published: April 2007

AbstractWe compare the results of fitting three longitudinal models, two autoregressive models (the serial correlation model and a damped autoregressive model) and a compound symmetry model, to data on a cohort of 1154 adult men in Boston. The serial correlation model assumes that the error terms are autocorrelated with correlation of the form λt for visits t years apart while the damped autoregressive assumes that the correlation between error terms of observations t years apart is of the form λtθ. The compound symmetry model assumes that the errors are correlated, with the same correlation regardless of how far apart observations are in time. These three models are all related in that the serial correlation and compound symmetry models are particular cases of the damped autoregressive models (that is, θ = 1 corresponds to the serial correlation model and θ = 0 corresponds to the compound symmetry model). For current smokers, the damped autoregressive model provided a significantly better fit than either of the other two models (p < 0·001); for never smokers the damped autoregressive and compound symmetry models were almost identical with both providing a significantly better fit than the serial correlation model (p < 0·001).
A statistic for comparing two correlated markers which are prognostic for time to an event
Authors: Jung Sin‐Ho , Wieand Sam , Cha Stephen S. 
Published: April 2007

AbstractA proliferation of biologic markers has led to increased interest in methods for comparing the prognostic value of markers in predicting time to recurrence of or death from disease. This paper proposes a non‐parametric test statistic for comparing two continuous markers when the outcome of interest is time to an event and the outcome is subject to right censoring. Results from Monte Carlo studies show that the new testing procedure is reliable for practical use. We present an example in which we use the statistic successfully in a colon cancer study to select between two potentially useful markers.
A simulation study used to design the sequential monitoring plan for a clinical trial
Authors: Brooks Maria Mori , Hallstrom Al , Peckova Monika 
Published: April 2007

AbstractIn designing a sequential monitoring plan for the AVID (Antiarrhythmics Versus Implantable Defibrillators) study, a multi‐centred clinical trial, we completed a simulation study to help ourselves and the Data Safety Monitoring Board understand the implications of various monitoring plans. In particular, we compared three test statistics and three use functions. Simulation was based on approximate trial parameters and two plausible but distinct alternative hypotheses. The power to detect a difference between the two treatment arms was computed as a function of time. The simulated results illuminate several of the important statistical and ethical issues involved in selecting a sequential monitoring plan and illustrate how one can use simulations to design a clinical trial.
Exact power computation for dose—response studies
Authors: Tang Man‐Lai , Hirji Karim F. , Vollset Stein E. 
Published: April 2007

AbstractThe toxicity of an agent or the therapeutic effect of a drug may be assessed by a dose‐response study. We present a method for computing the exact power of exact and large sample statistical tests employed for binary response data from such a study. This method, based on recursive polynomial multiplications, enables fast computation of exact power for studies with up to a moderately large sample size. We demonstrate the efficiency of our method using three examples. The method is suitable for the design and power analysis of dose—response studies in which the usual asymptotic approximations are suspect.
The power of focused tests to detect disease clustering
Authors: Waller Lance A. , Lawson Andrew B. 
Published: April 2007

AbstractStatistical tests have been proposed for determining whether incident cases of adverse health effects are ‘clustered’ together. Several procedures, termed ‘focused’, specifically analyse disease surveillance data around pre‐specified putative sources of environmental hazard. Little has been done to compare the performance of various proposed methods on actual models of clustering. Analytic power functions are derived for three tests of focused clustering. These functions are based on the probabilistic structure of the clustering tests and do not require simulation. The three tests are compared with respect to statistical power on hypothetical data where monotone multiplicative increases in disease risk near a putative hazard define disease clusters of varying intensity.
The choice of test for detecting raised disease risk near a point source
Authors: Bithell J. F. 
Published: April 2007

AbstractThis paper considers the problem of testing for excess risk near a point source of risk S, as might be postulated to exist near a nuclear installation, for example. The data are assumed to be in the form of counts in small areas for which expected numbers of cases have also been calculated by reference to national rates. It is shown how to derive the most powerful test against any given alternative hypothesis; such ‘linear risk score’ tests are further considered, particularly that which scores each case according to the reciprocal of the rank of the distance from S. These tests are compared with two tests due to Stone for general ordered alternatives and the important distinction is drawn between conditional and unconditional versions of the tests. Their behaviour is illustrated by application to data on childhood leukaemia in relation to nuclear installations in Britain.
Non‐parametric estimation of spatial variation in relative risk
Authors: Kelsall Julia E. , Diggle Peter J. 
Published: April 2007

AbstractWe consider the problem of estimating the spatial variation in relative risks of two diseases, say, over a geographical region. Using an underlying Poisson point process model, we approach the problem as one of density ratio estimation implemented with a non‐parametric kernel smoothing method. In order to assess the significance of any local peaks or troughs in the estimated risk surface, we introduce pointwise tolerance contours which can enhance a greyscale image plot of the estimate. We also propose a Monte Carlo test of the null hypothesis of constant risk over the whole region, to avoid possible over‐interpretation of the estimated risk surface. We illustrate the capabilities of the methodology with two epidemiological examples.
The map comparison problem: Tests for the overlap of geographic boundaries
Authors: Jacquez Geoffrey M. 
Published: April 2007

AbstractThe quality of environmental studies is often compromised by the use of statistics, such as correlation and regression for example, which presuppose a statistical model, linear or otherwise, between two variables. When investigating hypotheses about relationships among geographically distributed variables, an alternative approach is to measure the amount of boundary overlap. Boundaries are geographic zones of rapid change in the intensity of a variable, and are often of scientific interest in their own right. Examples of boundaries include ecotones, genetic hybrid zones, pollution plumes, and the front of the wave of advance of an epidemic. Boundary overlap describes zones where boundaries from two or more variables coincide, and are useful for evaluating epidemiologic hypotheses relating health to environmental exposures. This paper proposes four statistics of boundary overlap, and explores their performance using simulation models and real data describing ozone concentrations and hospital admissions for respiratory conditions. The statistics are found sensitive to different aspects of boundary overlap, and provide an additional diagnostic tool in the analysis of geographically distributed variables. Overlap statistics are expected to come into increasing use as the installed base of geographic information systems increases.
The spatial analysis of covariates in a study of environmental epidemiology
Authors: Donnelly Christl A. 
Published: April 2007

AbstractKanawha County, West Virginia, includes a deep river valley with a large population living in close proximity to many chemical manufacturing centres. One study of this area combined an epidemiologic survey of children attending Kanawha County schools with the measurement of the concentrations of 15 volatile organic compounds at each of the 75 elementary schools. Results have been reported by Ware et al. This paper further examines the associations of the concentrations of the ten individual volatile organic compounds related to chemical manufacturing processes with observed lower respiratory symptoms, generalizing the model of spatial variability and measurement error proposed by Donnelly et al. and using semi‐variograms to assess the validity of model assumptions.