bluuebunny/crossref_metadata_2025_split huggingface dataset:
Interfacing U.S. census map files with statistical graphics software: Application and use in epidemiology
Authors: Rizzardi M. , Mohr M. S. , Merrill D. W. , Selvin S. 
Published: April 2007

AbstractIn 1990, the United States Bureau of the Census released detailed geographic map files known as TIGER/Line )Topologically Integrated Geographic Encoding and Referencing(. The TIGER files, accessible through purchase or federal repository libraries, contain 24 billion characters of data describing various geographic features including coastlines, hydrography, transportation networks, political boundaries, etc. for the entire United States. Many of these physical features are of potential interest in epidemiological case studies. Unfortunately, the TIGER data base only provides raw alphanumeric data; no utility software, graphical or otherwise, is included. Recently, the S statistical software package has been extended to include a map display function. The map function augments S's high‐level approach towards statistical analysis and graphical display of data. Coupling this statistical software with the map data base developed for U.S. Census data collection will facilitate epidemiological research. We discuss the technical background necessary to utilize the TIGER data base for mapping with S. Two types of S maps, segment‐based and polygon‐based, are discussed along with methods to construct them from TIGER data. Polygon‐based maps are useful for displaying regional statistical data, such as disease rates or incidence at the census tract level. Segment‐based maps are easier to assemble and are appropriate when the data are not regionalized. Census tract data of AIDS incidence in San Francisco and lung cancer case locations relative to petrochemical refinery sites in Contra Costa County are used to illustrate the methods and potential uses of interfacing the TIGER data base with S.
Distribution‐free fitting of logit models with random effects for repeated categorical responses
Authors: Agresti Alan 
Published: April 2007

AbstractThis article discusses random effects models for within‐subject comparisons of repeated responses on the same categorical scale. The models account for the correlation that normally occurs between repeated responses. The standard way of fitting such models maximizes the marginal likelihood after integrating with respect to a distribution for the random effect. An alternative non‐parametric approach does not assume a distributional form for the random effects. Recent literature shows that for certain simple logit models, this approach yields essentially the same model parameter estimates as conditional maximum likelihood. Moreover, these estimates also result from fitting corresponding quasi‐symmetric log‐linear models. For simple data sets in which primary interest relates to subject‐specific comparisons of the repeated responses, one can easily obtain the estimates with standard software for log‐linear models. Examples include data from crossover designs and from comparisons of treatment and control groups regarding the change between baseline and follow‐up observations.
Controlling for a confounder monotonically related to exposure by means of isotonic quantile regression
Authors: Strömberg Ulf 
Published: April 2007

AbstractWe present a new approach to evaluating the effect of a continuous exposure factor when that factor increases with a covariate )for examle, age( which is regarded as a potential confounder. The basic idea is to estimate, as functions of the covariate, some selected quantiles of the exposure distribution, under the assumption that the dependence of each quantile on the covariate is monotonic. The resulting estimates are then used to divide the data into different exposure categories. This method of categorizing the data implies that the covariate distribution will be almost the same in each exposure group. We illustrate the approach with a study of blood pressure and cardiovascular mortality.
TWO‐stage sampling in pharmaceutical applications
Authors: Shao Jun , Chow Shein‐Chung 
Published: April 2007

AbstractIn the pharmaceutical industry, some tests for quality assurance before, during and after the manufacture of a drug product involve a two‐stage sampling technique. We propose statistical testing procedures in a two‐stage sampling problem with large within‐class sample sizes. We also derive a two‐stage sampling plan by minimizing the expected squared volume )or the generalized variance( of the confidence region related to the test. We present two examples to illustrate the proposed methods.
Sample sizes for event rate equivalence trials using prior information
Authors: Gould A. Lawrence 
Published: April 2007

AbstractTrials for demonstrating the ‘equivalence’ of active standard and test treatments generally require large sample sizes that depend on the definition of ‘equivalence’ and the overall event rate when the outcome is incidence of an event such as morality. The planning of sample sizes for such trials requires specification of a value for the overall event rate. This value often will reflect the outcomes of previous trials of the standard treatment, and is subject to uncertainty that needs some accommodation, to protect against an inadequate sample. Bayes and Empirical Bayes methods can be used to incorporate information from one or more previous trials into the sample size calculation when equivalence means high confidence that the event rate ratio is less than some specified value.
AN algorithm for robust non‐linear analysis of radioimmunoassays and other bioassays
Authors: Normolle Daniel P. 
Published: April 2007

AbstractThe four‐parameter logistic function is an appropriate model for many types of bioassays that have continuous response variables, such as radioimmunoassays. By modelling the variance of replicates in an assay, one can modify the usual parameter estimation techniques )for example, Gauss–Newton or Marquardt–Levenberg( to produce parameter estimates for the standard curve that are robust against outlying observations. This article describes the computation of robust )M‐( estimates for the parameters of the four‐parameter logistic function. It describes techniques for modelling the variance structure of the replicates, modifications to the usual iterative algorithms for parameter estimation in non‐linear models, and a formula for inverse confidence intervals. To demonstrate the algorithm, the article presents examples where the robustly estimated four‐parameter logistic model is compared with the logit‐log and four‐parameter logistic models with least‐squares estimates.
Testing concomitancy between two physiological pulse series
Authors: Yang Mark C. K. , Rao P. V. 
Published: April 2007

AbstractStatistical methods for detecting synchronization of onsets of two physiological events, such as concomitant releases of prolactin )PRL( and luteinizing hormone )LH(, are studied in this paper. Usually, the two events are measured regularly and simultaneously and the onset times are then determined by the changes in the values of the measurements. Owing to difficulties in determining the exact onset times, a leeway is allowed for counting the onset synchronizations in most physiological studies. Using such a relaxed definition of synchronization and needing to allow for a recovery time between two episodes make the traditional hypergeometric test of concomitance inappropriate. Based on a truncated geometric inter‐arrival distribution, Clifton et al. have used simulation to construct a table of critical values for performing tests of significance of the observed number of coincidences in a series of 73 measurements. In this paper, the conditions under which their method can be used are examined from a statistical point of view. Methods for testing cases not covered by Clifton et al. are also described.
Statistics in medicine: Citations of papers in the first ten years
Authors: Campbell M. J. , Julious S. A. 
Published: April 2007

AbstractAll papers from Volume 1 ofStatistics in Medicinewere followed up in theScience Citation Index. There were 6.7 citations per paper in medical journals as opposed to 1.5 citations per paper in statistical journals, and overall there were 8.7 citations per paper. The average citation rate was lower than that of the first issue ofBiometrics, published in the same year, but this was partly because of a greater proportion of zero‐cited papers. Citations from medical journals increased annually, until about 5 years after publication, after which they remained steady. Volumes 2‐6 ofStatistics in Medicineand Volumes 29‐33 ofBiometrics, for the years 1983‐87, were followed up for statistical/medical source of the citations. Citations for Volumes 2‐4 ofStatistics in Medicinewere relatively low, but picked up by Volume 5.Biometricshad more citations from statistical sources than from medical, by contrast toStatistics in Medicinewhich had far more citations from medical sources than from statistical.
A comparative study of several antibiotic formulations using a design based on a combination of balanced incomplete blocks and Latin squares
Authors: Prescott Philip , Ryan Joyce , Shuttleworth Paul 
Published: April 2007

AbstractA practical application of an experimental design, suitable for the comparison of several treatments, and based on combining balanced incomplete blocks and Latin squares balanced for carryover effects, is presented in the context of comparing a number of paediatric antibiotic formulations for taste, smell and colour. The recommended designs, originally suggested by Patterson, have the advantage of balanced incomplete blocks, in that a single trial may be used to compare a larger number of treatments than may reasonably be given to any individual subject. In addition, the incorporation of suitably chosen Latin squares allows for assessment of any effect of order of presentation of the treatments and for any simple first‐order carryover effect of one treatment into the following treatment period. Inclusion of such effects in the overall analysis could result in the reduction of bias in the comparisons of the treatments.
Estimating age, period and cohort effects using the multistage model for cancer
Authors: Holford Theodore R. , Zhang Zhongxin , McKay Lisa A. 
Published: April 2007

AbstractTo understand cancer aetiology better, epidemiologists often try to investigate the time trends in disease incidence with year of diagnosis (period) and birth cohort. Unfortunately, one cannot identify these factors uniquely in the usual regression model owing to a linear dependence between age, period and cohort, so that one requires additional information about the underlying biology of the disease. Carcinogenesis models provide one type of information that can result in a unique set of parameters for the effects of age, period and cohort. We use the multistage carcinogenesis model and its extensions to obtain a unique set of parameters for an age‐period‐cohort model of lung cancer trends of Connecticut males and females from 1935 to 1988. Some of these models do not seem to provide a reasonable set of model parameters, but we found that a model that included second‐order terms and a multistage mixture model both gave a good fit to the data and realistic parameter estimates.
On the analysis of life tables for dependent observations
Authors: Eriksson Bo , Adell Ragnar 
Published: April 2007

AbstractApplying methods assuming independence when observations are positively correlated means that confidence intervals become too short and significance levels of statistical tests are less extreme. This paper discusses elements of life‐table analysis based on the standard product‐limit estimator and a modified Greenwood formula for its variance to be used for dependent observations. An application from oral surgery is given. Erroneously assuming independence in the analysis of a life table could have serious consequences. It is demonstrated in a simulation study that the confidence levels can be much too low. The proposed modification of the Greenwood formula for the variance of the estimated survival function most often results in confidence levels not too much below the required level. Using the upper bound for the variance will give conservative confidence intervals but also larger standard errors. Averaging individual group survival curves should only be considered for situations with large groups.
Network analytic methods for epidemiological risk assessment
Authors: Altmann Michael , Wee Beeling C. , Willard Keith , Peterson Denton , Gatewood Laël C. 
Published: April 2007

AbstractThe authors measure the efficacy of three methods for predicting the time to infection for susceptible individuals in a population undergoing an HIV epidemic. The methods differ in whether they require detailed information of the contact network and whether they require knowledge of the initial source of infection. Efficacy is evaluated using simulations for 20 different contact patterns. Only the risk score that uses both kinds of information accounts for more than 15 per cent of individual variability. The efficacy of this score ranges from 10 per cent in very unstructured populations to 60 per cent for spatially localized contact networks. This improved performance may be explained by the larger fraction of the total variability not due to the disease dynamics. When all variables are dichotomized, the two poorer methods produce odds ratios between 1.4 and 2.3. The odds ratio for the risk score with full information ranges from 2.5 to 17. Risk assessment protocols and intervention programmes are encouraged to assess contact patterns and detect sources of infection.
Cohort versus cross‐sectional design in large field trials: Precision, sample size, and a unifying model
Authors: Feldman Henry A. , McKinlay Sonja M. 
Published: April 2007

AbstractIn planning large longitudinal field trials, one is often faced with a choice between a cohort design and a cross‐sectional design, with attendant issues of precision, sample size, and bias. To provide a practical method for assessing these trade‐offs quantitatively, we present a unifying statistical model that embraces both designs as special cases. The model takes account of continuous and discrete endpoints, site differences, and random cluster and subject effects of both a time‐invariant and a time‐varying nature. We provide a comprehensive design equation, relating sample size to precision for cohort and cross‐sectional designs, and show that the follow‐up cost and selection bias attending a cohort design may outweigh any theoretical advantage in precision. We provide formulae for the minimum number of clusters and subjects. We relate this model to the recently published prevalence model for COMMIT, a multi‐site trial of smoking cessation programmes. Finally, we tabulate parameter estimates for some physiological endpoints from recent community‐based heart‐disease prevention trials, work an example, and discuss the need for compiling such estimates as a basis for informed design of future field trials.
Sample size requirements for stratified prospective studies with null hypothesis of non‐unity relative risk using the score test
Authors: Nam Jun‐Mo 
Published: April 2007

AbstractThe standard test of the null hypothesis of unity of the relative risk seeks to determine if two treatments differ. It does not apply when the requirement is either to establish the equivalence of two treatments or to determine whether the relative risk is less than a specified value other than one. This paper presents the asymptotic power function of the score test for the null hypothesis of a specified value of a common relative risk for stratified prospective studies and proposes an approximate formula for the sample size required for a specific power of the test. One can obtain a sample size formula for stratified studies with the standard null hypothesis of unity relative risk as a special case of this formula.
Selecting the best dose when a monotonic dose‐response relation exists
Authors: Russek‐Cohen Estelle , Simon Richard M. 
Published: April 2007

AbstractWe propose a method for selecting the best treatment when a monotonic dose—response relationship exists. Because of side effects associated with higher doses, the highest dose may not be the optimum, particularly when a lower dose gives a similar response. Rather than assume a particular functional relationship of dose to response, we use isotonic regression techniques. We consider the case of three treatment levels, which is applicable to many clinical trials. The lowest treatment level may represent a placebo or no treatment control. While we focus primarily on Bernoulli response variables, we also discuss a model for normally distributed data. We suggest a two‐stage procedure that we have investigated via simulation.
Group sequential testing in clinical trials with multivariate observations: A review
Authors: Lee Jae Won 
Published: April 2007

AbstractMost group sequential testing procedures assume that each patient has only one response. Thus interim test statistics have independent increments. In many clinical trials, however, a response variable is measured at each follow‐up visit. In this article, I review some recently developed group sequential methods for repeated measures and for other types of multivariate responses. Six parametric methods and three non‐parametric methods are included. My review focuses on the comparison of the assumptions made and the applications of each of these methods.
Estimating the variations and autocorrelations in dietary intakes on weekdays and weekends
Authors: Bhargava Alok , Forthofer Ronald , McPherson Susie , Nichaman Milton 
Published: April 2007

AbstractThe high incidence of breast cancer in the U.S. and the possible link with dietary fat has led to the development of educational programmes for reducing women's fat intakes by agencies such as the National Cancer Institute and the U.S. Department of Agriculture. In this paper, we analyse the effects of an intervention on the intakes of 12 nutrients by 37 women in the Houston area. We estimate a dynamic random effects model by maximum likelihood to estimate the between and the within variations and the autocorrelations using 7 consecutive food records before and after the intervention programme. The main findings are that the pattern of within variations differs during weekdays and weekends. Secondly, the mean intakes of nutrients such as β‐carotene and ascorbic acid tend to be lower on weekends. Lastly, the intervention programme reduced the overall fat intakes and also increased the variation in the consumption of foods high in fats during weekdays. We discuss the implications of the results for the design of further studies.
Estimating the accuracy of dietary questionnaire assessments: Validation in terms of structural equation models
Authors: Kaaks Rudolf , Riboli Elio , Estève Jacques , Van Kappel Anne Linda , Van Staveren Wija A. 
Published: April 2007

AbstractThe validity and precision of questionnaire assessments of the habitual intake of individuals are usually evaluated by comparison with reference measurements that are supposed to provide a best possible substitute for the individuals' true intake values. In the present paper, a measurement error model is presented, defining different types of error ‐ random or systematic, and within or between individuals ‐ that may occur in dietary intake measurements. It is then discussed how simple latent variable models (structural equation models) can be used to estimate the average magnitude of these various types of error. So far, approaches described for the analysis of dietary validity studies have all been based on the assumption that the random errors of repeat reference measurements, taken by the same method on different occasions, are uncorrelated, so that the average of a sufficiently large number of repeat reference measurements will provide an accurate ranking of individuals by true intake level. In the present paper it is described how, by additional comparison with a third type of measurement such as a biochemical marker, the validity of dietary questionnaire measurements can be evaluated even in situations where the random errors of repeat reference measurements can no longer be assumed to be independent.
A robust goodness‐of‐fit test statistic with application to ordinal regression models
Authors: Lipsitz Stuart R. , Buoncristiani John F. 
Published: April 2007

AbstractWe propose a goodness‐of‐fit test statistic for linear regression with heterogeneous variance, which is asymptotically chi‐square if the given model is correct. The test statistic is computed as a quadratic form of observed minus predicted responses. We apply the method to a linear regression for an ordinal categorical response, the wheezing status of a child (no wheeze, wheeze with cold, wheeze apart from cold) as a function of maternal smoking and city of residence.
Non‐hierarchical logistic models and case‐only designs for assessing susceptibility in population‐based case‐control studies
Authors: Piegorsch Walter W. , Weinberg Clarice R. , Taylor Jack A. 
Published: April 2007

AbstractThis article describes how genetic components of disease susceptibility can be evaluated in case‐control studies, where cases and controls are sampled independently from the population at large. Subjects are assumed unrelated, in contrast to studies of familial aggregation and linkage. The logistic model can be used to test collapsibility over phenotypes or genotypes, and to estimate interactions between environmental and genetic factors. Such interactions provide an example of a context where non‐hierarchical models make sense biologically. Also, if the exposure and genetic categories occur independently and the disease is rare, then analyses based only on cases are valid, and offer better precision for estimating gene‐environment interactions than those based on the full data.
Median time from seroconversion to AIDS in Italian HIV‐positive haemophiliacs: Different parametric estimates
Authors: Chiarotti Flavia , Palombi Massimo , Schinaia Nicola , Ghirardini Alessandro , Bellocco Rino 
Published: April 2007

AbstractThe purpose of this study was to estimate the median incubation time between human immunodeficiency virus (HIV) infection and onset of acquired immunodeficiency syndrome (AIDS), using three parametric models and six estimates of seroconversion time. Study subjects were 732 HIV‐positive haemophiliacs enrolled in the Italian Registry of patients with congenital coagulation disorders. Seroconversion time was estimated for each subject according to six different criteria, based on three distributions of seroconversion (uniform, uniform on three sub‐intervals and truncated Weibull) and two indices synthesizing each distribution (median and median of three random values). The estimated seroconversion times were subsequently used as starting points in the analysis of incubation. This was performed applying Kaplan‐Meier non‐parametric survival analysis, and fitting to incubation data three probability density functions, representing three different situations with respect to the hazard of developing AIDS following seroconversion (namely Weibull (WE), generalized exponential (GE) and log‐logistic (LL)). The cumulative incidence over an 8‐year period ranged from 14.9 to 17.8 per cent when applying the Kaplan‐Meier method, from 14.1 to 17.2 per cent when using the WE function, from 14.5 to 17.3 per cent when using the GE function and from 14.4 to 17.3 per cent when using the LL function, depending on the estimate of seroconversion time used. Similarly, the median incubation times ranged from 12.6 to 15.0 years with the WE function, from 14.0 to 16.5 years with the GE function, and from 13.4 to 16.1 years with the LL function. The presence of a bound on the increase of the hazard function seems to affect the incubation more strongly than the eventual decrease following the attainment of the maximum risk. This may be due to the decrease in the hazard beginning when most of the seropositive subjects have already developed AIDS.