bluuebunny/crossref_metadata_2025_split huggingface dataset:
The application of time series forecasting methods to an estimation problem using provisional mortality statistics
Authors: Katzoff Myron 
Published: April 2007

AbstractProvisional estimates of mortality for selected causes of death are published each month by the National Center for Health Statistics. These estimates are based upon a ten per cent sample of death certificates in the United States. Final mortality results, based upon all the death certificates for a calendar year, are available one to two years after publication of the provisional estimates. This paper explores the potential of time series forecasting techniques for improving mortality estimates by using the correlation structure between the provisional and final series to obtain mortality estimates that are expected to be closer to final values than currently used provisional estimates.
Time series analysis of syphilis surveillance data
Authors: Zaidi Akbar A. , Schnell Daniel J. , Reynolds Gladys H. 
Published: April 2007

AbstractTo manage a public health programme effectively it is necessary to set objectives and priorities according to the resources available. To utilize available resources optimally in a disease control programme we should both estimate the present and predict the future magnitude of the health problem. The CDC has an effective programme to control syphilis, a sexually transmitted disease. To keep track of control activities, CDC receives the number of syphilis cases from all states every three months. Primary and secondary syphilis cases declined from an all time high of 106,539 cases in 1947 to 6399 cases in 1956. Since then, syphilis cases increased to 27,921 in 1986. Congenital syphilis cases declined from 17,600 in 1941 to 239 in 1983, but increased to 408 in 1986. We built time series models for primary and secondary syphilis cases in men and women and congenital syphilis cases in children under one year of age. These models were used to forecast syphilis cases in all three categories. This analysis suggests that no change in the trend of male syphilis cases has occurred, but syphilis cases in women and congenital syphilis cases in children under one year of age during 1987 have increased.
Covariate imbalance and random allocation in clinical trials
Authors: Senn S. J. 
Published: April 2007

AbstractA model is developed to estimate the effect of covariate imbalance on the size of a test of treatment efficacy in randomized clinical trials comparing two treatments when dispersion parameters are known. It is concluded that tests of homogeneity on the covariates should not be performed, that covariate imbalance is just as much a problem for large studies as for small ones in terms of effect on size, and that the effect of correlation between covariates and measures of efficacy is more complex than has previously been sugested. The best way to adjust for covariate imbalance is by an analysis of covariance.
A comparison of estimated proportional hazards models and regression trees
Authors: Segal Mark R. , Bloch Daniel A. 
Published: April 2007

AbstractWe present examples of the usage of regression trees for censored response via two real world datasets, one a rheumatoid arthritis survival study and the other a hip replacement study, and draw comparisons with the results of Cox proportional hazards modelling. The two methods pursue different goals. Motivation of the tree techniques is the desire to extract meaningful prognostic groups while the proportional hazards model enables assessment of the impact of risk factors. The methods are thus complementary. For the arthritis study the two techniques corroborate one another, although the flavour of the conclusions derived differ. For the hip replacement study, however, the regression tree approach reveals structure that would not emerge from a routine proportional hazards analysis. We also discuss the treatment of data analytic issues such as the handling of missing values and influence in the presence of non‐uniform censoring.
A log‐linear model for ordinal data to characterize differential change among treatments
Authors: Francom S. F. , Chuang‐Stein C. , Landis J. R. 
Published: April 2007

AbstractWe propose a family of log‐linear models for ordinal data that contain parameters reflecting change patterns to compare treatments relative to change from baseline. Under the most general model, rates of change can depend not only upon the direction of change, but also upon the level of the baseline classification. We describe methods for selection of a parsimonious model and for tests of hypotheses concerning treatment differences. Interpretation of treatment differences in the follow‐up response profiles, within baseline strata, employs the concept of stochastic ordering. Data from two clinical trials illustrate the proposed procedure.
Estimation of a common odds ratio in paired‐cluster randomization designs
Authors: Donner Allan , Hauck Walter 
Published: April 2007

AbstractWe develop two estimators of a common odds ratio Ψ for designs in which the investigator randomly assigns each of two clusters to interventions within strata. The estimators rely on an empirical adjustment for clustering to provide improved estimators of Ψ relative to the standard Woolf and Mantel—Haenszel estimators, respectively. The results of a simulation study show that the suggested adjustment improves the accuracy of both of these well‐known estimators under conditions likely to arise in practice. We find the clustered Woolf estimator as particularly effective in terms of mean squared error reduction. We also discuss interval estimation.
The control of confounding by intermediate variables
Authors: Robins James 
Published: April 2007

AbstractIn epidemiologic studies of the effect of an exposure on disease, the crude association of exposure with disease may fail to reflect a causal association due to confounding by one or more covariates. Most previous discussions of confounding in the epidemiologic literature have considered only point exposure studies, that is, studies that measure exposure and covariate status only once, at start of follow‐up. In this paper we offer definitions of confounding suitable for longitudinal studies that obtain data on exposure, covariate, and vital status at several points in time. An important difference between longitudinal studies and point exposure studies is that, in longitudinal studies, a time‐dependent covariate can be simultaneously a confounder and an intermediate variable on the causal pathway from exposure to disease. In this paper I propose an estimator, the extended standardized risk difference, that provides control for confounding by a covariate that is simultaneously a confounder and an intermediate variable.
Graphical generalized residuals in fitting distributions: Applications to epidemiological follow‐up data
Authors: Elandt‐Johnson Regina C. , Smith Fraser B. 
Published: April 2007

AbstractThree kinds of generalized residuals based on probability integral transformation are defined and their roles in graphical goodness of fit testing are discussed. Although they are useful in fitting parametric distributions, their value is questionable, however, in fitting Cox's proportional hazard rate (PHR) models. They almost always appear to give a good fit because of (a) almost non‐parametric nature of the PHR models; (b) heavy right‐hand censoring in epidemiological data; and (c) scale adjustment in graphical presentation. Thus, the apparent overall fit has little inferential meaning, but the residuals are useful in exploratory analysis. A simple method, based on non‐parametric stratified residual plots, for selection of risk factors is discussed. It is, in fact, equivalent to comparison of several empirical survival functions, one for each set of values (stratum) of a covariable under consideration. It is also suggested that for the preliminary PHR model, the continuous variables should be discretized and indicator variables used in the model to check analytically the graphical perception of the possible functional form of the contribution of each covariable to the PHR model.
Regression models for time to seroconversion following experimental bovine leukaemia virus infection
Authors: Lassauzet Marie‐Liesse G. , Johnson Wesley O. , Thurmond Mark C. 
Published: April 2007

AbstractThis paper develops a parametric model for time to seroconversion after experimental bovine leukaemia virus (BLV) infection, and examines the effects of inoculation route, volume of inoculum, type of inoculation material, and antigen status of donor on seroconversion time. We used parametric and nonparametric statistical methodology to analyse interval data on 150 animals from 13 published reports. The log‐logistic model fitted the observed times to seroconversion better than the log‐normal or Weibull models, which were the considered alternatives.
Unidirectional multiple comparisons of poisson rates
Authors: Suissa Samy , Salmi Rachid 
Published: April 2007

AbstractThis paper presents an exact significance test procedure for unidirectional multiple comparisons of Poisson variables. It is sufficiently general for application to situations where the ‘denominator’, which might be the amount of follow‐up time in cohort studies or the expected number of events in studies that use standardized rates, differs from one group to the other, and where a group‐specific alternative hypothesis is of interest. We use unidirectional Z statistics, of the form proposed by Dunnett, along with the conditional distribution needed in computing the exact attained significance levels, to address three distinct unidirectional hypotheses. In one case, we compare one exposed with several reference groups, while in the other two cases, we compare several exposed groups with one reference group. We also provide a multiple stage testing approach. Data from studies in cancer and trauma epidemiology illustrate the procedures.
Regression with an ordered categorical response
Authors: Hastie T. J. , Botha J. L. , Schnitzler C. M. 
Published: April 2007

AbstractA survey on Mseleni joint disease in South Africa involved the scoring of pelvic X‐rays of women to measure osteoporosis. The scores were ordinal by construction and ranged from 0 to 12. It is standard practice to use ordinary regression techniques with an ordinal response that has that many categories. We give evidence for these data that the constraints on the response result in a misleading regression analysis. McCullagh's11proportional‐odds model is designed specifically for the regression analysis of ordinal data. We demonstrate the technique on these data, and show how it fills the gap betweenordinary regressionandlogistic regression(for discrete data with two categories). In addition, we demonstrate non‐parametric versions of these models that do not make any linearity assumptions about the regression function.
Sample sizes for constructing confidence intervals and testing hypotheses
Authors: Bristol David R. 
Published: April 2007

AbstractAlthough estimation and confidence intervals have become popular alternatives to hypothesis testing and p‐values, statisticians usually determine sample sizes for randomized clinical trials by controlling the power of a statistical test at an appropriate alternative, even those statisticians who recommend the use of confidence intervals for inference. There is merit in achieving consistency in the techniques for data analysis and sample size determination. To that end, this paper compares sample size determination with use of the length of the confidence interval with that obtained by control of power.
Statistical analysis of the stages of HIV infection using a Markov model
Authors: Longini Ira M. , Clark W. Scott , Byers Robert H. , Ward John W. , Darrow William W. , Lemp George F. , Hethcote Herbert W. 
Published: April 2007

AbstractWe use a staged Markov model to estimate the distribution and mean length of the incubation period for acquired immunodeficiency syndrome (AIDS) from a cohort of 603 human immunodeficiency virus (HIV) infected individuals who have been followed through various stages of infection. The model partitions the infected period into four progressive stages: (1) infected but antibody‐negative; (2) antibody‐positive but asymptomatic; (3) pre‐AIDS symptoms and/or abnormal haematologic indicator; and (4) clinical AIDS. We also model a fifth stage: death due to AIDS. The estimated mean (median) waiting times in each stage of infection are stage 1, 2·2 (1·5) months; stage 2, 52·6 (36·5) months; stage 3, 62·9 (43·6) months; and stage 4, 23·6 (16·3) months. We estimate the mean AIDS incubation period (from infection to development of clinical AIDS) as 9·8 years with a 95 per cent confidence interval of [8·4, 11·2] years. The paper also considers the estimated density function of the AIDS incubation period and the estimated survival functions for individuals in each stage of infection. This work represents one of the most complete statistical descriptions to date of the natural history of HIV infection.
A linear regression model for the analysis of life times
Authors: Aalen Odd O. 
Published: April 2007

AbstractA linear model is suggested for the influence of covariates on the intensity function. This approach is less vulnerable than the Cox model to problems of inconsistency when covariates are deleted or the precision of covariate measurements is changed. A method of non‐parametric estimation of regression functions is presented. This results in plots that may give information on the change over time in the influence of covariates. A test method and two goodness of fit plots are also given. The approach is illustrated by simulation as well as by data from a clinical trial of treatment of carcinoma of the oropharynx.
Bootstrapped confidence intervals for the Cox model using a linear relative risk form
Authors: Barlow William E. , Sun Wei‐Huei 
Published: April 2007

AbstractA linear relative risk form for the Cox model is sometimes more appropriate than the usual exponential form. The usual asymptotic confidence interval may not have the appropriate coverage, however, due to flatness of the likelihood in the neighbourhood of β. For a single continuous covariate, we derive bootstrapped confidence intervals with use of two resampling methods. The first resamples the original data and yields both one‐step and fully iterated estimates of β. The second resamples the score and information quantities at each failure time to yield a one‐step estimate. We computed the bootstrapped confidence intervals by three different methods and compared these intervals to one based on the asymptotic standard error and to a likelihood‐based interval. The bootstrapped intervals did not perform well and underestimated the true coverage in most cases.
Exponential survival trees
Authors: Davis Roger B. , Anderson James R. 
Published: April 2007

AbstractThis paper concerns a recursive partitioning algorithm for incomplete survival data. The splitting criterion employed is exponential log‐likelihood loss. The method has been integrated into a computer program similar in structure to the commercial package CART. Special features incorporated into the program include a modification to prevent zero hazard estimates during the cross‐validation procedure and a method based on the chi‐square distribution for final tree selection. Simulation results demonstrate the program's capability to identify the underlying hazard structure in survival data. An example utilizing data from patients with diffuse large cell lymphoma is presented.
An overview of issues related to the correction of non‐differential exposure measurement error in epidemiologic studies
Authors: Willett Walter 
Published: April 2007

AbstractProcedures to correct estimates of association in epidemiologic studies for the effects of exposure measurement error have rarely been employed in practice. The application of correction procedures would be enhanced by methods that allow the inclusion of covariates, provide corrected confidence intervals, are compatible with commonly employed analytic methods, and that are clearly communicated to potential users. Before using such a procedure, it is important to clearly specify the conceptual ‘true’ exposure, determine the nature of the measurement error, and decide whether a reproducibility study or validity study is required to quantify the error. The careful use of correction procedures promises to improve our knowledge of the quantitative relationships between many exposures and disease since it is likely that we have substantially underestimated the effects of many exposures and overstated our confidence in null results.
Covariance analysis in generalized linear measurement error models
Authors: Caroll Raymond J. 
Published: April 2007

AbstractWe summarize some of the recent work on the errors‐in‐variables problem in generalized linear models. The focus is on covariance analysis, and in particular testing for and estimation of treatment effects. There is a considerable difference between the randomized and non‐randomized models when testing for an effect. In randomized studies, simple techniques exist for testing for a treatment effect. In some instances, such as linear and multiplicative regression, simple methods exist for estimating the treatment effect. In other examples such as logistic regression, estimating a treatment effect requires careful attention to measurement error. In non‐randomized studies, there is no recourse to understanding and modelling measurement error. In particular ignoring measurement error can lead to the wrong conclusions, for example the true but unobserved data may indicate a positive effect for treatment, while the observed data indicate the opposite. Some of the possible methods are outlined and compared.
A measurement‐error model for binary and ordinal regression
Authors: Tosteson Tor D. , Stefanski Leonard A. , Schafer Daniel W. 
Published: April 2007

AbstractExposure assessment poses special problems in air pollution epidemiology. This paper proposes a probit regression model for binary and ordinal outcomes that uses exposure validation information to develop estimates for the coefficient of the true exposure when only the inaccurate ‘surrogate’ measure of exposure is available for the individuals in the health study. This method is closely related to recently developed measurement‐error methods, and is based on the assumption that the outcome and the surrogate exposure are conditionally independent given the true exposure. A test statistic is proposed for checking this conditional independence assumption when more than one surrogate is available, and an interpretation of the coefficient estimate is provided in the event that the assumption is violated. The methods are applied to an example involving nitrogen dioxide exposure and wheeze in children.
Further results on covariate measurement errors in cohort studies with time to response data
Authors: Pepe Margaret Sullivan , Self Steven G. , Prentice Ross L. 
Published: April 2007

AbstractThe impact of covariate measurement errors on the estimation of relative risk regression parameters is discussed. First the dependence of the induced relative risk process on the cumulative baseline failure rate function is noted. Next induced relative risk models under some specific failure time and measurement error models are described, including the much simplified models that are appropriate under a ‘rare disease’ assumption. The presentation then turns to the joint estimation of relative risk parameters of primary interest along with measurement error parameters. A partial likelihood product is proposed for such estimation and asymptotic properties are indicated. Guidance is also presented as to the appropriate size of a ‘validation’ sample relative to the full cohort size. Finally some more general considerations are presented as to the usefulness and interpretation of deattenuated regression coefficients.
Correspondence analysis as a screening method for indicants for clinical diagnosis
Authors: Crichton Nicola J. , Hinde John P. 
Published: April 2007

AbstractIn clinical diagnosis, a patient's symptoms are observed and the probabilities of various diseases are assessed. A widely used method of formalizing this approach is independent Bayes in which symptoms are assumed to be independent conditional on the disease category. Correspondence analysis provides a method for examining the dependence between symptoms and assists in the selection of a reduced set of symptoms for the application of the independent Bayes method. This approach is illustrated on two data sets concerned with patients attending Accident and Emergency departments with chest pain and acute abdominal pain, respectively.
On the regression analysis of tumour recurrence rates
Authors: Seeber G. U. H. 
Published: April 2007

AbstractRegression models with mixture (random) components are proposed for the statistical analysis of recurrent events when waiting times between successive events are unknown. These models allow adjustment of parameter estimates for unobserved heterogeneity in the population (due for example to missing covariates) or overdispersion resulting from inexact distributional assumptions. The models are illustrated by a study of recurrence rates of superficial bladder cancer in men.
Constructing a cut‐off point for a quantitative diagnostic test
Authors: Schäfer Helmut 
Published: April 2007

AbstractThis paper is concerned with the problem of constructing a suitable cut‐off point for a quantitative diagnostic test from a sample of diseased and non‐diseased individuals, when the specificity and/or sensitivity required for the resulting diagnostic classification rule are specified. Statistical procedures are discussed which yield an explicit cut‐off point, together with the necessary confidence limits on the true specificity and sensitivity of the test operating at this point (by contrast to confidence limits referring to an unknown optimal choice of the cut‐off point). Sample size formulae are derived, showing a relative efficiency of up to 1.5 for a new procedure introduced in this paper over the usual procedure based on tolerance limits.
Estimation of haemophilia‐associated AIDS incidence in Japan using individual dates of diagnosis
Authors: Tango Toshiro 
Published: April 2007

AbstractThis paper presents a procedure for obtaining short‐term projections and lower bounds on the size of the acquired immunodeficiency syndrome (AIDS) epidemic. The method is similar to that proposed by Brookmeyer and Gail1, 2 but adapted to the situation where individual dates of AIDS diagnosis are available. It gives results that are not dependent on the choice of time intervals by contrast to the procedure proposed by Brookmeyer and Gail which was based on the tabulated numbers of AIDS cases. Applications are illustrated with haemophilia‐associated AIDS incidence in Japan where only 45 cases have been diagnosed by 31 December 1987.
Misclassification in more than one factor in a case‐control study: A combination of Mantel‐Haenszel and maximum likelihood approaches
Authors: Duffy S. W. , Rohan T. E. , Day N. E. 
Published: April 2007

AbstractMisclassification can arise in the case‐control setting in both the risk factor of interest, and in a potential confounder. If true status is not ascertainable, but an independent replicated sample is available, adjusted maximum likelihood estimates of the individual binomial proportions can be obtained, and hence a relative risk estimate of the Mantel—Haenszel type constructed. An interval estimate on the latter can be calculated using the asymptotic properties of the likelihood function. The method is illustrated by application to a case‐control study of breast cancer risk and such lifestyle attributes as diet, smoking and alcohol consumption, in which misclassification was anticipated and consequently repeatability data were collected.