bluuebunny/crossref_metadata_2025_split huggingface dataset:
A statistical method for assessing a threshold in epidemiological studies
Authors: Ulm K. 
Published: April 2007

AbstractI describe a method for estimating and testing a threshold value in epidemiological studies. A threshold effect indicates an association between a risk factor and a defined outcome above the threshold value but none below it. An important field of application is occupational medicine where, for a lot of chemical compounds and other agents which are non‐arcinogenic health hazards, so‐alled threshold limit values or TLVs are specified. The method is presented within the framework of the logistic regression model, which is widely used in the analysis of the relationship between some explanatory variables and a dependent dichotomous outcome. In most available programs for this and also for other models the concept of a threshold is disregarded. The method for assessing a threshold consists of an estimation procedure using the maximum‐likelihood technique and a test procedure based on the likelihood‐ratio statistic R, following under the null hypothesis (no threshold) a quasi one‐sided X2 distribution with one degree of freedom. This use of this distribution is supported by a simulation study. The method is applied to data from an epidemiological study of the relationship between occupational dust exposure and chronic bronchitic reactions. The results are confirmed by bootstrap resampling.
Determining efficacy of monitoring devices: Evaluating new technologies
Authors: Tolley Elizabeth A. , Somes Grant W. , Willey Elaine S. , Phelps Stephanie J. 
Published: April 2007

AbstractWe point out the connection between electronic monitoring and repeated screening tests, and suggest a flexible and practical method to determine the efficacy of monitoring devices and new technologies. We point out the benefits of the Mantel‐Haenszel estimator of an odds ratio for determining diagnostic test indices. From estimates of sensitivities and specificities, we produce receiver operating characteristic (ROC) curves to compare the performance of two or more monitoring devices in a clinical setting.
The effect of non‐differential outcome misclassification on estimates of the attributable and prevented fraction
Authors: Hsieh Chung‐heng 
Published: April 2007

AbstractThis paper considers the effect of non‐differential outcome misclassification on the population attributable fraction and the population prevented fraction. I examine the bias in the attributable and the prevented fraction derived from a risk ratio estimate as a function of the sensitivity and specificity of the outcome classification, the true risk ratio, the prevalence of the exposure, and the baseline disease frequency. With outcome misclassified, disease frequency is an important determinant of the magnitude of the bias; the rarer the disease, the more severe is the bias. For both the attributable and the prevented fraction, the specificity of the outcome classification has a greater influence on the magnitude of the bias than the sensitivity; this is in contrast to the dominant effect of sensitivity in situations of exposure misclassification. Also, unlike the findings in the exposure misclassification, the bias due to outcome misclassification does not increase monotonically with increased prevalence of exposure. For the attributable and prevented fraction derived from an odds ratio estimate, the specificity of the outcome classification does not have a greater influence on bias than the sensitivity, and a perfect specificity alone does not lead to unbiased effect estimates if the sensitivity of the outcome classification is imperfect.
Death after marital bereavement‐is the risk increased?
Authors: Jagger Carol , Sutton Christopher J. 
Published: April 2007

AbstractPrevious studies on the effect of marital bereavement on mortality have suggested various time periods during which the risk of mortality is increased. As many of the studies compared the widowed group with national mortality statistics for the married, there has been no opportunity to adjust for confounders which might themselves be responsible for this increased risk after bereavement. In this paper the various hypotheses proposed are reviewed and then modelled on a dataset of 344 elderly persons who were living with a spouse and who were part of a survey of a population of people aged 75 years and over. The 344 index‐ases and their spouses were followed up for seven years and the times of death (for those who died) of the index‐ase and spouse were noted. The data were analysed by fitting a proportional hazards model to the subject's survival time after adjustment for other factors such as mental and physical health which had already been shown to be associated with mortality. The bereavement effects were fitted as time‐dependent covariates. The best fitting model for females and males indicated an increased relative risk of mortality which lasted for approximately six months after bereavement. In the case of widows this relative risk was significantly increased, being 3.8 with 95 per cent confidence interval (1.4, 10.3) while for widowers the risk was 0.03 with 95 per cent confidence interval (0.00, 37.3).
A qualifier Q for the survival function to describe the prevalence of a transient condition
Authors: Pepe Margaret Sullivan , Longton Gary , Thornquist Mark 
Published: April 2007

AbstractWe propose an estimator of the prevalence of a transient condition among surviving patients using right censored data. The prevalence of opportunistic infection among surviving AIDS patients and the probability of being in tumour response following cancer therapy conditional on being alive are two examples of such functions. In essence these functions describe a major aspect of the quality of life for surviving patients and may be useful when viewed in conjunction with the survival curves themselves. The method is illustrated using data from a randomized trial of bone marrow transplant patients where the prevalence of chronic graft‐versus‐host disease is of interest. The non‐parametric estimator which we have proposed is contrasted with estimators derived from Markov and semi‐Markov models.
Confidence intervals for post‐test probability
Authors: Monsour Michael J. , Evans Arthur T. , Kupper Lawrence L. 
Published: April 2007

AbstractConfidence intervals are a natural way to describe the uncertainty of post‐test probability in diagnostic tests. We consider confidence intervals for two different scenarios. At a site, for example, hospital emergency room or student health centre, with measured values of disease prevalence, sensitivity and specificity available, the confidence interval is similar to results in the literature, but at a site where measured values of these indices are unavailable, we develop a method, using the values of disease prevalence, sensitivity and specificity from other sites, to obtain a confidence interval for post‐test probability. We use the diagnosis of strep throat to illustrate the results. We also obtain confidence intervals from simulations to compare with the results of both scenarios.
Sample sizes for repeated measurements in dichotomous data
Authors: Lui Kung‐Jong 
Published: April 2007

AbstractWhen the measurement of outcome varies within studied subjects and the cost of additional subjects is high, taking more than one measurement for each subject constitutes a useful alternative to increase the power or to reduce the total cost of the study. In this paper, I present sample size formulae for repeated measurements in dichotomous data under different situations. I also discuss optimal sample allocation for repeated measurements.
Using routinely collected data for clinical research
Authors: Safran Charles 
Published: April 2007

AbstractClinical research involving prospective data collection in randomized controlled trials is not always feasible. Increasingly, hospitals are developing large clinical databases that are waiting to be mined. We have developed a computer program, ClinQuery, that facilitates such exploration and analysis. We have also shown in a series of studies that the use of clinical data is a powerful tool in health services research. In some cases, we have shown that coded data are inaccurate and that alternative clinical data are preferable. In other cases, a combination of clinical data and coded discharge diagnoses is preferable.
Methods for the analysis and assessment of clinical databases: The clinician's perspective
Authors: Pryor David B. , Lee Kerry L. 
Published: April 2007

AbstractInnovative approaches to analysing clinical databases can be considered from a perspective of innovations that improve the analytic approach or from a more global perspective in which clinical databases themselves are evaluated as a technology. The analytic approach for using a database to estimate risk can be considered as a matrix of three methodologic concerns: the predictive method; the assessment of the quality of the predictions; and the assessment of the validity or generalizability of the predictions. Considering databases as a technology places in perspective the merit of clinical databases and defines their potential value to the health care system. An awareness of both the clinical and analytic problem encourages innovation and can lead to creative solutions to the many problems present in the analysis of clinical databases.
Using databases to evaluate therapy
Authors: Hlatky Mark A. 
Published: April 2007

AbstractClinical and administrative databases are increasingly used for clinical research. Descriptive studies and analyses of prognostic factors are established research uses of databases, but using them to compare therapies remains controversial. Such comparisons may be possible when a validated model of prognosis can account for the effect of clinically recognized variables on outcome. Models meeting these criteria have been developed using the Duke Cardiovascular Disease Database. They were applied to compare medical therapy to surgical therapy for coronary artery disease. Predictions from these models agreed well with the results of the three major randomized trials of bypass surgery versus medical therapy. These findings indicate that when data is reliable and understanding of factors affecting prognosis is good, researchers can use statistical techniques to minimize the bias due to non‐random treatment assignment. Carefully performed analyses of observational clinical data can complement and extend randomized studies.
Constructing time‐specific reference ranges
Authors: Royston Patrick 
Published: April 2007

AbstractReference ranges which take time (such as age) into account are often required in medicine, but simple, systematic and efficient statistical methods for constructing them are lacking. A method is described which is based on low order polynomial curves (linear, quadratic or occasionally cubic), together with guidelines for when and how to apply a logarithmic transformation to the variable analysed, testing for departures from normality, and assessment of the adequacy of the reference range which is constructed from the regression line plus or minus a multiple of the standard deviation. Standard statistical packages may be used to carry out the calculations. The question of comparing two or more groups of patients is addressed. Three examples are discussed in detail.
Estimation of reference ranges from normal samples
Authors: Royston P. , Matthews J. N. S. 
Published: April 2007

AbstractReference ranges are very widely used in medicine, and are typically computed as mean plus or minus two standard deviations. However, their precise statistical basis is rarely discussed, and is open to different interpretations. They are sometimes calculated using the Student t distribution and satisfy the condition that the expected coverage of the reference population is some nominal value such as 95 per cent or 99 per cent. A case is made for using reference ranges which are based on estimating symmetric centiles of the underlying normal distribution, and appropriate formulae are given. The simplest such estimator, the sample mean plus or minus the normal‐based number of sample standard deviations, is shown to have both theoretical and practical advantages.
A proposal for examining and reporting stepwise regressions
Authors: Hauck Walter W. , Miike Rei 
Published: April 2007

AbstractA common mode of regression analysis is a stepwise regression. One of the problems associated with the stepwise algorithm is that the data analyst may wrongly identify the selected variables as the important variables. We propose a simple table as an aid in examining and reporting stepwise regression analyses. The key to our proposal is recognition of ‘close alternatives’, that is, those variables excluded from selection by the inclusion of another variable. This presentation should encourage the analyst and reader to think in terms of sets of variables that, together, represent factors associated with the outcome.
The effects of model selection on confidence intervals for the size of a closed population
Authors: Regal Ronald R. , Hook Ernest B. 
Published: April 2007

AbstractOne encounters in the literature estimates of some rates of genetic and congenital disorders based on log‐linear methods to model possible interactions among sources. Often the analyst chooses the simplest model consistent with the data for estimation of the size of a closed population and calculates confidence intervals on the assumption that this simple model is correct. However, despite an apparent excellent fit of the data to such a model, we note here that the resulting confidence intervals may well be misleading in that they can fail to provide an adequate coverage probability. We illustrate this with a simulation for a hypothetical population based on data reported in the literature from three sources. The simulated nominal 95 per cent confidence intervals contained the modelled population size only 30 per cent of the time. Only if external considerations justify the assumption of plausible interactions of sources would use of the simpler model's interval be justified.
A note on the calculation of expected survival, illustrated by the survival of liver transplant patients
Authors: Thomsen Birthe Lykke , Keiding Niels , Altman Douglas G. 
Published: April 2007

AbstractBased on historical regression analyses of survival one may calculate expected survival curves for patients in a study group, based on their observed covariates. This note discusses this calculation and illustrates it on a comparison of the survival of a certain group of liver transplant patients to their prognosis under conservative treatment.
Analytic methods for two‐stage case‐control studies and other stratified designs
Authors: Flanders W. Dana , Greenland Sander 
Published: April 2007

AbstractNested case‐control studies, or case‐control studies within a cohort, combine the advantages of cohort studies with the efficiency of case‐control studies. Case‐control studies can often be viewed as having two stages; the first stage consists of vital status, disease, and basic covariate ascertainment, and the second stage consists of detailed covariate and exposure ascertainment. Breslow and Cain (1988) and Breslow and Zhao (1988) recently showed that conventional analyses of such two‐stage studies may ignore some of the available information. In this paper, we show how one can adapt the pseudo‐likelihood analyses developed by Kalbfleisch and Lawless (1988) to the analysis of data from two‐stage case‐control studies.
The construction of biochemical reference ranges and the identification of possible adverse drug reactions in the elderly
Authors: Lawrence C. J. , Trewin V. F. 
Published: April 2007

AbstractIn the assessment of adverse drug reactions (ADRs) it is important to establish what is a normal range of values for a given biochemical measurement. Furthermore, such ranges need to be specific to the study population, for example, the elderly. A mixture of Gaussian distributions has been used in the construction of the reference ranges with maximum likelihood used to obtain estimates of the unknown parameters. With this approach, well‐defined reference ranges have been established. Brief details are given of the numbers of ADRs identified in the study population using these ranges.
On the use of historical controls in the analysis of laboratory data
Authors: Yoshimura Isao , Hayashi Makoto 
Published: April 2007

AbstractOur purpose is to propose a method of data analysis incorporating historical controls. It consists of three steps of hypothesis testing procedure. The first step is the check of concurrent controls. The second is the significance testing of treatment groups in comparison with historical controls which are composed of negative and positive controls. The third is the significance testing of a linear trend of the dose‐response relationship. The superiority of the proposed procedure is also verified.
Multiple testing in clinical trials
Authors: Bauer P. 
Published: April 2007

AbstractThe basic ideas of multiple testing are outlined and the problem of how to control the probability of erroneous decisions is discussed. The main emphasis is on the concept of the multiple level of significance (controlling the experiment, or family error in the strong sense) which can be achieved by applying the principle of closed tests. Various practical situations encountered in multiple testing in clinical trials are considered: more than one end point; more than two treatments, such as comparisons with a single control, comparisons using ordered alternatives, all pairwise comparisons and contrast methods; and more than one trial. Tests based on global statistics, the union intersection principle and other criteria are discussed. The application of the multiple test concept in sequential sampling is investigated. Finally some comments are made on multiple power, multiple confidence intervals and directed decisions.
A random effects model for ordinal responses from a crossover trial
Authors: Ezzet Farkad , Whitehead John 
Published: April 2007

AbstractCrossover studies have been successfully conducted in the case of continuous responses. Existing procedures of analysis for ordinal responses, on the other hand, are rarely satisfactory unless strict, usually unrealistic, assumptions are made. In this paper we investigate a random effects model and show that the model is simple and general. Interpretation of parameters is easy, though with a complicated fitting procedure.
Interim analysis in clinical trials
Authors: Armitage Peter 
Published: April 2007

AbstractThe early development of experimental design discouraged a sequential approach to the analysis of data, yet this seems a natural form of scientific enquiry. Clinical trial investigators should continuously monitor the quality of their techniques, but will often wish to delegate data monitoring to an independent group. The history and functions of data monitoring committees (DMCs) are reviewed. DMCs come in many shapes and sizes. They will need to consider many aspects of the data before making recommendations to the investigators, who have ultimate responsibility for early termination or protocol changes.Statistical issues form part of the assessment, and will involve management, safety and efficacy. Two broad approaches to early stopping are (i) the demonstration of strong evidence that a treatment effect falls above or below some critical value (not necessarily zero); (ii) stochastic curtailment based on prediction of final results. The latter is examined somewhat critically. Most trials will involve group sequential analyses at discrete time points. The effect of repeated data inspection on (i) is well known, although its relevance is debatable. Bayesian and likelihood methods do not entirely remove the difficulty.
Step‐down multiple tests for comparing treatments with a control in unbalanced one‐way layouts
Authors: Dunnett Charles W. , Tamhane Ajit C. 
Published: April 2007

AbstractWe show how a well‐known multiple step‐down significance testing procedure for comparing treatments with a control in balanced one‐way layouts can be applied in unbalanced layouts (unequal sample sizes for the treatments). The method we describe has the advantage that it provides p‐values, for each treatment versus control comparison, that take account of the multiple step‐down testing nature of the procedure. These joint p‐values can be used with any value of α, the fixed type I familywise error rate bound, that may be specified by the investigator. To determine the p‐values, it is necessary to compute a multivariate Student t integral, for which a computer program is available. This procedure is more powerful than the step‐down Bonferroni procedure of Holm1 and the single‐step procedure of Dunnett.2 An example from the pharmaceutical literature is used to illustrate the procedure.
Problems induced by meta‐analyses
Authors: Chalmers Thomas C. 
Published: April 2007

AbstractOver 150 meta‐analyses of randomized control trials have so far been published in the English language, and new ones are appearing at a rate of over fifteen per year. This explosion of a relatively new method of evaluating clinical medicine presents a number of challenges to statisticians and those responsible for health care policy. The pitfalls of retrospective research must be avoided, and the quality of the original trials should be evaluated. Heterogeneity of the control event rates and the treatment differences need to be dealt with statistically. Most important are the problems raised by the need to update meta‐analyses as new trials are published.
Maximum likelihood techniques applied to method comparison studies
Authors: Nix A. B. J. , Dunstan F. D. J. 
Published: April 2007

AbstractOne component of method comparison studies is the assessment of relative bias. Most authors use ordinary regression techniques for this purpose. The potential inadequacies in this approach are highlighted and a more generally applicable maximum likelihood technique proposed. The properties of the various methods of estimation are obtained by means of a simulation study, using a variety of different within‐assay precision profiles.
The potential for spread of HIV in the heterosexual population in Norway: A model study
Authors: Stigum Hein , Grønnesby J. K. , Magnus P. , Sundet J. M. , Bakketeig L. S. 
Published: April 2007

AbstractA simulation model is used to investigate the potential for indigenous spread of human immunodeficiency virus (HIV) in the heterosexual population in Norway. The heterosexual population is grouped according to age, sex and whether paired or single. Estimates of various parameters of sexual behaviour are taken from a population‐based questionnaire study. The questionnaire was sent to a random sample of 10,000 individuals, with a response rate of 63 per cent. The main simulation result is that with no inflow of infection from other risk groups, the heterosexual epidemic will decline if the average transmission probability per intercourse is 1 per cent or less. This result is insensitive to changes in the initial conditions. It is sensitive to changes in sexual behaviour, and to the shape of the transmission probability distribution. A review of empirical partner study data indicates an average transmission probability about 0.1 per cent per intercourse when no transmission modulating cofactors are present. It is concluded that with present sexual behaviour, the indigenous spread of HIV is not likely to sustain an epidemic in the Norwegian heterosexual population.