bluuebunny/crossref_metadata_2025_split huggingface dataset:
Designing an optimal experiment for Bayesian stimation: Application to the Kinetics of iodine thyroid uptake
Authors: Merlé Y. , Mentré F. , Mallet A. , Aurengo A. H. 
Published: April 2007

AbstractWe consider the problem of designing an optimal experiment for Bayesian estimation of the parameters of a non‐linear model. When their distribution is known, the Bayesian approach allows individual estimation from a small number of measurements; the design determines the accuracy of the estimates. We propose to optimize this design by maximizing a general criterion: the expectation of the information supplied by the experiment. This approach is applied to optimize the two sampling times for Bayesian estimation of the kinetics of radioiodine thyroid uptake from an estimated non‐parametric prior distribution.
Foundations of medical diagnosis: What actually are the parameters involved in Bayes' theorem?
Authors: Miettinen Olli S. , Caro J. Jaime 
Published: April 2007

AbstractThree decades ago, the thesis was adduced that setting diagnostic probabilities requires, by the inherent nature of diagnosis‐pertinent medical knowledge, the use of Bayes' theorem. That paper was both vague and inconsistent in its delineation of the nature of the parameters involved in this formulation, and subsequent authors have only added to the confusion. Nevertheless, that thesis has been, and continues to be, enthusiastically embraced by clinical scholars. We here posit what those parameters must be taken to represent in principle; and this explication reveals that their quantification poses generally unsurmountable epistemologic challenges. The implication of this is not that informed setting of diagnostic probabilities is generally infeasible. Our conclusion is, instead, that the seminal thesis was founded on an untenable pair of premises about the nature of scientifically attainable knowledge pertinent to diagnosis.
Fisher's game with the devil
Authors: Senn Stephen 
Published: April 2007

AbstractThe publication of Fisher's correspondence1on statistics has shed new light on his views on randomization. Quotations from this correspondence and from other works of Fisher are used to illustrate the role of randomization in clinical trials. It is concluded that Fisher's views not only are coherent but, despite having been developed over 60 years ago and with particular reference to agricultural experiments, are still relevant to the planning and analysis of clinical trials today.
Estimation of time since infection using longitudinal disease‐marker data
Authors: Dubin Neil , Berman Simeon , Marmor Michael , Tindall Brett , Jarlais Don Des , Kim Mimi 
Published: April 2007

AbstractWe propose a method to estimate the usually unknown time since infection for individuals infected with human immunodeficiency virus type 1 (HIV‐1). If we assume the time since infection has an exponential prior distribution, then under the model the conditional distribution of time since infection, given the CD4 level at the time of the first positive HIV‐1 antibody test, is a truncated normal density. We applied the method to prevalent cohort data both from intravenous drug users and from homosexual/bisexual men. For the intravenous drug users the estimated mean time since infection was 15.0 months from infection at a presumed mean CD4 level of 1060 cells/ml to first positive antibody test at a CD4 level of 597 cells/ml, which was the average CD4 at enrolment for infected subjects. For the homosexual/bisexual men the estimated mean time since infection was 16.7 months from infection at a presumed mean CD4 level of 699 cells/ml to first positive antibody test at an average CD4 level of 577 cells/ml. We performed a validation study using initially seronegative subjects in these cohorts who seroconverted to HIV‐1‐positive antibody status during the follow‐up period. For the intravenous drug users, data were too few to provide definitive verification of the method. In the cohort of homosexual/bisexual men, however, there was a total of 70 seroconverters with relevant data. Among them, the median absolute difference between the midpoint of the known seroconversion interval and the estimated mean infection date was 4.6 months, conditional on CD4‐lymphocyte measurements taken approximately 18 months subsequent to infection. Conditional on CD4 approximately 30 months after infection, this median difference increased modestly to 8.2 months. Our analysis suggested that the underlying mathematical model tends to overestimate short times since infection and underestimate long times since infection. We consider potential corrective modifications to the model.
Comparative exposure ratios: A non‐parametric, multifactor technique for case‐control studies
Authors: Aickin Mikel , Ritenbaugh Cheryl , Surwit Earl , Meyskens Frank 
Published: April 2007

AbstractThe odds ratio in a two‐by‐two table is widely used in case‐control studies to measure association between disease and a binary risk factor. In this article we propose a more general measure of association, the comparative exposure ratio (CER), which is the ratio of the number of case‐control pairs where the case has greater exposure divided by the number where the control has greater exposure. In simple cases, the CER is an odds ratio or a weighted combination of odds ratios. In more general cases a CER continues to measure association even when an odds ratio computation is not feasible. Moreover, CERs improve on odds ratios in several ways: they do not require binary risk factors, or a choice of the scale of measurement of continuous risk factors; they make it possible to investigate multiple risk factors simultaneously, without multivariate parametric assumptions; they also can be used to detect patterns that might indicate possible causal pathways. We illustrate how various choices of the definition of ‘greater exposure’ make the CER a powerful and flexible tool. We give expressions for confidence intervals for CERs, and verify in a pilot simulation that they are valid. Finally, we illustrate with a case‐control study of cervical dysplasia how exploratory inference using CERs can be carried out. (This research was partially supported by grants from the National Cancer Institute, CA 41108 and CA 25702).
A geometric approach to the analysis of physiological flow data
Authors: Shih Weichung Joseph 
Published: April 2007

AbstractPhysiological flow data are common in various medical fields. Examples include urinary, blood and expiratory flows. They are widely used in assessing functions in the urinary, circulatory, or pulmonary systems, respectively. Current statistical methods for analysing these flow data in clinical trials are either univariate analyses, which do not utilize all the information together, or some conventional multivariate methods (such as regression analyses) which yield results that do not render clear medical interpretations. This paper presents a new approach to analysing the flow data, using urinary flow as the primary focus. The basic idea and technical steps are applicable to other flow data as well. The proposed method aims to transform the flow measurements back to the shape of the flow graphs. Since the whole geometric pattern of the flow graph provides more information about the patient's flow condition than any individual flow parameter alone, the method is a meaningful way of combining and analysing the flow data in both statistical and clinical senses. The method is a three‐stage procedure. Patients are classified into three classes in the first stage and then ranked in sequence in the second stage, according to the geometry of the shape pattern and some clinical criteria. The classification procedure is shown to be very reliable when compared with the clinician's visual evaluation, and hence can be implemented by computer programming to aid clinical trials involving many patients. The whole ranking score is then readily analysed at the third stage for comparing treatment effects by the analysis of covariance method based on ranks, with the post‐treatment score as the response variable and the baseline score as the covariate. An example of a urinary flow data set is provided to illustrate the use of the procedure.
Analysis of the wideband external pulse: An application of graphical methods
Authors: Denby Lorraine , Mallows Colin L. , Blank Seymour G. 
Published: April 2007

AbstractThis paper demonstrates the power of statistical graphics in gleaning information from very large medical data sets. In the case of the particular data set described here (the wideband external pulse signal) little was known about the shape of the signal and the relationship of that shape to the characteristics of the related physical system, the cardiovascular system. Previous numerical statistical analyses of the data were unfruitful in finding the important relationships which were easily discovered through the appropriately designed graphics. The paper also describes the process of designing graphics to help in discovering the important relationships in the data.
Practical problems in fitting a proportional hazards model to data with udated measurements of the covariates
Authors: Altman Douglas G. , De Stavola Bianca L. 
Published: April 2007

AbstractWe review and discuss the practical problems encountered when analysing the effect on survival of covariates which are measured repeatedly over time. Specific issues arise over and above those met with the standard proportional hazards model and concern all stages of data preparation, data analysis and interpretation of the results. Data from a randomized clinical trial of patients with primary biliary cirrhosis, on whom several measurements were taken at regular intervals after entry, are presented as an illustration.
Application of sample survey methods for modelling ratios to incidence densities
Authors: Lavange Lisa M. , Keyes Lynette L. , Koch Gary G. , Margolis Peter A. 
Published: April 2007

AbstractWe describe ratio estimation methods for multivariately analysing incidence densities from prospective epidemiologic studies. Commonly used in survey data analysis, these ratio methods require minimal distributional assumptions and take into account the random variability in the at‐risk periods. We illustrate their application with data from a study of lower respiratory illness (LRI) in children during the first year of life. One question of interest is whether children with passive exposure to tobacco smoke have a higher rate of LRI, on average, than those with no exposure and in a setting where age of child and season are taken into account. A second question is whether the relationship persists after adjusting for background variables such as family's socioeconomic status, crowding in the home, race, and type of feeding. The basic strategy consists of a two‐step process in which we first estimate subgroup‐specific incidence densities and their covariance matrix via a first‐order Taylor series approximation. These estimates are used to test for differences in marginal rates of LRI between children exposed to tobacco smoke and those not exposed. We then fit a log‐linear model to the estimated ratios in order to test for significant covariate effects. The ability to produce direct estimates of adjusted incidence density ratios for risk factors of interest is an important advantage of this approach. For comparison purposes and to address the limitations of the ratio method with respect to the number of covariates that can be controlled simultaneously, we consider survey logistic regression methods for the example data as well as logistic and Poisson regression models fitted via generalized estimating equation methods. Although the analysis strategy is illustrated with illness data from an epidemiologic study, the context of application is broader and includes, for example, data on adverse events from a clinical trial.
Exposure efficacy and change in contact rates in evaluating prophylactic HIV vaccines in the field
Authors: Halloran M. Elizabeth , Longini Ira M. , Haber Michael J. , Struchiner Claudio J. , Brunet Robert C. 
Published: April 2007

AbstractField studies of the efficacy of prophylactic vaccines in reducing susceptibility rely on the assumption of equal exposure to infection in the vaccinated and unvaccinated groups. Differential exposure to infection could, however, be the goal of other types of intervention programme, or it could occur secondary to belief in the protective effects of a prophylactic measure, such as vaccination. We call this differential exposure the exposure efficacy, or behaviour efficacy. To study the relative contribution of unequal exposure to infection and differential susceptibility to the estimate of vaccine efficacy, we formulate a simple model that explicitly includes both susceptibility and exposure to infection. We illustrate this on the example of randomized field trials of prophylactic human immunodeficiency virus vaccines. Increased exposure to infection in the vaccinated group may bias the estimated reduction in susceptibility. The bias in the estimate depends on the choice of efficacy parameter, the amount of information used in the analysis, the distribution and level of protection in the population, and the imbalance in exposure to infection. Sufficient increase in contacts in the vaccinated could result in the vaccine being interpreted as having an immunosuppressive effect. Estimates of vaccine efficacy are generally more robust to imbalances in exposure to infection when the detailed history of exposure to infection can be used in the analysis or at high levels of protection. The bias also depends on the relationship between the distribution of vaccine protection and the distribution of behaviour change, which could differ between blinded and unblinded trials.
Design and analysis considerations in a cohort study involving repeated measurement of both exposure and outcome: The association between genital papillomavirus infection and risk of cervical intraepithelial neoplasia
Authors: Duffy S. W. , Rohan T. E. , McLaughlin J. R. 
Published: April 2007

AbstractCohort studies commonly involve a single determination of exposure, and one or more assessments of whether or not the outcome of interest has occurred. This approach is appropriate when the exposure does not change with time, and when one can readily determine the time of outcome (for example, mortality). If either of these conditions is not met, however, we may introduce bias into the estimate of effect, since we may misclassify individual members of the cohort with respect to exposure, outcome or both. We can reduce this bias by measuring exposure and outcome on more than one occasion. In this paper, we illustrate the design and analysis issues that arise in such circumstances, by reference to an ongoing prospective study of the relationship between genital papillomavirus infection and risk of cervical intraepithelial neoplasia. This study entails annual assessments of the status of the study subjects with respect to both conditions. In particular, we examine the implications that use of this design has on the statistical power of the study.
Sample size determination for an exponential survival model with an unrestricted covariate
Authors: Zhen Boguang , Murphy James R. 
Published: April 2007

AbstractWe derive formulae for estimating sample size and power for detecting the effect of an unrestricted covariate on survival time. These are useful in designing survival studies with different patterns of recruitment and follow‐up when survival time is exponentially distributed. We use the asymptotic covariance matrix, conditional expectation and Taylor's expansion techniques to develop these formulae. Computer simulations indicate that the asymptotic approximations used in developing the formulae are good over a range of parameter values and different patterns of recruitment and follow‐up that are relevant to survival studies.
New designs for the selection of treatments to be tested in randomized clinical trials
Authors: Simon R. , Thall P. F. , Ellenberg S. S. 
Published: April 2007

AbstractThe most important aspect of phase III randomized clinical trials is the selection of the experimental treatments to be tested. Often this decision is based on uncontrolled phase II trials. Substantial statistical attention has been focused on the design of phase III trials and for simple phase II trials, which determine whether a new drug has any anti‐disease activity. Much less statistical effort has been devoted to the design and analysis of phase II trials for screening active experimental treatments to determine whether they are sufficiently active, relative to standard treatments, to warrant the conduct of a large randomized phase III trial. This problem is particularly acute in the development of drug combinations where many regimens are possible. We review several designs for such screening trials which we have developed.
A multiple decision procedure in clinical trials
Authors: Chen T. Timothy , Simon Richard 
Published: April 2007

AbstractIn some multiple treatment arm clinical trials there is an order of preference for the treatments based on secondary considerations like toxicity or cost. In this paper, we consider the case where two or more treatments could have equal prior preference. This formulation includes the problem of comparing several equally preferred experimental treatments to one control, or the comparison of a combination with its components. Our decision procedures will guarantee a high selection probability for the correct treatment(s) when that selection is appropriate. We establish sample size requirements for our decision procedures which can be applied to clinical trials with normal, binomial, or right censored exponential endpoints.
Time series for modelling counts from a relapsing‐remitting disease: Application to modelling disease activity in multiple sclerosis
Authors: Albert Paul S. , McFarland Henry F. , Smith Mary E. , Frank Joseph A. 
Published: April 2007

AbstractMany chronic diseases are relapsing‐remitting diseases, in which subjects alternate between periods with increasing and decreasing disease activity; relapsing‐remitting multiple sclerosis is an example. This paper proposes two classes of models for sequences of counts observed from a relapsing‐remitting disease. In the first, the relapsing‐remitting nature of the data is modelled by a Poisson time series with a periodic trend in the mean. In this approach, the mean is expressed as a function of a sinusoidal trend and past observations of the time series. An algorithm that uses GLIM is developed, and it results in maximum‐likelihood estimation for the amplitude, frequency and autoregressive effects. In the second class of models, the relapsing‐remitting behaviour is described by a Poisson time series in which changes in the mean follow a latent Markov chain. An EM algorithm is developed for maximum‐likelihood estimation for this model. The two models are illustrated and compared with data from a study evaluating the use of serial magnetic resonance imaging as a measure of disease activity in relapsing‐remitting multiple sclerosis.
Modelling transitional and joint marginal distributions in repeated categorical data
Authors: Follmann Dean 
Published: April 2007

AbstractWhen a repeated measures endpoint classifies people into several categories, marginal and transitional models provide two distinct approaches for data analysis. Marginal models estimate the probabilities of being in different categories over time. Transitional models estimate the probability of changing between any two given states during follow‐up visits. This paper develops transitional and marginal models and applies them to a clinical trial of treatments of opiate addiction. The primary outcome was the presence or absence of opiates in a thrice weekly urine test, administered for 17 weeks. Subjects frequently miss visits, however, and in effect respond in one of three ways to a visit: missing, opiates present or opiates absent. Thus we have three possible states.Our transitional model conditions on the current state and models the transition from statekto one of the other (0,…,K‐ 1) states using a mutinomial logit model. This model generalizes previous work of Muenz and Rubinstein. Significant covariates in this model are predictive of statechanges.Our marginal model views the state at each time point, rather than the transitions, as the primary response. Here we model the probability of being in statekwith a multinomial logit model. Correlation within individuals over visits can be handled by applying the approach of Zeger and Liang or the bootstrap. Significant covariates in this model can include more ‘global’ summaries of a person such as extent of previous opiate use. Both marginal and transitional models are needed to provide a complete description of an individual's behaviour over time since global summaries might not affect transitions. Of particular substantive interest is how the opiate treatments affect both the marginal and transition probabilities.
A comparison of likelihood‐based and marginal estimating equation methods for analysing repeated ordered categorical responses with missing data: Application to an intervention trial of vitamin prophylaxis for oesophageal dysplasia
Authors: Mark Steven D. , Gail Mitchell H. 
Published: April 2007

AbstractThe purpose of this research was to develop appropriate methods for analysing repeated ordinal categorical data that arose in an intervention trial to prevent oesophageal cancer. The measured response was the degree of oesophageal dysplasia at 2.5 and 6 years after randomization. An important feature was that some response measurements were missing, and the missingness was not ‘completely at random’ (MCAR). We show that standard likelihood‐based methods and standard methods based on marginal estimating equations yield biased results, and we propose adaptations to both these approaches that yield valid inference under the weaker ‘missing at random’ (MAR) assumption. On the basis of efficiency calculations, simulation studies of finite sample properties, ease of computation, and flexibility for testing and exploring a range of treatment models, we recommend the adapted likelihood‐based approach for problems of this type, in which there are abundant data for estimating parameters.
Advances in statistical methodology for the evaluation of diagnostic and laboratory tests
Authors: Campbell Gregory 
Published: April 2007

AbstractThe ROC plot is a useful tool in the evaluation of the performance of medical tests for separating two populations. For a two‐state decision rule based on such a test, the ROC plot is the graph of all observed (1‐specificity, sensitivity) pairs. Each point on this empirical plot can be represented by a 2 × 2 contingency table. The non‐parametric statistics of Mann‐Whitney and Kolmogorov‐Smirnov can be immediately identified on this plot. Local non‐parametric confidence interval procedures related to the theoretical ROC curve are briefly reviewed. For continuous data, two new simultaneous confidence regions associated with the ROC curve are presented, one based on Kolmogorov‐Smirnov confidence bands for distribution functions and the other based on bootstrapping.Two different tests on the same patients can be compared on the ROC scale. For continuous data, one important problem concerns the comparison of two ROC plots (as would arise from two correlated diagnostic tests on each patient) using a sup norm (this metric can detect differences that the ROC area cannot). The distribution of a statistic based on this norm is studied, using the bootstrap. A biomedical example illustrates the methodologies.