bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bayesian methods for prevalence estimation from incomplete administrative lists
Authors: Smith Philip J. 
Published: April 2007

AbstractMany studies aimed at estimating prevalence use several administrative lists from different sources in an attempt to enumerate all persons affected with the health condition of interest. Each list is ‘incomplete’ in the sense that none of them enumerates all persons affected with the health condition. Further, because the lists are drawn from different administrative sources the probability of enumeration varies from list to list. The goal is to use information from the lists to estimate the total number of affected persons in the population, but with some accounting for the different but unknown probabilities of enumeration on each list. This paper presents a Bayesian method to estimate prevalence when the probability of enumeration varies from list to list. Data from a survey of children with spina bifida illustrate the methodology.
Estimating prediction equations in repeated measures designs
Authors: Stanek Edward J. , Kline Gregory 
Published: April 2007

AbstractExperimental designs with repeated measures allow response patterns over time (or dose) to be modelled and compared between different homogeneous groups. Issues in data analysis often focus on the pattern of variation of the repeated measures, the appropriateness of a univariate or multivariate analysis, and the shape of the response pattern. An aspect of analysis that is often of equal importance is the development of a regression model for response once the pattern has been characterized. Analysis of variance or multivariate growth curve results often do not include easily interpretable regression equation estimates that can be used for prediction. We present methods and tables that permit simple construction of such predictive equations for repeated measures designs when response is modelled as a polynomial over time with univariate or multivariate analyses.
The preliminary clinical evaluation of the safety and efficacy of a fertility regulating vaccine
Authors: Griffin P. D. , Jones W. R. 
Published: April 2007

AbstractSince the mid 1970s, the World Health Organization's Special Programme of Research, Development and Research Training in Human Reproduction, through its Task Force on Vaccines for Fertility Regulation, has been supporting and coordinating the development of a birth control vaccine based on a synthetic peptide representing part of the beta subunit of human chorionic gonadotrophin (β‐hCG). The majority of this work has been carried out in the laboratories of Dr V. C. Stevens at Ohio State University. This hCG vaccine has recently been tested for tolerability and immunogenicity in a phase I clinical trial, and a preliminary evaluation of the vaccine's efficacy in fertile women volunteers is currently being planned. This paper describes the preclinical and clinical testing of this vaccine and the problems and challenges presented by this novel preparation. These have included the need to carry out safety and efficacy studies in the baboon, and the difficulties of designing appropriate preclinical toxicity and immunosafety studies and clinical trial protocols in the absence of previous experience with vaccines of this type.
Identifying the fertile phase of the human menstrual cycle
Authors: Royston Patrick 
Published: April 2007

AbstractThe identification of the human fertile phase as the time during which a woman or a couple may conceive is elusive. The fertile time depends on many factors in each individual menstrual cycle and may be said to be more of a statistical than a physiological entity. This paper reviews the application of statistical methods to three areas related to conception and the fertile phase. The first is the prediction and detection of ovulation from serial measurements, such as hormones, basal body temperature and cervical mucus, throughout the menstrual cycle. Typically, such variables increase from some baseline level to a peak around ovulation (the most fertile time), then subside to low levels in the postovulatory phase. The statistical challenge is to detect the rise (signalling the onset of potential fertility) and subsequent fall. Analytic methods considered include thresholds, Bayesian change‐point models and particularly the cumulative sum (cusum) technique which is both simple to apply and understand, and effective. The second area comprises appropriate methods of analysing and interpreting data from clinical studies of the fertile phase, especially in so‐alled natural family planning (NFP) where it is usual for women to observe several indices of potential fertility. Such studies usually try to establish the temporal relationships between markers of the fertile phase and examine the success of different combinations of markers in delineating the fertile time in comparison with a standard ‘defined’ phase, for example, the interval from three days before to two days after the peak of luteinizing hormone. The third area is the assessment of the probability of conception on certain days of the cycle, which is vital to the understanding of the fertile phase and its application to NFP. Direct estimation of such probabilities is impractical; instead, resort must be made to estimation by maximum likelihood of the parameters of specially constructed models. Suitable models are described. Finally, the need for a new prospective study of the probability of conception in relation to the markers of the fertile phase used in the symptothermal method of NFP is discussed.
Using the ratio of urinary oestrogen and progesterone metabolites to estimate day of ovulation
Authors: Baird Donna Day , Weinberg Clarice R. , Wilcox Allen J. , McConnaughey D. Robert , Musey Paul I. 
Published: April 2007

AbstractWe have developed a method of estimating day of ovulation using urinary ovarian hormone data. The method identifies a day of luteal transition that occurs at the shift from production of follicular oestrogen to luteal progesterone. The algorithm for identifying this shift was evaluated and judged better than specified alternatives in that it resulted in (1) a high concordance between the day of luteal transition and peaks in urinary luteinizing hormone (LH) for cycles with well‐defined peaks, (2) a low variance in the length of the luteal phase of the menstrual cycle, which presumably reflects a low measurement error in estimating day of ovulation, and (3) a high proportion of cycles for which an approximate day of ovulation could be determined. To validate the new algorithm, it was applied to an independent data set. The algorithm identified a day of luteal transition in 88 percent of these cycles, and the identified day occurred within two days of the urinary LH peak for all of the cycles with clear LH peaks. Determination of the day of luteal transition to estimate ovulation requires only first‐morning urine specimens, requires no correction for day‐to‐day variations in urine concentration, and can be applied to a mid‐ycle window of data.
Monitoring a randomized clinical trial for futility: The north‐norwegian lidocaine intervention trial
Authors: Berntsen Rolf Franck , Rasmussen Knut , Bjørnstad Jan F. 
Published: April 2007

AbstractRandomized clinical trials of acute disease are usually designed as single‐look, fixed sample size trials. This methodological study compares the conventional approach with a multiple‐look, group sequential design with stopping rules for both treatment efficacy and for an inconclusive trial outcome, called trial futility. An ongoing trial on pre‐hospital prophylaxis of sudden death in acute myocardial infarction forms the basis of the analysis. The effects of introducing multiple looks (or interim analyses) and tests for futility were obtained by binomial simulation. The introduction of four looks (that is, three interim and a final analysis) resulted in a modest increase in the maximal number of patients required. This was, however, fully compensated for by the high probability of early termination in case of treatment efficacy. The addition of futility tests, enabling termination at half the maximum trial size when there is no treatment difference, resulted in only a negligible reduction of overall power. We conclude that multiple‐look, group sequential designs testing for both treatment efficacy and trial futility may improve the cost‐effectiveness of randomized trials of acute disease.
Generalization of normal discriminant analysis using fourier series density estimators
Authors: Odom‐Maryon Tamara , Langholz Bryan , Niland Joyce , Azen Stanley 
Published: April 2007

AbstractIn this paper we examine the efficiency of a generalization of the traditional normal linear (LDA) or quadratic (QDA) discriminant analysis. This procedure (the generalized discriminant analysis, GDA) replaces each normal density used in the traditional classification rule by a Fourier series density estimator which ‘adjusts’ the normal density if the data deviate markedly from normality (for example, heavily skewed or multimodal). We derive the GDA in both the univariate and multivariate situations. In a simulation study for the univariate situation, we evaluate the relative efficiency of the GDA. In addition, we demonstrate the performance of the GDA through a series of multivariate applications. We conclude that if the distributions of the data do not deviate markedly from normality, the GDA is as efficient as the LDA or QDA. On the other hand, if either of the distributions deviates from normality, then the GDA, which performs as a semiparametric discriminant procedure, is more efficient than the LDA or QDA.
An approach to several problems in using large databases for population‐based case‐ontrol studies of the therapeutic efficacy and safety of anti‐hypertensive medicines
Authors: Psaty Bruce M. , Koepsell Thomas D. , Siscovick David , Wahl Pat , Logerfo James P. , Inui Thomas S. , Wagner Edward H. 
Published: April 2007

AbstractIn this paper we discuss approaches to two distinct problems in using large computerized databases to conduct population‐based case‐ontrol studies. The first topic, concerning case ascertainment, is a methodo‐logic problem, and the second, concerning confounding by indication, is an analytic problem. The first involves attempting to ascertain all incident cases of coronary disease among enrollees of Group Health Cooperative of Puget Sound (GHC), a large health‐maintenance organization. Methodologic studies reported here have helped us improve the efficiency of using the large computerized databases for case ascertainment at GHC. The second problem involves the issue of confounding by drug indication. Drugs such as beta‐blockers have multiple indications, including the treatment of both high blood pressure and angina. These two indications may make it difficult in an observational study to determine whether beta‐blockers may prevent coronary disease in patients with high blood pressure. We discuss here our current thinking about the best analytic approach to this problem.
Using empirical bayes methods in biopharmaceutical research
Authors: Louis Thomas A. 
Published: April 2007

AbstractA compound sampling model, where a unit‐specific parameter is sampled from a prior distribution and then observeds are generated by a sampling distribution depending on the parameter, underlies a wide variety of biopharmaceutical data. For example, in a multi‐centre clinical trial the true treatment effect varies from centre to centre. Observed treatment effects deviate from these true effects through sampling variation. Knowledge of the prior distribution allows use of Bayesian analysis to compute the posterior distribution of clinic‐specific treatment effects (frequently summarized by the posterior mean and variance). More commonly, with the prior not completely specified, observed data can be used to estimate the prior and use it to produce the posterior distribution: an empirical Bayes (or variance component) analysis. In the empirical Bayes model the estimated prior mean gives the typical treatment effect and the estimated prior standard deviation indicates the heterogeneity of treatment effects. In both the Bayes and empirical Bayes approaches, estimated clinic effects are shrunken towards a common value from estimates based on single clinics. This shrinkage produces more efficient estimates. In addition, the compound model helps structure approaches to ranking and selection, provides adjustments for multiplicity, allows estimation of the histogram of clinic‐specific effects, and structures incorporation of external information. This paper outlines the empirical Bayes approach. Coverage will include development and comparison of approaches based on parametric priors (for example, a Gaussian prior with unknown mean and variance) and non‐parametric priors, discussion of the importance of accounting for uncertainty in the estimated prior, comparison of the output and interpretation of fixed and random effects approaches to estimating population values, estimating histograms, and identification of key considerations in the use and interpretation of empirical Bayes methods.
A statistical procedure for the estimation of accuracy parameters in interlaboratory studies
Authors: Kristiansen Sören 
Published: April 2007

AbstractInterlaboratory studies are conducted to estimate the accuracy of methods of laboratory measurements. The standard parameters used to describe this accuracy are the repeatability and the reproducibility. Usually variance components models are used to estimate these parameters. If the model assumptions are violated the resulting estimates for reproducibility and repeatability may, however, be biased. A new method of residual analysis in variance components models‐developed by the author‐may be used to detect violations of the model assumptions. If the residual analysis indicates that the model assumptions are violated, a simple robust method‐which makes fewer assumptions‐may be used for the estimation of accuracy parameters.The application of this residual analysis is demonstrated using data of an interlaboratory study. Graphical methods play an important role in the evaluation of the residuals. The analysis of the residuals uses methods similar to those used for the analysis of Studentized residuals in the linear model.The estimates obtained by the variance components model and the simple robust method are compared. The results of the residual analysis may be used to decide which of the two estimates can be considered more appropriate.The necessity of residual analysis in the analysis of interlaboratory studies by variance components models is pointed out. Potential hazards inherent to residual analysis in variance components models are discussed. Conclusions for the analysis of interlaboratory studies are drawn.
Estimation of the mean and standard deviation using order statistics
Authors: Dunstan F. D. J. , Nix A. B. J. 
Published: April 2007

AbstractEstimation of the mean and standard deviation of a distribution using linear combinations of the order statistics of a censored sample is discussed. Existing methods are compared with the optimal estimator of this type and new approximations to the optimal estimator are proposed. Their performances in the presence of outliers are assessed by means of simulation.
Analysis of the crossover design in the presence of residual effects
Authors: Lehmacher W. 
Published: April 2007

AbstractWhen a residual effect is suspected in a two‐period crossover trial, an analysis of the first‐period data is often chosen instead of the potentially biased crossover analysis. This paper indicates how the usual crossover test has to be interpreted correctly and that its bias has two different consequences, namely a conservative or a liberal test decision if a positive (carryover) or a negative (withdrawal) residual effect exists. A multiple testing procedure is presented allowing for simultaneous crossover and first‐period analysis controlling the experimental error rate. This procedure together with the correct interpretation of the crossover test enables many useful applications of crossover designs.
Individual bioequivalence: What matters to the patient
Authors: Hauck Walter W. , Anderson Sharonerson 
Published: April 2007

AbstractCurrent procedures for assessing the bioequivalence of two formulations are based on the concept of average bioequivalence. That is, they assess whether the average responses between individuals on the two formulations are similar. We show first that average bioequivalence is not sufficient to assure that an individual patient could be expected to respond similarly to the two formulations. To have such reasonable assurance requires a different notion of bioequivalence; individual (or within‐subject) bioequivalence.Second, we propose a simple statistical procedure for assessing individual bioequivalence. This decision rule, TIER (test of individual equivalence ratios) requires the specification of the minimum proportion of subjects in the applicable population for which the two formulations being tested must be bioequivalent (a regulatory decision). The TIER rule is summarized in terms of the minimum number of subjects with bioavailability ratios falling within the specified equivalence interval necessary to be able to claim bioequivalence for given sample size and type I error. We recommend that the corresponding lower bounds (one‐sided confidence intervals) for the proportion of bioequivalent subjects be calculated.
Sample sizes for bioequivalence studies
Authors: Metzler Carl M. 
Published: April 2007

AbstractIn recent years a number of decision rules, based on sound statistical principles, have been proposed for deciding if a test formulation is bioequivalent to a reference formulation. The decision rule based on confidence intervals has been accepted by regulatory agencies, at least by the Food and Drug Administration of the United States. A useful property of this decision rule is that the regulatory agency need not require a certain sample size, since the level of protection against wrongly deciding bioequivalence is set by the choice of the alpha level used to compute the confidence intervals. The manufacturer claiming bioequivalence is concerned about sample size, for sample size determines the probability of falsely deciding non‐bioequivalence when the test formulation does indeed have an acceptable relative bioavailability. Curves of probability of rejecting bioequivalence have been computed for error coefficient of variation of 10, 20 and 30 per cent, for relative bioavailability from 70 to 130 per cent, and for protection levels of 90 and 95 per cent. These curves can be used for choosing the sample size for a bioequivalence study.
Random models for margins of a 2 × 2 contingency table and application to pharmacovigilance
Authors: Tubert Pascale , Begaud Bernard 
Published: April 2007

AbstractThe identification of new adverse drug reactions is often tricky. For a given case, the relationship between drug exposure and symptom occurrence is usually questionable. It could be investigated statistically from a series of drug‐event association cases with an independence test between the two variables. Analysing the related 2 × 2 contingency table obviously requires knowledge of its margins. However this information is often not available. We develop a calculation of the P‐value by choosing some random models for the unknown margins. Under the hypothesis of independence, a negative binomial distribution for the frequency of the drug‐event association cell is obtained, exactly or approximately. This method allows investigation of the causal relationship when some parameters, as incidence of the symptom in the population, are uncertain. An example is presented.
Analyses of black and white differentials in the age trajectory of mortality in two closed cohort studies
Authors: Manton Kenneth G. , Stallard Eric , Wing Steve 
Published: April 2007

AbstractWe examine the relationship of age to mortality in blacks and whites in two cohort studies, the 20‐year follow‐up of the Evans County, Georgia, Study population and the 25‐year follow‐up of the Charleston Heart Study population. We conducted analyses with two parametric forms of hazard models (Gompertz and Weibull) for total mortality experience and considered the fit of the two hazard models in each study both separately and with the data pooled. We evaluated the robustness of conclusions about differences in the age pattern of mortality for blacks and whites by comparing results from the two hazard models. Where the tests were non‐nested, we used an information‐based statistic (AIC) to compare the fit of the models to the data. Results were robust to the selection of the hazard function, that is both models provided evidence that mortality rates at younger ages were lower for whilte than black females but that mortality rates increased more rapidly with age for white females. The absolute differences and the differences in the rates of increase were in the same direction, but smaller, for males. Though both models represented the general features of mortality patterns, the information statistic suggested better performance of the Gompertz function. The Gompertz function was also less sensitive to the initial age of determination of mortality exposure.
Estimating standardized parameters from generalized linear models
Authors: Greenland Sander 
Published: April 2007

AbstractAlthough the traditional unrestricted ('non‐parametric') estimators of directly standardized rates and rate differences remain unbiased in sparse data, they tend to suffer from instability (low precision). As a result, many authors have proposed more precise estimators based on parametric models for the rates. This paper provides a general approach for constructing estimators of standardized parameters using generalized linear models, and shows that, in some common special cases, these model‐based ('smoothed') estimators can have an exceptionally simple form.
Evaluation of statistical methods for monitoring periodontal disease
Authors: Yang Mark C. K. , Marks Ronald G. , Clark William B. , Magnusson Ingvar 
Published: April 2007

AbstractThree methods for detecting the progression of periodontal disease based on the change in attachment level on teeth are compared from a statistical point of view. Regardless of gradual or burst disease progression theories, the regression method is found to be the most efficient way to detect changes. The exact false alarm rate is computed when the decision is based on the maximum regression coefficient at all sites during several visits.