bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessing antiarrhythmic drug efficacy: Multivariate considerations
Authors: Ulm K. , Schmidt G. , Barthel P. , Linzmaier A. 
Published: April 2007

AbstractThe spontaneous variability of ventricular arrhythmias is the major problem in assessing antiarrhythmic drug efficacy. Efficacy is usually assessed on one form of ventricular premature beats, namely either single beats (VPC) or more complex forms like couplets or salvos. The reduction rates required to assume efficacy with a confidence of 95 per cent are 63 per cent for VPCs, 92 per cent for couplets or 96 per cent for salvos. These rates are valid for two Holter electrocardiogram recordings within one week; otherwise the rates are higher and close to 100 per cent. In this paper we extend our ratio method for calculating these criteria to consider two or all three forms of ventricular premature beats simultaneously. The method is based on the first principal component. For use in practice we decided to calculate fixed criteria for each form of ventricular premature beats. For the combination of VPCs and couplets and a confidence level of 95 per cent, reduction rates of 42 per cent for VPC and 87 per cent for couplets are required. Both criteria must be fulfilled to assume the efficacy of a drug. These criteria are lower than those for each form alone. For all three forms together, reduction rates of 28, 72 and 88 per cent for VPC, couplets and salvos respectively are required.
Meta‐analysis for 2 × 2 tables: A bayesian approach
Authors: Carlin John B. 
Published: April 2007

AbstractThis paper develops and implements a fully Bayesian approach to meta‐analysis, in which uncertainty about effects in distinct but comparable studies is represented by an exchangeable prior distribution. Specifically, hierarchical normal models are used, along with a parametrization that allows a unified approach to deal easily with both clinical trial and case‐control study data. Monte Carlo methods are used to obtain posterior distributions for parameters of interest, integrating out the unknown parameters of the exchangeable prior or ‘random effects’ distribution. The approach is illustrated with two examples, the first involving a data set on the effect of beta‐blockers after myocardial infarction, and the second based on a classic data set comprising 14 case‐control studies on the effects of smoking on lung cancer. In both examples, rather different conclusions from those previously published are obtained. In particular, it is claimed that widely used methods for meta‐analysis, which involve complete pooling of ‘O‐E’ values, lead to understatement of uncertainty in the estimation of overall or typical effect size.
Differential influence of prognostic factors on the occurrence of metastases at various anatomical sites in human breast cancer
Authors: Zedeler Karin , Keiding Niels , Kamby Claus 
Published: April 2007

AbstractThe present study was initiated by data on the anatomical occurrence of metastases from breast cancer. Metastases may occur at various organs, here grouped into ten categories, and simultaneous occurrences at several sites are common. Our aim was to identify and compare risk factors for development of metastases at each site.The influence of the various risk factors for recurrence at one specific site can be modelled as a standard competing risk problem, using well‐established survival analysis techniques such as the Cox regression model. Recently a generalization to joint occurrences at more than one site has been proposed. The more general models allowcomparisonof the differential prognostic influence of various risk factors on recurrence at different sites.We applied these methods to data from the Danish Breast Cancer Cooperative Group on recurrence after breast cancer, and examined the effect of degree of anaplasia, number of positive lymph nodes, size of primary tumour, skin or deep fascial invasion, age of the patient and adjuvant treatment regimen, as possible risk factors.There were no differential effects of type of therapy on recurrence in different sites. However, chemotherapy had a marginally significant positive effect at all sites. Moreover the number of positive lymph nodes was associated with an increased risk of metastases, homogenous across sites, except for brain where an opposing trend was found. Similarly the degree of anaplasia was associated with a somewhat increased risk of metastases, homogenous except for brain. For brain metastases a significantly more pronounced effect of degree of anaplasia was found.Comparison of the results obtained from the two types of models reveals that the new more general models confirm fewer differences between the influence of the prognostic factors than the traditional regression models suggest.
Which measures of skewness and kurtosis are best?
Authors: Royston Patrick 
Published: April 2007

AbstractIndices of distributional shape based on linear combinations of order statistics have recently been described by Hosking. Their usefulness as tools for practical data analysis is examined. They are found to have several advantages over the conventional indices of skewness and kurtosis (√b1andb2) and no serious drawbacks. It is proposed, therefore, that they should replace √b1andb2in routine data analysis. To implement this suggestion, action by the developers of standard statistical software is needed.
Directory of registries of clinical trials
Authors: Easterbrook Philippa J. 
Published: April 2007

AbstractRegistries of clinical trials are a potentially useful resource for the planning of new studies, the promotion of communication and collaboration between researchers, the conduct of meta‐analyses, and the facilitation of patient access and recruitment to trials. However, many physicians and researchers are unaware of their existence, and as a result they remain underused. A directory of registries of clinical trials has been developed as a result of an international survey of 63 organizations and 51 individuals in 13 different countries to identify the existence of such registries. This is intended as a resource to keep physicians, researchers, trial organizers, funding bodies and government agencies abreast of the growing number of registries available, and to assist in the planning of future registries. Twenty‐four current and six planned registries of clinical trials have been identified. Most focus on AIDS or oncology, but such diverse areas as neurosurgery, cardiovascular disease, dentistry and perinatology are also represented. This paper presents a descriptive profile on each registry and discusses the relative merits of their different organizational features. Recommendations are given for the establishment of future registries.
The use of subjective rankings in clinical trials with an application to cardiovascular disease
Authors: Follmann Dean , Wittes Janet , Cutler Jeffrey A. 
Published: April 2007

AbstractEvaluating a clinical trial can be problematic if the studied treatments affect patients in many ways. A possible method for evaluating treatments is to have raters rank all the patients' trial experiences and then test whether the distribution of ranks differ between treatments. Before one can advocate such a procedure, however, one would like to be assured that raters agree fairly well with one another. As a first step in examining whether raters tend to agree, we conducted a small study with 20 raters evaluating 43 trial experiences from an imaginary cardiovascular clinical trial. Raters showed a high degree of consensus. Moreover, the average ranks agreed fairly well with two quantitative ranking rules. On the other hand, the average ranks did not agree very well with weightings usually selected in cardiovascular trials. These results suggest ranking may be a feasible approach to analysing certain clinical trials with multiple outcomes.
Graphical representation of survival curves associated with a binary non‐reversible time dependent covariate
Authors: Feuer Eric J. , Hankey Benjamin F. , Gaynor Jeffrey J. , Wesley Margaret N. , Baker Stuart G. , Meyer Jason S. 
Published: April 2007

AbstractThe use of time dependent covariates has allowed for incorporation into analysis of survival data intervening events that are binary and non‐reversible (for example, heart transplant, initial response to chemotherapy). We can represent this type of intervening event as a three‐state stochastic process with a starting state (S), an intervening state (I), and an absorbing state (D), which usually represents death. In this paper we present three procedures for calculating survivorship functions which attempt to display the prognostic significance of the time dependent covariate. The first method compares survival from baseline for the two possible paths through the stochastic process; the second method compares overall survival to survival with stateIremoved from the process; and, the third method compares survival for those already in stateIat a landmark timexto those in stateSat timexwho will never enter stateI.We develop discrete hazard estimates for the survival curves associated with the three methods. Two examples illustrate how these methods can yield different results and in which situations one might employ each of the three methods. Extensions to applications with reversible binary time dependent covariates and models with both baseline and time dependent covariates are suggested.
Cross‐validation performance of mortality prediction models
Authors: Hadorn David C. , Draper David , Rogers William H. , Keeler Emmett B. , Brook Robert H. 
Published: April 2007

AbstractMortality prediction models hold substantial promise as tools for patient management, quality assessment, and, perhaps, health care resource allocation planning. Yet relatively little is known about the predictive validity of these models. We report here a comparison of the cross‐validation performance of seven statistical models of patient mortality: (1) ordinary‐least‐squares (OLS) regression predicting 0/1 death status six months after admission; (2) logistic regression; (3) Cox regression; (4–6) three unit‐weight models derived from the logistic regression, and (7) a recursive partitioning classification technique (CART).We calculated the following performance statistics for each model in both a learning and test sample of patients, all of whom were drawn from a nationally representative sample of 2558 Medicare patients with acute myocardial infarction: overall accuracy in predicting six‐month mortality, sensitivity and specificity rates, positive and negative predictive values, and per cent improvement in accuracy rates and error rates over model‐free predictions (i.e., predictions that make no use of available independent variables). We developed ROC curves based on logistic regression, the best unit‐weight model, the single best predictor variable, and a series of CART models generated by varying the misclassification cost specifications.In our sample, the models reduced model‐free error rates at the patient level by 8–22 per cent in the test sample. We found that the performance of the logistic regression models was marginally superior to that of other models. The areas under the ROC curves for the best models ranged from 0·61 to 0·63. Overall predictive accuracy for the best models may be adequateto support activities such as quality assessment that involve aggregating over large groups of patients, but the extent to which these models may be appropriately applied to patient‐level resource allocation planning is less claar.
On the use of the generalized t and generalized rank‐sum statistics in medical research
Authors: Blair R. Clifford , Morel Jorge G. 
Published: April 2007

AbstractWe have used Monte Carlo methods to compare the type I error properties of the conditional and unconditional versions of the generalized t and the generalized rank‐sum tests to those of the independent samples t and Wilcoxon rank‐sum tests. Results showed inflated type I errors for the conditional generalized tests but not for the unconditional tests. We also compared the power of the unconditional generalized tests to that of the t and Wilcoxon tests under a variety of conditions. Results showed the generalized tests to be much more efficient than their traditional counterparts in some circumstances, but substantially less powerful in others. Based on these and other considerations, we conclude that the application of these newer statistics in medical research needs further consideration.
Maximum likelihood estimation of between and within variations in energy and protein intakes from infancy to adolescence for the philippines
Authors: Bhargava Alok , Bouis Howarth 
Published: April 2007

AbstractThe assesment of subjects' ‘usual’ intake of nutrients is important in assessing relationships between diet and disease and in identifying malnourished sub‐groups of the populations. Estimation of the variation in intakes within subjects over time (‘within variation’) has importance in epidemiologic research; estimation of the between subject variation in the sample has use in defining the recommended dietary allowances that take into account the inter‐individual differences. This paper estimates the between and within variances in the energy and protein intakes of 1189 Filipino children, based on 4 rounds of 24‐hour recall data within a dynamic framework by means of maximum likelihood. The main findings are that the proportion of variation due to the within variance is higher for childern from poorer households. Also, from the estimates of dynamic regression models for nutrient intakes of children and adults, it appears that school programmes that provide subsidized foods with good sources of protein to the poorest among school attendees will be cost effective.
Optimum sample size determination in stratified case‐control studies with cost considerations
Authors: Nam Jun‐Mo , Fears Thomas R. 
Published: April 2007

AbstractWe investigate sample size determination for Cochran's test for stratified case‐control studies when samples of cases and controls are allocated to maximize the asymptotic efficiency of Cochran's test subject to fixed total cost with cost per control varying by strata. We consider two situations typical of strata‐matched case‐control studies: when one samples both cases and controls and when cases are given and one samples controls. In each situation we develop and study an asymptotic method for finding the sample size required for a specific power under the optimum allocation proposed by Nam and Fears. Also, for the second situation, we investigate an asymptotic method for determining the common ratio, k, in one‐to‐k stratamatched case‐control studies without cost consideration for a given power. When cases are given, neither the optimum nor the standard control sample sizes appear in a closed form; we present numerical methods for calculating these sample sizes and illustrate them with examples. We find the reduction in total cost obtained under the optimum allocation compared to standard allocation more pronounced as the differences in stratum‐specific costs of sampling controls increase.
Estimating transmission probabilities for chlamydial infection
Authors: Katz Barry P. 
Published: April 2007

AbstractEstimates of transmission probabilities for sexually transmitted diseases historically come from studies of uninfected individuals exposed to those with a high disease prevalence (for example, prostitutes). However, changes in sexual behaviour, much of which relates to concerns about AIDS, has made identification of populations suitable for such studies extremely difficult. This paper presents a method for estimating these probabilities that utilizes a deterministic model and routinely collected data available in many clinics. Variance estimates for the estimators are also derived. Data for chlamydial infection and sensitivity analyses for the input parameters and assumptions illustrate the method.
Application of empirical bayes inference to estimation of rate of change in the presence of informative right censoring
Authors: Mori Motomi , Woodworth George G. , Woolson Robert F. 
Published: April 2007

AbstractWe apply parametric empirical Bayes inference of Morris7 to the estimation of rate of change from incomplete longitudinal studies where the right censoring process is considered informative, that is, the length of time the subjects participate in the study is associated with level of the study variable. Ignoring such an association can result in a biased estimate of rate of change. The proposed method provides estimates of rate of change for individual subjects as well as for the entire group, adjusted for informative right censoring. The method is considered more robust than those based on a specific parametric model for the censoring distribution. Under non‐informative right censoring these estimators of slopes are equivalent to the Bayes estimators derived by Fearn.11 We illustrate the method with an example involving renal transplant data. We evaluate the method's performance through a simulation study.
Sample size requirement for repeated measurements in continuous data
Authors: Lui Kung‐Jong , Cumberland William G. 
Published: April 2007

AbstractIn this paper we extend Bloch's discussion on the usefulness and the limitations in the application of repeated measurements per subject in study designs. We derive general sample size formulae for any finite number of comparison groups to calculate the required number of subjects with repeated measurements, that do not have to be conditionally independent. For fixed total cost, we discuss the optimal sample allocation for repeated measurements needed to maximize the power and the underestimation when using Bloch's sample size formula if in the hypothesis testing procedure the variance parameters are unknown. We have also included a quantitative investigation of the effectiveness of taking repeated measurements per subject to reduced the required number of subjects for a given power at a given α‐level.
Monitoring clinical trials with a conditional probability stopping rule
Authors: Snapinn Steven M. 
Published: April 2007

AbstractConditional probability procedures offer a flexible means of performing sequential analysis of clinical trials. Since these procedures are not based on repeated significance tests, the number and schedule of the interim analyses is less important than with group sequential procedures. Their main disadvantage is that the magnitude of their effect on the significance level is difficult to assess. This paper describes a conditional probability procedure which attempts to maintain the overall significance level by balancing the probabilities of false early rejection and false early acceptance. Monte Carlo sampling results suggest that this procedure can achieve a large reduction in expected sample size without greatly affecting either the significance level or power of the trial.
A decision‐theoretic evaluation of early stopping rules
Authors: Heitjan Daniel F. , Houts Peter S. , Harvey Harold A. 
Published: April 2007

AbstractWe consider the decision‐theoretic evaluation of early stopping rules for clinical trials. We describe a hypothetical phase III, two‐arm trial with interim analysis, modelling it as a decision problem. We present methods for constructing a group‐sequential test that is optimal for a given utility and prior distribution, and for determining a utility and prior under which a given test is optimal. Using our methods, we construct optimal tests for utilities and priors representing a variety of perspectives: a view that seeks to maximize the long‐run response rate, a view that seeks to maximize the chance of a correct decision, and a personal view elicited from a medical oncologist. Characteristics of the optimal rules are sensitive to the input utilities and priors, and their critical values and sampling properties can differ markedly from those of group‐sequential tests. We also compute utility functions and priors under which some standard group‐sequential stopping rules are optimal. Our results suggest that the group‐sequential tests, considered as decision procedures, imply a symmetry in the decision problem that is inconsistent with the response‐rate perspective. We conclude that decision theory is a useful device for illuminating the sometimes conflicting goals of clinical research, and that the use of decision theory in designing clinical trials could lead to marked changes in statistical criteria for early termination.
Regression toward the mean in 2 × 2 crossover designs with baseline measurements
Authors: Grender Julie Myers , Johnson William D. , Elston Robert C. 
Published: April 2007

AbstractClinical trials often involve the study of patients whose values for some quantitative characteristic are in the extreme of the distribution for the normal population. Subsequent observation of the characteristic of interest will ordinarily tend to regress toward the mean of the distribution even in the absence of a treatment effect. We consider the impact of regression toward the mean in the analysis of data arising in 2 × 2 crossover trials with two baseline measurements. The usual summary statistics can be especially misleading in the presence of regression toward the mean.
Sample size requirements for stratified cluster randomization designs
Authors: Donner Allan 
Published: April 2007

AbstractSample size requirements are provided for designs of studies in which clusters are randomized within each of several strata, where cluster size itself may be a stratifying factor. The approach generalizes a formula derived by Woolson et al., which provides sample size requirements for the Cochran‐Mantel‐Haenszel statistic. Issues of data analysis are also discussed.
Designs and analysis of two‐stage studies
Authors: Zhao L. P. , Lipsitz S. 
Published: April 2007

AbstractThis paper concerns the design and analysis of two‐stage studies, where, at the first stage, the response and the exposure variables are available among a large group of subjects. The other covariables, however, are available in only a subset of the large group, obtained in a second‐stage sample. This paper introduces a class of twelve such two‐stage designs, including two‐stage case‐control and case‐cohort designs as special cases. In analysing such two‐stage data, one objective is to extract information about the relationship between the exposure variable and the response after controlling for other covariables. We discuss three statistical methods to analyse the data and report results of Monte Carlo simulation to study the efficiency of the three methods.