bluuebunny/crossref_metadata_2025_split huggingface dataset:
A comprehensive algorithm for determining whether a run‐in strategy will be a cost‐effective design modification in a randomized clinical trial
Authors: Schechtman Kenneth B. , Gordon Mae E. 
Published: April 2007

AbstractIn randomized clinical trials, poor compliance and treatment intolerance lead to reduced between‐group differences, increased sample size requirements, and increased cost. A run‐in strategy is intended to reduce these problems. In this paper, we develop a comprehensive set of measures specifically sensitive to the effect of a run‐in on cost and sample size requirements, both before and after randomization. Using these measures, we describe a step‐by‐step algorithm through which one can estimate the cost‐effectiveness of a potential run‐in. Because the cost‐effectiveness of a run‐in is partly mediated by its effect on sample size, we begin by discussing the likely impact of a planned run‐in on the required number of randomized, eligible, and screened subjects. Run‐in strategies are most likely to be cost‐effective when: (1) per patient costs during the post‐randomization as compared to the screening period are high; (2) poor compliance is associated with a substantial reduction in response to treatment; (3) the number of screened patients needed to identify a single eligible patient is small; (4) the run‐in is inexpensive; (5) for most patients, the run‐in compliance status is maintained following randomization and, most importantly, (6) many subjects excluded by the run‐in are treatment intolerant or non‐compliant to the extent that we expect little or no treatment response. Our analysis suggests that conditions for the cost‐effectiveness of run‐in strategies are stringent. In particular, if the only purpose of a run‐in is to exclude ordinary partial compliers, the run‐in will frequently add to the cost of the trial. Often, the cost‐effectiveness of a run‐in requires that one can identify and exclude a substantial number of treatment intolerant or otherwise unresponsive subjects.
An evaluation of sexual behaviour change using statistical and cognitive models
Authors: Schnell Daniel J. , Galavotti Christine , O'reilly Kevin R. 
Published: April 2007

AbstractWe evaluate sexual behaviour change among homosexual men enrolled in the cohorts in four AIDS Community Demonstration Projects. Behaviour change is classified following the stages of behaviour change model and described using a Markov model. Predictors of behaviour change are identified and evaluated using logit models for correlated data. Sexual behaviour change within the cohort could be modelled as a first‐order Markov process. In addition, predictors suggested by models of health behaviour were correlated with particular patterns of sexual behaviour change. Our evaluation revealed a variety of patterns of sexual behaviour change in the cohorts and suggests multi‐faceted interventions for promotion of behaviour change.
Analysis of a measles epidemic
Authors: Allen L. J. , Lewis T. , Martin C. F. , Jones M. A. , Lo C. K. , Stamp M. , Mundel G. , Way A. B. 
Published: April 2007

AbstractIn January, February and March of 1989 an epidemic of rubeola occurred on the campus of Texas Tech University. A vaccination programme was initiated as soon as the epidemic was confirmed. Extensive case histories of all confirmed cases were collected by the Lubbock City Health Department and given an exhaustive statistical analysis by a group from the Department of Mathematics at Texas Tech University. The data and statistical analysis were used to formulate stochastic and deterministic models of the measles epidemic based on the standard SEIR model. The analysis and the simulations indicate that in order to prevent a measles outbreak on a university campus a high rate of immunity may be required (> 98 per cent). The assumptions in the models raise some interesting questions regarding social contacts which require further investigation.
Using autoregressive and random walk models to detect trends and shifts in unequally spaced tumour biomarker data
Authors: Schlain Brian R. , Lavin Philip T. , Hayden Cheryl L. 
Published: April 2007

AbstractContinuous time autoregressive (CAR(1)) and random walk models of time series data are provided for detecting non‐random shifts and trends of tumour markers in breast cancer patients following resection for cure. The continuous time random walk model with observation error is extended to the case of multiple patient time series. These models can be used to monitor large numbers of patients with time series with few sampling events that are serially correlated and unequally spaced. Further, the methodologies can be used to recommend appropriate testing intervals. A Kalman filter recursive algorithm is used to calculate the likelihood functions arising from the CAR(1) and random walk models and to calculate recursive residuals, which are monitored by Shewhart—cusum schemes.
Improved imputation of non‐responses to mailback questionnaires
Authors: Drane J. Wanzer , Richter Donna , Stoskopf Carleen 
Published: April 2007

AbstractPoor response rates, perhaps as low as 10 to 30 per cent, certainly bring into question the validity of any survey suffering such a malady. Repeated mailouts are capable of providing information on those persons who are resistant to responding. If one is willing to assume an exponential drop‐off in response rates, then imputation is possible for the entire group of non‐responders. Furthermore, a second exponential drop‐off in favourable (or non‐favourable) responses to particular items within the questionnaire leads to closed form estimates of population parameters and their associated standard errors. Three or more mailouts also provide a test of validity of the model assumed.
The effect of matching on the power of randomized community intervention studies
Authors: Martin Donald C. , Diehr Paula , Perrin Edward B. , Koepsell Thomas D. 
Published: April 2007

AbstractCurrently, there is considerable interest in studies that use the community as the experimental unit. Health promotion programmes are one example. Because such activities are expensive, the number of experimental units (communities) is usually very small. Investigators often match communities on demographic variables in order to improve the power of their studies. Matching is known to improve power in certain circumstances. However, we show here that if the number of communities is small, the matched design will probably have less power than the unmatched design. This is due primarily to the loss of degrees of freedom in the matched design, which outweighs the benefits of matching on any but the strongest correlates of changes in behaviour. In the community intervention situation, even small differences in sample size between the matched and unmatched analyses can have expensive consequences.
Stopping guidelines for clinical trials with multiple treatments
Authors: Hughes Michael D. 
Published: April 2007

AbstractStopping guidelines are widely used in long‐term clinical trials involving two treatments. These allow planned interim analyses of the accumulating data to be undertaken whilst preserving the type I error rate for the treatment comparison. Their philosophy is extended to the case of trials with multiple treatments so that at any interim analysis, treatments may be considered for dropping from the trial if they are significantly inferior to all treatments that will remain. The proposed guidelines are designed to preserve the error rate in determining that treatment or group of treatments which is, in reality, best. Using simulation studies, stopping guidelines developed for two‐arm trials are shown to be directly usable in multiple‐arm studies when the treatments studied are all experimental and so in direct competition. When one treatment is the standard of care, a modification gives a better ethical perspective by also permitting treatments to be dropped when they are deemed inferior to the standard. Results are presented for normally distributed responses in trials involving three or four treatments and using stopping boundaries of the form proposed by O'Brien and Fleming. In conclusion, I discuss some of the additional considerations that are important in employing stopping guidelines in trials with multiple treatments.
Construction of age‐related reference centiles using absolute residuals
Authors: Altman Douglas G. 
Published: April 2007

AbstractThis paper proposes a simple approach to the parametric derivation of age‐related reference ranges which avoids the creation of arbitrary age groups, copes easily with a non‐linear relation between variability and age, and is computationally simple. After the mean is modelled as a function of age, the age‐specific standard deviation is estimated by regressing the absolute residuals on age. The method assumes that the data are, or can be transformed to be, normal at each age, and uses the properties of the half normal distribution. An example is given using 450 measurements of fetal foot length.
A modelling approach to the analysis of menstrual diary data
Authors: Collett D. , Weerasooriya N. 
Published: April 2007

AbstractIn clinical trials to compare contraceptives, women are usually asked to record whether or not each day is a bleeding day over the duration of the trial. In this paper we describe how parametric models, which include terms corresponding to covariates recorded for each woman, can be used to analyse data on the occurrence of certain adverse events identified from the diary record. Linear logistic models are used to analyse the probability of prolonged bleeding or amenorrhoea, and log‐linear models are used to analyse the lengths of bleeding episodes. In both cases variation between women is allowed for by including a random effect in the model. The application of our methods is illustrated using a data base made available by the World Health Organization.
Cox regression models for quality adjusted survival analysis
Authors: Cole Bernard F. , Gelber Richard D. , Goldhirsch Aron 
Published: April 2007

AbstractWe develop a method for incorporating covariates as regressors in a quality adjusted survival analysis (Q‐TWiST) using Cox's proportional hazards model. The standard Q‐TWiST method assumes that patients progress through a series of health states which differ in quality of life. The Kaplan—Meier product limit method is used to estimate the mean duration of each state by estimating the survival curves for the health state transition times. These estimates provide the basis for quality adjusted survival analysis. In this paper, the survival curves are modelled using Cox's proportional hazards regression. Quality adjusted survival is estimated given sets of covariate values, allowing one to profile patients. The results are useful for investigating how prognostic factors affect treatment benefits in terms of quality of life. We give a brief review of the standard Q‐TWiST method and illustrate the extended methodology with an example from the International Breast Cancer Study Group Trial V comparing short duration versus long duration chemotherapy in the treatment of node‐positive breast cancer.
Robust inference for multivariate survival data
Authors: Segal Mark R. , Neuhaus John M. 
Published: April 2007

AbstractMultivariate survival data arise when an individual records multiple survival events or when individuals recording single survival events are grouped into clusters. In this paper we propose a new method for the analysis of multivariate survival data. The technique is a synthesis of the Poisson regression formulation for univariate censored survival analysis and the generalized estimating equation approach for obtaining valid variance estimates for generalized linear models in the presence of clustering. When the survival data are clustered, combining the methods provides not only valid estimates for the variances of regression parameters but also estimates of the dependence between survival times. The approach entails specifying parametric models for the marginal hazards and a dependence structure, but does not require specification of the joint multivariate survival distribution. Properties of the methodology are investigated by simulation and through an illustrative example.
Tests for qualitative treatment‐by‐centre interaction using a ‘pushback’ procedure
Authors: Ciminera Joseph L. , Heyse Joseph F. , Nguyen Ha H. , Tukey John W. 
Published: April 2007

AbstractIn multicentre clinical trials using a common protocol, the centres are usually regarded as being a fixed factor, thus allowing any treatment‐by‐centre interaction to be omitted from the error term for the effect of treatment. However, we feel it necessary to use the treatment‐by‐centre interaction as the error term if there is substantial evidence that the interaction with centres is qualitative instead of quantitative. To make allowance for the estimated uncertainties of the centre means, we propose choosing a reference value (for example, the median of the ordered array of centre means) and converting the individual centre results into standardized deviations from the reference value. The deviations are then reordered, and the results ‘pushed back’ by amounts appropriate for the corresponding order statistics in a sample from the relevant distribution. The pushed‐back standardized deviations are then restored to the original scale. The appearance of opposite signs among the destandardized values for the various centres is then taken as ‘substantial evidence’ of qualitative interaction. Procedures are presented using, in any combination: (i) Gaussian, or Student's t‐distribution; (ii) order‐statistic medians or outward 90 per cent points of the corresponding order statistic distributions; (iii) pooling or grouping and pooling the internally estimated standard deviations of the centre means. The use of the least conservative combination — Student's t, outward 90 per cent points, grouping and pooling — is recommended.
Evaluation of multicentre clinical trial data using adaptations of the mosteller‐tukey procedure
Authors: Ciminera Joseph L. , Heyse Joseph F. , Nguyen Ha H. , Tukey John W. 
Published: April 2007

AbstractTwo procedures, based on proposals discussed by Mosteller and Tukey, are described for obtaining a combined estimate of the difference between two treatment means and its confidence interval from multicentre clinical trial data. Both procedures provide estimates in the possible presence of heteroscedasticity. The first procedure is designated the primary analysis for efficacy assessment. It omits treatment‐by‐centre interaction from the error term for treatment, unless there is substantial evidence of qualitative interaction (Ciminera et al.) or other special circumstances. The second procedure is the primary analysis whenever there is substantial evidence of qualitative interaction, and can be used whenever there are other reasons to make an analysis allowing for interaction.
Visual and statistical assessment of spatial clustering in mapped data
Authors: Walter S. D. 
Published: April 2007

AbstractMaps have seen increasing use to examine regional variation in health, but there has been little research on the visual perception of spatial patterns in mapped data. Theories of graphical perception suggest that the interpretation of maps is complex relative to other types of graphical material. This paper describes an experiment in which observers assessed a series of maps with respect to their amount of clustering. Maps with various types of spatial pattern were visually distinguishable; comparisons between variants of the same map, however, using different shading and plotting symbols indicated that the method of data representation also had a strong effect on visual perception. There was some evidence for a learning effect in complex maps. The relationship between the visual assessments and a statistical measure of spatial autocorrelation was significant but imperfect.
Combining independent studies of a diagnostic test into a summary roc curve: Data‐analytic approaches and some additional considerations
Authors: Moses Lincoln E. , Shapiro David , Littenberg Benjamin 
Published: April 2007

AbstractWe consider how to combine several independent studies of the same diagnostic test, where each study reports an estimated false positive rate )FPR( and an estimated true positive rate )TPR(. We propose constructing a summary receiver operating characteristic )ROC( curve by the following steps. )i( Convert each FPR to its logistic transform U and each TPR to its logistic transform V after increasing each observed frequency by adding 1/2. )ii( For each study calculate D = V – U, which is the log odds ratio of TPR and FPR, and S = V + U, an implied function of test threshold; then plot each study's point )Si, Di(. )iii( Fit a robust‐resistant regression line to these points )or an equally weighted least‐squares regression line(, with V – U as the dependent variable. )iv( Back‐transform the line to ROC space. To avoid model‐dependent extrapolation from irrelevant regions of ROC space we propose defining a priori a value of FPR so large that the test simply would not be used at that FPR, and a value of TPR so low that the test would not be used at that TPR. Then )a( only data points lying in the thus defined north‐west rectangle of the unit square are used in the data analysis, and )b( the estimated summary ROC is depicted only within that subregion of the unit square. We illustrate the methods using simulated and real data sets, and we point to ways of comparing different tests and of taking into account the effects of covariates.
How to establish equivalence when data are censored: A randomized trial of treatments for B non‐Hodgkin lymphoma
Authors: Com‐Nougue C. , Rodary C. , Patte C. 
Published: April 2007

AbstractInterest in equivalence trials has been increasing for many years, though the methodology which has been developed for such trials is mainly for uncensored data. In cancer research we are more often concerned with survival. In an efficacy trial, the null hypothesis specifies equality of the two survival distributions, but in an equivalence trial, a null hypothesis of inequivalenceH0has to be tested. The usual logrank test has to be modified to test whether the true valuerof the ratio of hazard rates in two treatment groups is at least equal to a limit valuer0. If prognostic factors have to be taken into account, the Cox model provides tests ofH0, and a useful confidence interval for the adjusted relative risk derived from the regression parameter for the treatment indicator. An equivalence trial of maintenance therapy was carried out in children with B non‐Hodgkin lymphoma, and serves as an illustration.
The use of simulation in the design of two cardiovascular survival studies
Authors: Yateman Nigel A. , Skene Allan M. 
Published: April 2007

AbstractSimulation has become practical as an everyday tool given the wide availability of high performance workstations. We argue that simulation can play an important role in determining the number of patients required in a survival study, particularly if the assumption of proportional hazards does not hold, or if the study design is complex. The argument is illustrated by two examples. The first considers the design of a post myocardial infarction survival trial in which the hazard ratio is not constant. The second provides sample size estimates for a multicentre heart failure study in which both the treatment effect and the control mortality rate vary across centres.
Clinical trials and causation: Bayesian perspectives
Authors: Schaffner Kenneth F. 
Published: April 2007

AbstractIn addition to the safety, it is essential to establish the causal efficacy of extant and new treatments, and well‐designed clinical trials are thought by most to be the ‘gold standard’ to accomplish this. Contrary to most statisticians' and regulators' views, however, I will argue that the concept of causation involved in clinical trials is not all that clear. I discuss the manipulability approach to causation, interpreted counterfactually, which seems to fit causation as it is found in such sciences as physiology, but it has unclear relations to a concept of causation proposed by a number of epidemiologists. I characterize £epidemiological causation as probabilistic and formulated at a population level, and dependent on certain general criteria for causation as well as study‐design considerations. I then attempt to clarify the connections between these concepts of causation and Cartwright's views on complexity and causality, a ‘Bayesian’ framework proposed by Rubin and further elaborated by Holland, and Glymour and his colleagues' recent directed graphical causal modelling approach.
Verifying keyed medical research data
Authors: Blumenstein Brent A. 
Published: April 2007

AbstractAlthough diminished use of double keying for the verification of keyed medical research data has occurred, there are no published data that demonstrate the existence of equivalent or better replacement. The simple replacement of double keying with visual comparison is not recommended because visual comparison is too susceptible to the transient energy levels of the individual who makes the comparison. Some system design innovations applicable in specific situations allow elimination of verification by providing nearly equivalent or possibly even superior data quality assurance opportunities. These alternatives include designs where one replaces the transcription to data forms by direct keying from original source documents coupled with extensive quality reviews, and designs with direct data base input of data subjected to intense automated consistency checking and immediate analytic use. In both cases, the absence of keyed data verification places a greater burden for data quality assurance on other subsystems, such as quality review, and ultimately the analytic process. Data entry systems based on new technologies, such as tablet computers, electronic image processing, and voice input, will require similar considerations for assuring data quality.
A model for seasonal changes in time series regression relationships: With an application in psychiatry
Authors: Albert Paul S. 
Published: April 2007

AbstractThis paper proposes a two‐stage linear model for the regression relationships between a time series and a set of time‐dependent covariates when the relationships change according to a seasonal pattern. The first stage consists of a piecewise constant autoregression model for these relationships. The second stage models the piecewise coefficients with harmonic equations to examine for seasonal trends. This approach models the seasonal trends in time series regression relationships in the presence of non‐constant autoregression, non‐constant residual variance, and stochastic variation in the regression effects across time. Seasonal affective disorder )SAD( time series data illustrate the methodology. I model the seasonally changing regression relationships between the change in daily sunlight )from the previous day( and depressive mood and between the change in daily temperature and depressive mood for a SAD patient, and I employ an empirical Bayes method to obtain efficient month‐specific estimates for these relationships.