bluuebunny/crossref_metadata_2025_split huggingface dataset:
Polio trial: An early efficient clinical trial
Authors: Meier Paul 
Published: April 2007

AbstractThe Salk Vaccine Field Trial was a randomized, placebo‐controlled trial designed to test the efficacy of the Salk killed virus vaccine. Although the trial was not without some problems, both in design and analysis, none was important enough to raise serious questions about the results. Thus, the design of the Field Trial was well adapted to the demanding requirements. The trial owes its success in a large part to substantial public involvement. In designing future, large‐scale, simple trials, we may still be able to benefit from the lessons of the Polio Vaccine Trial.
Cost and efficiency considerations in community‐based trials of vitamin A in developing countries
Authors: Tielsch James M. , West Keith P. 
Published: April 2007

AbstractRecent investigations conducted in Indonesia have identified an important role for adequate vitamin A nutriture in preventing mortality in young children. A randomized, community‐based trial of vitamin A supplementation in northern Sumatra demonstrated a 34 per cent reduction in mortality among pre‐school aged children who received twice yearly vitamin A supplementation. These results have stimulated renewed interest in vitamin A as a potentially important mediator of improved child survival. Cost and efficiency considerations are important in these types of trials but must be balanced with logistical constraints that are unique to these environments. A number of these issues are discussed and some general guidelines are proposed for such investigations.
Selection of patients for randomized controlled trials: Implications of wide or narrow eligibility criteria
Authors: Yusuf Salim , Held Peter , Teo K. K. , Toretsky Elizabeth Rudin 
Published: April 2007

AbstractThis paper discusses the various philosophies that influence the selection of patients for entry into randomized controlled trials. Although a number of different and often competing issues have to be considered depending upon the trial, keeping entry criteria simple, wide and at times even flexible is usually preferable. Such a strategy can be a positive virtue by helping to attain the large numbers of patients that are usually needed to reliably detect the sorts of moderate benefits that are plausible, at a reasonable cost and by providing answers that are relevant to many different categories of patients with a particular condition.
The use of a run‐in to enhance compliance
Authors: Lang Janet M. 
Published: April 2007

AbstractWith a run‐in, potential subjects are given practice with all or part of the study protocol prior to randomization. The purpose is to exclude poor compliers from the trial and randomize only proven, good compliers. The feasibility of a run‐in and the net benefit derived from that strategy depend upon the difficulty of the protocol; the availability of compliance aids during the trial; and the impact of the chosen common practices for all subjects during the run‐in on the post‐randomization event rates of the groups and the post‐randomization blinding of study subjects to their treatment assignments. Two detailed examples of the use of a run‐in are presented along with six general recommendations to guide the use of a run‐in.
An evaluation of central laboratories in three VA cooperative studies
Authors: Peduzzi Peter , Hartigan Pamela , Johnson Gary 
Published: April 2007

AbstractWe compared central laboratory with local determinations of key clinical measurements in three VA Cooperative Studies. Electrocardiographic evidence of new myocardial infarction was assessed in the study of Aspirin Therapy and Unstable Angina, ejection fraction measurement in the Coronary Artery Bypass Surgery Trial and lesion size in the Angioplasty Compared with Medicine (ACME) Trial. The findings in the Aspirin Trial indicated that central coding of all serial electrocardiograms in 1266 patients to detect new acute myocardial infarction by computer algorithm was not cost‐effective when compared with the local investigator's diagnosis on the basis of a central Electrocardiographic Committee as the reference standard. In the other two trials, the contribution of the central laboratories was important because the assessments of the local investigators generally underestimated the degree of abnormality in the Bypass Trial and overestimated it in the ACME Trial. The VA results have clearly demonstrated two cases in which the decision for central evaluation was prudent, but one case in which it was not cost‐effective. These equivocal findings indicate the need to evaluate the contribution of central laboratories when used as an adjunct to local determinations. Such evaluations may provide guidelines for decision‐making in the design of future trials.
Using cost‐effectiveness analysis to improve the efficiency of allocating funds to clinical trials
Authors: Detsky Allan S. 
Published: April 2007

AbstractThis study applied a cost‐effectiveness model to seven randomized trials. The model demonstrates the effect of design choices made in the planning stages of a clinical trial on the costs and benefits derived from conducting the trial. The study focused on one parameter used to calculate sample size: the minimum clinically important difference in event rates between control and experimental therapies. The study shows that the model can be operationalized to these trials. A computerized software package and manual has been developed to simplify the calculations. While there was some variation in the incremental cost‐effectiveness ratios across the seven trials in this study, all ratios may be below the funding threshold. This analytical technique can be used to demonstrate explicitly the resource consequences of the design of randomized trials and perhaps to set funding priorities.
The applicability of the results of streamlined trials to clinical practice
Authors: Wilhelmsen Lars 
Published: April 2007

AbstractDuring recent decades we have seen an encouraging development of trial methodology. Less effort, however, has been devoted to the application of findings of trials to the practice of medicine. We cannot expect results of controiled trials alone to determine standard therapy, for clinical judgements are also required. Both beneficial and adverse effects are often only detectable on the group level. There are generally very few indications that drug effects differ qualitatively in subgroups of patients. It is important not only to prove or disprove effects, but also to find out how to implement a proper therapy for a majority of eligible patients. Four examples from treatment after myocardial infraction show that drug usage may not always be based upon results of clinical trials. The adoption of Swedish guidelines for thrombolytic therapy in acute myocardial infarction is taken as an example of an apparently optimal application of trial results in practice.
Incorporating historical control data in planning phase II clinical trials
Authors: Thall Peter F. , Simon Richard 
Published: April 2007

AbstractPhase II studies of new medical treatments often use historical data on the standard treatment for comparative evaluation. Incorrectly disregarding inherent variability in the historical data may lead to erroneous conclusions regarding the efficacy of the experimental treatment. We propose an approach to phase II trial design which accounts for both inter‐study and intra‐study variation. Our results indicate that it is sometimes best to randomize a proportion of the patients to a control arm. We choose this proportion to maximize the precision of the estimated experimental treatment effect. We evaluate operating characteristics of the design numerically, and provide illustrations based on historical data from cancer chemotherapy trials.
Modelling peritonitis rates and associated risk factors for individuals on continuous ambulatory peritoneal dialysis
Authors: Vonesh Edward F. 
Published: April 2007

AbstractA mixed effects Poisson regression model is proposed for analysing potential risk factors associated with peritonitis, a bacterial infection of the peritoneum which is common among individuals on continuous ambulatory peritoneal dialysis (CAPD). The model incorporates a set of fixed effects corresponding to concomitant information collected across individuals as well as a random effect due to individuals. The method of maximum likelihood is used to estimate the unknown parameters. When applied to clinical data obtained on 81 CAPD patients from four centres, the mixed effects model demonstrated a much better fit than the corresponding fixed effects Poisson regression model.
Application of the Cox model as a predictor of relative risk of coronary heart disease in the albany study
Authors: Chang Hwa‐Gan H. , Lininger Lloyd L. , Doyle Joseph T. , Maccubbin Patricia A. , Rothenberg Richard B. 
Published: April 2007

AbstractPatients in long term studies of coronary heart disease may have different levels of risk during the course of study. Smoking habits, blood pressure, and obesity may change drastically during this period. The multiple logistic model, the most commonly used model for the analysis of coronary heart disease studies, does not consider survival time in assessment of the dependent covariates and does not account for the censoring which usually occurs in such studies. We propose a Cox model with time‐dependent covariates to model the risk of coronary heart disease in the Albany study. The Cox model we fitted evaluates the patients' risk on the basis of the data at the last visit. With this methodology, we can evaluate whether it is advantageous for individuals to modify their risk of disease by their effecting changes in their covariates, that is to stop smoking, lose weight, change diet and so on. The important covariates that explain the risk of coronary heart disease were the same in our model as in the models used in the earlier reports. The estimated relative risks were slightly higher in most cases and lend more support to the need to encourage patients to achieve a better covariate state.
Disease entities, mixed multi‐normal distributions, and the role of the hyperkinetic state in the pathogenesis of hypertension
Authors: Schork Nicholas J. , Weder Alan B. , Schork M. Anthony , Bassett David R. , Julius Stevo 
Published: April 2007

AbstractThis paper concerns the theory and relevance of finite mixtures of univariate and multivariate normal distributions in medical research and suggests that multivariate normal mixture analysis, hitherto not extensively explored, is an appealing approach to the investigation of etiologically obscure, multifactorial diseases such as hypertension. We elaborate a statistical strategy to resolve and test for a normal mixture distribution in a seemingly heterogenous population actually comprising homogenous subpopulations. We use this strategy to validate the hypothesis that in the population at large there is a subgroup of individuals with the characteristic of a hyperkinetic circulatory state, defined as the association of an elevated cardiac index or heart rate with high blood pressure. This subgroup may have implications for the pathogenesis of hypertension. We discuss directions and implications for future research into the pathogenesis of hypertension.
The assessment of methods of measurement
Authors: Chinn Susan 
Published: April 2007

AbstractCriteria are given for the choice of scale prior to estimation of repeatability. Recommendations of Bland and Altman should then be used for expressing repeatability and agreement of methods of measurement on the same scale. Repeatability of measurements on different scales should be compared using the appropriate ratio of variances, or intraclass correlation coefficient. A reference range for diagnosis requires a high ratio of between‐subject variation to total variation. The index of separation between diseased and healthy subjects should be used whenever possible. Changes within patients should be compared with reference change ranges, and not against the diagnostic range.
Yates's correction for continuity and the analysis of 2 × 2 contingency tables
Authors: Haviland Mark G. 
Published: April 2007

AbstractDespite recommendations to the contrary, medical researchers still routinely use the Yates‐corrected chisquare statistic in analyses of 2 × 2 contingency tables. Research has shown that these ‘corrected’ statistics are overly conservative and that the conventional Pearson chi‐square generally provides adequate control over type I error probabilities. This paper makes a straightforward argument against use of Yates's correction for continuity and Fisher's exact probability test.
Application of new two‐sample tests to data from a randomized placebo‐controlled heart‐failure trial
Authors: Tandon P. K. , Stander Herbert , Schwarz Richard P. 
Published: April 2007

AbstractIn a randomized placebo‐controlled double‐blind trial of 230 congestive heart failure patients, four treatments were evaluated for efficacy, with exercise tolerance time (ETT) as the primary outcome. Various two‐sample tests were applied to the analysis of ETT data. It is shown in this paper that the conventional two‐sample tests (t and rank‐sum) are insensitive to situations where the effect of the experimental therapy is not consistent across a patient population. Tests recommended by O'Brien are more appropriate for these data. It is also shown that the application of the O'Brien tests led to the identification of sub‐groups where the observed effect of the experimental therapy was most pronounced.
MODEL SELECTION AND THE ESTIMATION OF ODDS RATIOS IN THE PRESENCE OF EXTRANEOUS FACTORS
Authors: Schall Robert , Zucchini Walter 
Published: April 2007

AbstractThis paper deals with model selection and the estimation of odds ratios from cross‐classified frequencies in the presence of extraneous factors. The odds ratio is estimated in different ways dependent on whether the extraneous factor is modelled as an effect modifier, a confounder, or neither. Routinely this choice is based on statistical tests of null hypotheses. By contrast, we propose selection of the model which is estimated to maximize the accuracy of the estimator of the odds ratio, on average. We demonstrate how a non‐parametric bootstrap method can be used to carry out the selection, and illustrate the methodology using an example on use of oral contraceptives and myocardial infarction.
Estimation of parasitic infection dynamics when detectability is imperfect
Authors: Nagelkerke Nico J. D. , Chunge Ruth N. , Kinoti Stephen N. 
Published: April 2007

AbstractThe simultaneous estimation of infection rate, cure rate and detectability of parasitic infections is considered. A new method for this estimation based on a simple statistical model assuming constant transition rates between parasite states is proposed. Repeated observations on the infection status of the same individuals is required for this method. A maximum likelihood approach is used for parameter estimation and the calculation of standard errors of the estimates. The method is illustrated by a longitudinal study of the presence of Giardia lamblia infection in Kenyan children.
An improved approximation for calculation of confidence intervals after a sequential clinical trial
Authors: Facey Karen M. , Whitehead John 
Published: April 2007

AbstractA method for the calculation of confidence intervals following a sequential clinical trial is proposed which is more accurate than methods based solely on continuous monitoring assumptions, but is not as computationally laborious as previous methods suggested for group sequential trials. The calculation takes account of the excess of the sample path over the boundary at the final inspection of the trial, which may be substantial after group sequential monitoring, and is ignored when continuous monitoring is assumed. Accuracy of such confidence intervals after a triangular test is investigated by simulation of coverage probabilities, individual confidence limit probabilities and p‐value curves.
Predictive value of statistical models
Authors: Van Houwelingen J. C. , Le Cessie S. 
Published: April 2007

AbstractA review is given of different ways of estimating the error rate of a prediction rule based on a statistical model. A distinction is drawn between apparent, optimum and actual error rates. Moreover it is shown how cross‐validation can be used to obtain an adjusted predictor with smaller error rate. A detailed discussion is given for ordinary least squares, logistic regression and Cox regression in survival analysis. Finally, the split‐sample approach is discussed and demonstrated on two data sets.
Principles, problems and a paradox with the measurement of energy expenditure of free‐living subjects using doubly‐labelled water
Authors: Speakman J. R. 
Published: April 2007

AbstractMany aspects of the study of human nutrition would benefit from the ability to measure the energy which is expended by subjects as they go about their normal activities. The doubly‐labelled water technique is a method which allows such measures to be made. The technique depends on the principle that a measure of carbon dioxide production is possible from the difference in the rates at which isotopic labels of hydrogen and oxygen are eliminated from the body. This simple explanation however conceals a host of assumptions. Several issues have emerged as important in the application of the technique to humans. These issues include first, the use of two samples (an initial and final sample) or multiple samples to estimate the rates of isotope elimination, and secondly the appropriate use of dilution spaces. This paper reviews the current status of the debates concerning these two issues. Paradoxically, improvements in our understanding of the technique, in the methods for calculating carbon dioxide production and in the accuracy of mass spectrometry, have not led to improvements in the accuracy of the technique. The mean deviation of estimates of carbon dioxide production using the technique from estimates made by conventional methods averages 7 per cent.
Adjusting for early treatment termination in comparative clinical trials
Authors: Lagakos S. W. , Lim L. L‐Y. , Robins J. M. 
Published: April 2007

AbstractIn clinical trials of long‐term therapies, patients often terminate their treatments earlier than planned. When analysing time‐to‐failure data, one approach to account for early treatment termination censors failure at the time of termination of therapy. In general, however, this does not produce valid inferences about the distribution of time to failure that would have occurred had treatment not been terminated. In contrast, intent‐to‐treat analyses, which are based on time to failure regardless of whether and when treatment is terminated, always produce valid inferences about the unconditional distribution of time to failure. Early treatment termination does not distort the size (type I error rate) of intent‐to‐treat tests but can cause a loss in power. Modifications to ordinary logrank tests can be used to recover some of the lost power without affecting test size, and can be most useful when the proportion of at‐risk patients still taking their treatment changes substantially during periods when failures are observed. Extensions of the modified test to include strata are straightforward, although important design questions require further research.
Optimum allocation of samples in strata‐matching case‐control studies when cost per sample differs from stratum to stratum
Authors: Nam Jun‐Mo , Fears Thomas R. 
Published: April 2007

AbstractWe investigate the allocation of samples over strata in case‐control studies with consideration of sampling costs. We consider two situations, typical of strata‐matched case‐control studies. Subject to a fixed total cost we derive optimum allocations that maximize the asymptotic efficiency of Cochran's test, that is, minimize the variance of the maximum likelihood estimator of the common odds ratio when the common odds ratio is one. Our results show that the standard design with equal numbers of cases and controls in each stratum is inefficient when costs per control differ greatly from stratum to stratum. We show that the optimum design suggested in this paper is robust for values of the odds ratio common in epidemiologic studies.
Sample size determination for case‐control studies: The influence of the joint distribution of exposure and confounder
Authors: Lui Kung‐Jong 
Published: April 2007

AbstractIn case‐control studies, the results about how the exposure distribution affects sample size are well known. This paper extends previous results by incorporating the effect of a confounder into the calculation of sample size for a desired size and power of a statistical test. The paper also includes a quantitative discussion on the influence of the joint distribution for exposure to a putative cause and a confounder on required sample sizes. The results show that, to detect a specified alternative for a given size and power, the required sample size decreases as either the variance of exposure or the effect of exposure on disease increases. The required sample size, however, increases as either the variance of the confounder or the effect of the confounder on disease increases. Generally, the higher is the absolute value of the simple correlation between the exposure and the confounder, the larger is the required sample size.
Relation of pooled logistic regression to time dependent cox regression analysis: The framingham heart study
Authors: D'Agostino Ralph B. , Lee Mei‐Ling , Belanger Albert J. , Cupples L. Adrienne , Anderson Keaven , Kannel William B. 
Published: April 2007

AbstractA standard analysis of the Framingham Heart Study data is a generalized person‐years approach in which risk factors or covariates are measured every two years with a follow‐up between these measurement times to observe the occurrence of events such as cardiovascular disease. Observations over multiple intervals are pooled into a single sample and a logistic regression is employed to relate the risk factors to the occurrence of the event. We show that this pooled logistic regression is close to the time dependent covariate Cox regression analysis. Numerical examples covering a variety of sample sizes and proportions of events display the closeness of this relationship in situations typical of the Framingham Study. A proof of the relationship and the necessary conditions are given in the Appendix.
Maximum likelihood regression methods for paired binary data
Authors: Lipsitz Stuart R. , Laird Nan M. , Harrington David P. 
Published: April 2007

AbstractWe discuss maximum likelihood methods for analysing binary responses measured at two times, such as in a cross‐over design. We construct a 2 × 2 table for each individual with cell probabilities corresponding to the cross‐classification of the responses at the two times; the underlying likelihood for each individual is multinomial with four cells. The three dimensional parameter space of the multinomial distribution is completely specified by the two marginal probabilities of success of the 2 × 2 table and an association parameter between the binary responses at the two times. We examine a logistic model for the marginal probabilities of the 2 × 2 table for individual i; the association parameters we consider are either the correlation coefficient, the odds ratio or the relative risk. Simulations show that the parameter estimates for the logistic regression model for the marginal probabilities are not very sensitive to the parameters used to describe the association between the binary responses at the two times. Thus, we suggest choosing the measure of association for ease of interpretation.