bluuebunny/crossref_metadata_2025_split huggingface dataset:
Interpretation of interaction in factorial analysis of variance design
Authors: Ottenbacher Kenneth J. 
Published: April 2007

AbstractThe validity of statistical conclusions in medical research depends on proper analysis and interpretation of collected data. One potential area of invalidity is the inappropriate post hoc analysis of statistically significant interactions in the analysis of variance of factorial designs. This paper examines the statistical explanations included in 83 studies published in three leading medical journals where the findings indicated significant interaction effects. Only 24 per cent of the reported statistically significant interactions had an accompanying correct interpretation. The most common form of misinterpretation involved a comparison of individual cell means within a row or column of one factor used in the design. This interpretation did not conform to the factorial ANOVA model with interaction. This misinterpretation occurs when the correct omnibus test of a hypothesis is followed by an incorrect post hoc analysis and/or an inaccurate assessment of the original statistical result.
Analysis of clinical trials by treatment actually received: Is it really an option?
Authors: Lee Young Jack , Ellenberg Jonas H. , Hirtz Deborah G. , Nelson Karin B. 
Published: April 2007

AbstractThe primary analysis of a randomized clinical trial should compare patients in their randomly assigned treatment groups (intention to treat analysis). When a substantial number of subjects fail to take a prescribed medication or are switched to a different study medication, it is tempting to consider treatment comparisons using only those subjects with treatment as actually received rather than as prescribed. There are several arguments against this approach: the prognostic balance brought about by randomization is likely to be disturbed; sample size will be reduced; and the validity of the statistical test procedures will be undermined. Further, results of analysis by treatment actually received may suffer from a bias introduced by using compliance, a factor often related to outcome independently of the treatment received, to determine the groups for comparison. The extent and nature of this bias will be related to the definition of compliance in an as treated analysis, a definition which could be unintentionally self‐serving.We have investigated the problem of the definition of actual treatment in the context of a recent clinical trial. We used several definitions to classify patients as having received or not received treatment as prescribed. These definitions, when used in as treated analyses, provided results that were at times inconsistent or counter‐intuitive, and which neither helped to confirm nor further explain the intention to treat analysis.
The analysis of categorical data from cross‐over trials using a latent variable model
Authors: Kenward Michael G. , Jones Byron 
Published: April 2007

AbstractA latent variable model for categorical data is described. The nuisance parameters in the model can be eliminated from the analysis through the use of a conditional likelihood and this leads to an analysis based on a log‐linear model. It is shown how the structure of a cross‐over trial allows considerable simplification in the formulation of this model and routine application of well‐known statistical packages. The method is illustrated by data from a three‐period cross‐over trial on the relief of primary dysmenorrhea using the GLIM and SAS packages.
The healthy responder phenomenon in non‐randomized clinical trials
Authors: Hallstrom Alfred P. , Greene H. Leon , Huther Melissa L. ,   AND THE CAST INVESTIGATORS
Published: April 2007

AbstractAn example from the Cardiac Arrhythmia Suppression Trial (CAST) illustrates what we term the ‘healthy responder’ phenomenon. The hypothesis is that patients who respond to a given treatment are healthier than patients who do not respond. In observational studies this results in an apparent but not real benefit for the treatment. The unique design of CAST, namely, titration before randomization, allows illustration of this phenomenon since we can view the study as both a non‐randomized as well as a randomized trial of therapy. We conclude that, in demonstration of a drug effect, randomized trials are essential.
Analysis of failure time data with ordinal categories of response
Authors: Berridge Damon M. , Whitehead John 
Published: April 2007

AbstractWhen failure times are observed, additional information concerning the type of failure is often recorded. A method which simultaneously models the failure times and additional information in the form of ordinal categories is discussed. An application to clinical trial data, in which the failure times are times of onset of headache, and the headaches are classified into the ordinal categories mild, moderate and severe, illustrates how this method may be used and how the final model can be interpreted. The continuation ratio model, which is used in this method, is described in detail.
The detection of artefacts in eeg series
Authors: Dunstan F. D. J. , Marshall R. W. 
Published: April 2007

AbstractIn a clinical trial of an anticonvulsant drug, series of electroencephalogram readings are sometimes obtained. These may contain artefacts, that is patches of atypical values which must be identified and either removed or compensated for before a full analysis can be carried out. Methods for identifying such artefacts are discussed. These include non‐parametric methods and also parametric ones combining the ideas of autoregressive modelling and of influence in regression. These methods are described and illustrated on typical data sets.
Subacute sclerosing panencephalitis in England and Wales: Transient effects and risk estimates
Authors: Farrington C. P. 
Published: April 2007

AbstractData on cases of subacute sclerosing panencephalitis (SSPE) with onset in 1970‐89 show a marked increase in the interval from initial measles attack to onset of SSPE by year of onset. It is shown that this increase is a transient effect resulting from the decline in measles incidence following the introduction of mass vaccination in 1968. The risk of SSPE after measles is estimated to be 4.0 × 10−5 (18 × 10−5 after measles under one year of age) and the risk after vaccination to be no greater than 0.14 × 10−5, thus confirming that vaccination is effective in reducing the incidence of SSPE. The paper makes use of methods developed to estimate the incubation period of AIDS.
Estimation of relative risk functions
Authors: Bithell J. F. 
Published: April 2007

AbstractThe risk associated with different levels of a quantitative factor X is often measured relative to the level corresponding to X = 0. There are situations, however, where there is no natural zero for X, for example where the risk factor is the age of an individual. In this case it is more natural to measure risk relative to an overall average for the study population. To use the whole population in this way also raises the possibility of regarding X as truly continuous, rather than as a grouped variable. This gives rise to the concept of a relative risk function. Methods for estimating such functions are discussed, concentrating for the most part on the discrete case. The extension to higher dimensions permits the investigation of joint effects of several factors, while the problem of controlling for confounding variables can be handled by fitting multiplicative risk models. Relating the latter to the log‐linear model permits the estimation of adjusted relative risk functions. The method is illustrated using data on childhood cancer. The continuous case can in principle be handled in a similar way using density estimation techniques.
A decade of progress in statistical methodology for clinical trials
Authors: Simon Richard 
Published: April 2007

AbstractClinical trials played a dominant and expanding role in the evaluation of new treatments during the decade of the 1980s. There were major improvements in the quality of clinical trials in many medical fields. There were also important developments in the methodology of designing, monitoring, conducting, analysing, reporting and interpreting clinical trials. This paper attempts to review some of these developments. A comprehensive review is beyond the abilities of any one individual. Consequently, this paper attempts to offer a broad stroke description of this area and to highlight specific topics of importance based on my particular experience. An extensive, but non‐comprehensive bibliography is included to provide entry points to the literature of methodologic developments for clinical trials in the 1980s.
Sampling design of multiwave studies with an application to the Massachusetts health care panel study
Authors: Chappell Rick 
Published: April 2007

AbstractA technique is presented which provides guidance on the spacing of follow‐up waves in a multiwave study. Only information from the baseline wave is needed, as well as rough parameter estimates for the survival distribution. The computations use the expected Fisher information; a new method for its calculation is given. An analysis of the effects of simplifying assumptions, such as presuming that a distributional parameter is known, shows that gross discrepancies may result. Data from the Massachusetts Health Care Panel Study (MHCPS) are used for a demonstration. The MHCPS example shows that this technique can prevent large inefficiencies in multiwave experimental design.
Semi‐parametric and non‐parametric methods for the analysis of repeated measurements with applications to clinical trials
Authors: Davis Charles S. 
Published: April 2007

AbstractTechniques applicable for the analysis of longitudinal data when the response variable is non‐normal are not nearly as comprehensive as for normally‐distributed outcomes. However, there have been several recent developments. Semi‐parametric and non‐parametric methodology for the analysis of repeated measurements is reviewed. The commonly encountered design in which, for each subject, one assesses a univariate response variable at multiple fixed time points, is considered. The types of outcomes considered include binary, ordered categorical, and continuous (but extremely non‐normal) response variables. All of the methods considered allow for incomplete data due to the occurrence of missing observations. In addition, discrete and/or continuous covariates, which may be time‐dependent, are accommodated by some of the approaches. The methods are demonstrated using data from three clinical trials.
Application of the McNemar test to non‐independent matched pair data
Authors: Eliasziw Michael , Donner Allan 
Published: April 2007

AbstractMcNemar's one degree of freedom chi‐square test for the equality of proportions appears frequently in the analysis of pairs of matched, binary outcome data (Y1i, Y2i). An assumption underlying this test is that the responses from pair to pair are mutually independent. In certain applications, however, the pairs may represent repeated measurements on the same experimental unit, and hence this assumption is violated. In this paper we suggest an adjustment to the McNemar test to account for the repeated measures clustering effect and we report on a Monte Carlo simulation that evaluates the effectiveness of this approach.
Smoothing reference centile curves: The lms method and penalized likelihood
Authors: Cole T. J. , Green P. J. 
Published: April 2007

AbstractReference centile curves show the distribution of a measurement as it changes according to some covariate, often age. The LMS method summarizes the changing distribution by three curves representing the median, coefficient of variation and skewness, the latter expressed as a Box‐Cox power. Using penalized likelihood the three curves can be fitted as cubic splines by non‐linear regression, and the extent of smoothing required can be expressed in terms of smoothing parameters or equivalent degrees of freedom. The method is illustrated with data on triceps skinfold in Gambian girls and women, and body weight in U.S.A. girls.
Optimal checking procedures for monitoring laboratory analyses
Authors: McShane Lisa M. , Turnbull Bruce W. 
Published: April 2007

AbstractMany clinical, environmental, and epidemiologic studies rely heavily upon biochemical data, and the quality of these data is of paramount importance to the validity of study conclusions. Traditionally, far more attention has been given to the analysis of study data than has been given to monitoring the quality of the data. In this paper we draw an analogy between monitoring a laboratory system and an industrial production process and discuss the limitations of industrial quality control plans when applied in a laboratory setting. We derive methods for computing optimal checking schedules for laboratory analyses. These schedules formalize traditional laboratory practices of periodic checking and provide guidelines for the frequency and placement of checks within a finite batch of analyses. When laboratory system failure can be reasonably approximated by an exponential or geometric distribution, optimal checking schedules are relatively easy to compute. For more complex failure distributions, we present a dynamic programming approach. We describe an application to the measurement of selenium status in plasma samples using an electrothermal atomic absorption spectrophotometry procedure.
Sample size determination for group sequential clinical trials with immediate response
Authors: Kim Kyungmann , Demets David L. 
Published: April 2007

AbstractThe use function approach to group sequential methods has been explored previously. Any group sequential design requires specifying the frequency and times of repeated analyses, but only the use function approach allows deviations from those specified in the design, without affecting the type I error in the analysis. This paper illustrates how the use function provides a simple and flexible design procedure and how the initially projected maximum sample size can be calculated for randomized clinical trials in which responses are known relatively soon after patient entry and for which there is an early stopping rule built into the study protocol. Also the consequence of using the proposed design procedure is investigated in terms of the operating characteristics of the subsequent group sequential analyses.
Study duration for group sequential clinical trials with censored survival data adjusting for stratification
Authors: Kim Kyungmann 
Published: April 2007

AbstractThe study duration in a clinical trial with censored survival data is the sum of the accrual duration, which determines the sample size in a traditional sense, and the follow‐up duration, which more or less controls the number of events to be observed. We propose a design procedure for determining the study duration or for calculating the power in a group sequential clinical trial with censored survival data and possibly unequal patient allocation between treatments, adjusting for stratified randomization. The group sequential method is based on the use function approach. We describe a clinical trial recently activated by the Eastern Cooperative Oncology Group for an illustration of the proposed procedure.
A goodness‐of‐fit approach to inference procedures for the kappa statistic: Confidence interval construction, significance‐testing and sample size estimation
Authors: Donner Allan , Eliasziw Michael 
Published: April 2007

AbstractWe propose a new procedure for constructing a confidence interval about the kappa statistic in the case of two raters and a dichotomous outcome. The procedure is based on a chi‐square goodness‐of‐fit test as applied to a model frequently used for clustered binary data. The procedure provides coverage levels that are accurate in samples of smaller size than those required for other procedures. The procedure also has use for significance‐testing and the planning of corresponding sample size requirements.
Analysis of aberrations in public health surveillance data: Estimating variances on correlated samples
Authors: Kafadar Karen , Stroup Donna F. 
Published: April 2007

AbstractThe detection of unusual patterns in health data presents an important challenge to health workers interested in early identification of epidemics or important risk factors. A useful procedure for detection of aberrations is the ratio of a current report to some historic baseline. This work addresses the problem of finding the variance of such a ratio when the surveillance reports are correlated. Results show that, when estimating this variance or the variance of the sample mean from a series of observations with an estimated correlation structure, bootstrap and jackknife estimates may be overly optimistic. The delta method or a classical method may be more useful when such model dependence is inappropriate.
The epidemic dynamics of HIV‐1 in Italy: Modelling the interaction between intravenous drug users and heterosexual population
Authors: Arcà Massimo , Perucci Carlo A. , Spadea Teresa 
Published: April 2007

AbstractData gathered through the Latium HIV Surveillance System were used in conjunction with a compartmental mathematical model to describe the transmission dynamics of HIV‐1 in Italy. In the Latium region, as in the rest of Italy, fewer than 1 in 5 cumulative cases of AIDS are attributable to male homosexual transmission, while 55–60 per cent of the cases have been observed among intravenous drug users (IVDUs). Moreover, the number of non‐drug‐using heterosexual cases is increasing (14 per cent of cumulative cases). Anonymous notification of positive HIV‐1 tests, mandatory in Latium since 1985, were used to produce a time series of new HIV‐1 diagnoses; just over 400 new cases of infection per quarter were diagnosed from mid 1989 to mid 1990, with no evidence of increasing incidence. A minimum of 6009 and a maximum of 10,000 individuals with HIV‐1 as of the end of 1989 were estimated, 80 per cent of adult cases occurring among IVDUs. The model included two main subgroups: IVDUs and non‐drug‐using heterosexuals, both with behavioural heterogeneities. Sharing of needles among IVDUs and heterosexual contact were considered as possible ways of transmitting HIV‐1. A mathematical framework was developed to reproduce different mixing patterns within and between subgroups. A scenario analysis of the model showed incident cases of HIV‐1 among IVDUs peaking early, then declining dramatically and stabilising at low values, with a stable‐state prevalence higher than 0.75. Heterosexual interaction with IVDUs resulted in a significant, but non‐self‐sustaining, virus spread in the general population, affecting females more than males. The extent of this spread is associated with the assortativeness of the sexual mixing pattern adopted. The qualitative features of the Italian epidemic are well represented by the model, which highlights the role of IVDUs as an infection reservoir. However, the need emerges for more accurate information on the key parameters influencing the transmission dynamics of HIV‐1.
Repeated measures in clinical trials: Analysis using mean summary statistics and its implications for design
Authors: Frison Lars , Pocock Stuart J. 
Published: April 2007

AbstractThis paper explores the use of simple summary statistics for analysing repeated measurements in randomized clinical trials with two treatments. Quite often the data for each patient may be effectively summarized by a pre‐treatment mean and a post‐treatment mean. Analysis of covariance is the method of choice and its superiority over analysis of post‐treatment means or analysis of mean changes is quantified, as regards both reduced variance and avoidance of bias, using a simple model for the covariance structure between time points. Quantitative consideration is also given to practical issues in the design of repeated measures studies: the merits of having more than one pre‐treatment measurement are demonstrated, and methods for determining sample sizes in repeated measures designs are provided. Several examples from clinical trials are presented, and broad practical recommendations are made. The examples support the value of the compound symmetry assumption as a realistic simplification in quantitative planning of repeated measures trials. The analysis using summary statistics makes no such assumption. However, allowance in design for alternative non‐equal correlation structures can and should be made when necessary.
Prediction of short‐term survival with an application in primary biliary cirrhosis
Authors: Hughes Michael D. , Raskino Claire L. , Pocock Stuart J. , Biagini Maria Rosa , Burroughs Andrew K. 
Published: April 2007

AbstractMany long‐term follow‐up studies for survival accumulate repeated measurements of prognostic factors. Survival models which include only covariate values at baseline do not use all available information, and do not relate to survival predictions for times other than at that baseline. Time‐dependent covariate models (which update covariate values as measurements occur through time) might be used, though limitations of software for estimating the underlying hazard functions and difficulty in relating hazard function changes to survival prediction present serious drawbacks. By dividing each patient's follow‐up into successive intervals of equal length (using a length of interest for prediction) and with measurements available at the start of each, we describe how an analysis taking person‐intervals as the observation units can be undertaken using readily available software to produce short‐term survival models. We show that this approach is related to both the baseline and time‐dependent covariate models. The method is illustrated using data from a long‐term study of patients with primary biliary cirrhosis, where interest is in short‐term survival predictions to aid the decision when to undertake liver transplantation.
Importance of good nonlinear analysis
Authors: Krawinkler Helmut 
Published: April 2007

AbstractNonlinear analysis is becoming a popular tool for performance evaluation of structural systems at the life safety and collapse prevention levels. The challenge is to develop analytical tools and component models that make it possible to conduct such analysis with a sufficient degree of confidence. An exact replication of reality is desirable but not critical because of many sources of uncertainty, such as ground motion intensity and frequency content, and modeling uncertainties, which cannot be eliminated by the most accurate analysis. As summarized in this paper, there are several options to nonlinear analysis, and the simplest one that gets close to reality is usually the best one. Observation of behavior during the analysis (whether pushover or time history) is perhaps more important than a rigorous quantitative evaluation based on maximum recorded force or deformation values. In the author's opinion, the main objective of nonlinear analysis is to understand behavior rather than fulfill code‐specified limit values. This paper presents arguments why this is a critical issue, even though it is understood that reality in engineering practice usually dictates priority to fulfilling code specified limits. Copyright © 2006 John Wiley & Sons, Ltd.