bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exact unconditional tables for significance testing in the 2 × 2 multinomial trial
Authors: Shuster Jonathan J. 
Published: April 2007

AbstractThis paper presents tables analogous to T‐tables for use in the 2 × 2 multinomial trial, where the continuity corrected Z‐statistic is used to make exact unconditional inference. This is the first solution of a discrete exact unconditional inference problem involving a multivariate nuisance parameter for which no ancillary statistic exists.
Parametric versus non‐parametric methods for estimating cure rates based on censored survival data
Authors: Cantor Alan B. , Shuster Jonathan J. 
Published: April 2007

AbstractIf a patient's failure time is incorrectly recorded as being too early, the correction will lower the plateau of the Kaplan‐Meier curve and, hence, the associated estimated cure rate. Implications of this counter‐intuitive observation are discussed. In addition, a parametric approach, based on the Gompertz distribution, to the problem of cure rate estimation is presented.
Bias in relative odds estimation owing to imprecise measurement of correlated exposures
Authors: Phillips Andrew N. , Smith George Davey 
Published: April 2007

AbstractA series of graphs is presented that show the estimated degree of bias in logistic coefficient estimates for two correlated continuous exposures measured with imprecision. These graphs indicate that even when the correlation coefficient between the exposure of interest and a correlated exposure is as low as 0·2, imprecision in the measurement of the latter exposure can result in at least as serious bias in the logistic coefficient estimate for the exposure of interest as measurement imprecision in the exposure of interest itself. The implications for the design and interpretation of epidemiological studies are discussed.
Analysis of premature ventricular counts in long term ecg following myocardial infarction
Authors: Laor Arie , Cohen Ayala 
Published: April 2007

AbstractFrequent and recurrent ventricular premature beats (VPB's) are considered to be associated with a higher risk of sudden cardiac death, particularly for survivors of myocardial infarction (MI). The distribution of VPB's has a large probability mass at zero, and a very heavy right hand tail. In this research, we fitted a model to VPB for patients for which VPB was present. The model was fitted on the basis of a relatively large data set on MI survivors in Israel. The model was fitted by a method which is based on the generalized linear model. This method, which was introduced by Zeger and Liang, is designed for longitudinal data and uses the quasi‐likelihood concept. No specific assumptions are required on the shape of the distribution of the dependent variable. The results indicate that the model fits the data quite well but underestimates the very extreme high values. This research demonstrates the applicability of generalized linear models for longitudinal non‐Gaussian data. Such data often arise in medical studies. The study also points out the distributional properties of VPB counts. In particular, it shows their associations with simple clinical and epidemiological variables, and with certain time periods during the day.
Divergent biases in ecologic and individual‐level studies
Authors: Greenland Sander 
Published: April 2007

AbstractSeveral authors have shown that ecologic estimates can be biased by effect modification and misclassification in a different fashion from individual‐level estimates. This paper reviews and discusses ecologic biases induced by model misspecification; confounding; non‐additivity of exposure and covariate effects (effect modification); exposure misclassification; and non‐comparable standardization. Ecologic estimates can be more sensitive to these sources of bias than individual‐level estimates, primarily because ecologic estimates are based on extrapolations to an unobserved conditional (individual‐level) distribution. Because of this sensitivity, one should not rely on a single regression model for an ecologic analysis. Valid ecologic estimates are most feasible when one can obtain accurate estimates of exposure and covariate means in regions with internal exposure homogeneity and mutual covariate comparability; thus, investigators should seek out such regions in the design and analysis of ecologic studies.
An overview of methods for the analysis of longitudinal data
Authors: Zeger Scott L. , Liang Kung‐Yee 
Published: April 2007

AbstractThis paper reviews statistical methods for the analysis of discrete and continuous longitudinal data. The relative merits of longitudinal and cross‐sectional studies are discussed. Three approaches, marginal, transition and random effects models, are presented with emphasis on the distinct interpretations of their coefficients in the discrete data case. We review generalized estimating equations for inferences about marginal models. The ideas are illustrated with analyses of a 2 × 2 crossover trial with binary responses and a randomized longitudinal study with a count outcome.
Evaluation of alternative statistical methods for linear model analysis to compare two treatments for 24‐hour blood pressure response
Authors: Turney Elizabeth A. , Amara Ingrid A. , Koch Gary G. , Stewart William H. 
Published: April 2007

AbstractThis paper evaluates alternative statistical methods for the analysis of 24‐hour blood pressure data from a clinical trial which compares two treatments for hypertension. The primary objective of the study discussed here was to determine the time course for the blood pressure lowering effects of a test drug given once daily in the treatment of mild to moderate hypertension when compared with placebo. Thirty‐five patients (24 on drug and 11 on placebo) were monitored for 24 hours at baseline and at two weeks post treatment, with diastolic blood pressure (DBP) measurements recorded at 22 time points within each 24‐hour visit. The changes in DBP from baseline across the 22 time points are the response variables of interest. Various statistical methods for the assessment of treatment effects over the entire 24‐hour dosing interval in a setting with small sample size are discussed and illustrated. The results from a special application of weighted least squares analysis of covariance, which employs a smoothed covariance matrix, support the hypothesis that a once daily dose of the drug significantly reduces DBP over the entire 24‐hour dosing interval when compared with placebo. This method has the distinct advantage of enabling evaluation of treatment differences for the change in DBP from baseline at the 22 time points with the corresponding 22 baseline DBP as covariates simultaneously in a situation where the treatment group sample sizes are small.
Methods for the analysis of informatively censored longitudinal data
Authors: Schluchter Mark D. 
Published: April 2007

AbstractThis paper describes the problem of informative censoring in longitudinal studies where the primary outcome is rate of change in a continuous variable. Standard approaches based on the linear random effects model are valid only when the data are missing in a non‐ignorable fashion. Informative censoring, which is a special type of non‐ignorably missing data, occurs when the probability of early termination is related to an individual subject's true rate of change. When present, informative censoring causes bias in standard likelihood‐based analyses, as well as in weighted averages of individual least‐squares slopes. This paper reviews several methods proposed by others for analysis of informatively censored longitudinal data, and outlines a new approach based on a log‐normal survival model. Maximum likelihood estimates may be obtained via the EM algorithm. Advantages of this approach are that it allows general unbalanced data caused by staggered entry and unequally‐timed visits, it utilizes all available data, including data from patients with only a single measurement, and it provides a unified method for estimating all model parameters. Issues related to study design when informative censoring may occur are also discussed.
The accelerated failure time model: A useful alternative to the cox regression model in survival analysis
Authors: Wei L. J. 
Published: April 2007

AbstractFor the past two decades the Cox proportional hazards model has been used extensively to examine the covariate effects on the hazard function for the failure time variable. On the other hand, the accelerated failure time model, which simply regresses the logarithm of the survival time over the covariates, has seldom been utilized in the analysis of censored survival data. In this article, we review some newly developed linear regression methods for analysing failure time observations. These procedures have sound theoretical justification and can be implemented with an efficient numerical method. The accelerated failure time model has an intuitive physical interpretation and would be a useful alternative to the Cox model in survival analysis.
An analysis of two‐period crossover designs with carry‐over effects
Authors: Laird Nan M. , Skinner Jonathan , Kenward Michael 
Published: April 2007

AbstractThe crossover design is a type of longitudinal study with subjects receiving different treatments in different time periods. When carry‐over effects are absent, the usual crossover design is structured so that all the information about treatment effects is contained in the within‐subject contrasts; standard analyses are based on these within‐subject contrasts and ignore any between‐subject information. With carry‐over effects present these standard analyses can be very inefficient, especially for suboptimal designs. We describe alternative approaches based on methods for the analysis of longitudinal data.
Random effects models with non‐parametric priors
Authors: Butler Steven M. , Louis Thomas A. 
Published: April 2007

AbstractWe discuss the performance of non‐parametric maximum likelihood (NPML) estimators for the distribution of a univariate random effect in the analysis of longitudinal data. For continuous data, we analyse generated and real data sets, and compare the NPML method to those that assume a Gaussian random effects distribution and to ordinary least squares. For binary outcomes we use generated data to study the moderate and large‐sample performance of the NPML compared with a method based on a Gaussian random effect distribution in logistic regression. We find that estimated fixed effects are compatible for all approaches, but that appropriate standard errors for the NPML require adjusting the likelihood‐based standard errors. We conclude that the non‐parametric approach provides an attractive alternative to Gaussian‐based methods, though additional evaluations are necessary before it can be recommended for general use.
Importance of trends in the interpretation of an overall odds ratio in the meta‐analysis of clinical trials
Authors: Brand R. , Kragt H. 
Published: April 2007

AbstractThis paper contains a proposition related to the publication of meta‐analyses of clinical trials. We consider the situation where the results of a number of trials are summarized by a common or typical odds ratio. We show that stating such an odds ratio as the summary of evidence from a number of trials can be misleading if certain systematic differences between trials exist. In such cases the author should state not just one odds ratio but also its dependence on the relevant characteristics of the trials. In particular, we propose that those reporting a meta‐analysis state in advance a (limited) number of variables to be considered for potential interaction with the exposure (risk factor or treatment) of interest. The list might include centre size and the odds in the placebo or control group if such an effect is a priori clinically plausible. The trials should be ordered according to each of these variables and a trend test for the odds ratio should be computed. Apart from a ‘genuine’ effect, an appreciable interaction could also be indicative of the (multiplicative) odds ratio being an inappropriate measure for the particular meta‐analysis. Without any consideration as to the possibility of interaction, the meta‐analysis should be considered incomplete. If such an interaction exists, the odds ratio should be stated as a function of the interacting variable, either as a formula or (preferably) in a table stating the odds ratio for a number of different values of the interacting variable, and not as a single summary statistic.
Harmonic regression analysis of the effect of drug treatment on the diurnal rhythm of blood pressure and angina
Authors: Gaffney Michael , Taylor Colin , Cusenza Elizabeth 
Published: April 2007

AbstractHarmonic regression is a method for analysis of diurnal rhythms which we apply to blood pressure and angina attack rate. We show that population amplitude and phase as previously defined are not the expected values of amplitude and phase in the population unless patients are in phase and that the estimates of these population parameters are the amplitude and phase of the mean measurements of the observation times. We show that the amplitude of the mean measurements underestimates the magnitude of the diurnal rhythm of individual patients due to significant differences among patients in the phase parameter. Thus, the mean of the individual‐patient amplitudes and phases, rather than the amplitude and phase of the mean measurements, is necessary to describe the diurnal rhythm in the population and to assess the effect of drug treatment. Harmonic regression analysis yields specific hypotheses that relate directly to the consistency of drug effect over 24 hours.
On the efficacy of the rank transformation in stepwise logistic and discriminant analysis
Authors: O'gorman Thomas W. , Woolson Robert F. 
Published: April 2007

AbstractWe have evaluated the performance of four stepwise variable selection procedures commonly used in medical and epidemiologic research. The four procedures are discriminant and logistic regression and their rank transformed versions, where the independent variables are replaced by their ranks. We generated, by computer, data for two groups from several distributions with a variety of sample sizes and covariance matrices. The two ranking procedures each increased the chance of correctly selecting those variables related to group membership for data generated from log‐normal or contaminated distributions. For normally distributed data the ranking procedure had little effect on variable selection. Rank transformed discriminant analysis and rank transformed logistic regression were equally effective in selecting variables when sample sizes exceeded 100. Rank transformed discriminant analysis was superior for smaller data sets. We discuss the implications of the results of this study for clinical and epidemiologic research.
The design of observer agreement studies with binary assessments
Authors: Freedman Laurence S. , Parmar Mahesh K. B. , Baker Stuart G. 
Published: April 2007

AbstractWe discuss the design of observer agreement studies with binary assessments, with particular emphasis on the need for adequate sample size and the use of replicate observations. First, we present a method and tables for determining the sample size required for ensuring a desired precision for the estimate of the probability of disagreement between two observers. Second, for studies including replicate observations, we present a statistical model that allows estimation of the magnitude of within‐ and between‐observer variation. We then derive sample sizes guaranteeing a specified precision for these estimates, present tables of these sample sizes and give examples of their use.
A bivariate survival model with modified gamma frailty for assessing the impact of interventions
Authors: Wassell James T. , Moeschberger M. L. 
Published: April 2007

AbstractBivariate survival analysis models that incorporate random effects or ‘frailty’ provide a useful framework for determining the effectiveness of interventions. These models are based on the notion that two paired survival times are correlated because they share a common unobserved value of a random variate from a frailty distribution. In some applications, however, investigators may have some information that characterizes pairs and thus provides information about their frailty. Alternatively, there may be an interest in assessing whether the correlation within certain types of pairs is different from the correlation within other types of pairs. In this paper, we present a method to incorporate ‘pair‐wise’ covariate information into the dependence parameter of the bivariate survival function.We provide an example using data from the Framingham Heart Study to investigate the times until the occurrence of two events within an individual: the first detection of hypertension and the first cardiovascular disease event. We model the dependence between these two events as a function of the age of the individual at the time of enrolment into the Framingham Study.
Measurement error in dietary assessment: An investigation using covariance structure models. Part II
Authors: Plummer Martyn , Clayton David 
Published: April 2007

AbstractIn Part I we presented a covariance structure model for analysing measurement error in the assessment of nitrogen intake. In this paper we include data on urine nitrogen excretion which allows a critical assessment of the model proposed. Inclusion of urine nitrogen data produces more pessimistic estimates of the quality of dietary intake measurements and shows that previous assumptions about independence of measurement error were wrong. This underscores the need for well founded assumptions in the analysis of measurement error.
Multivariate analysis of cholesterol distribution for monitoring the risk of coronary heart disease
Authors: Percy David F. , Hine Trevor J. 
Published: April 2007

AbstractThe screening of people for potential coronary heart disease and the monitoring of subjects considered at risk have been performed for some time by measuring total serum cholesterol and its constituent lipoproteins. However, these measurements vary substantially within subjects, making such assessments imprecise. It has been suggested that greater consistency can be achieved by analysing the joint distribution of the individual lipoproteins or of transformed variables derived from them. In this paper we present the results of a laboratory experiment to investigate these ideas with a view to improving current methods of monitoring patients at risk. Nested, random‐effects, multivariate analysis of variance and the log‐ratio analysis of compositions are used to include information on all three lipoproteins simultaneously, and ratios of generalized variances are used to assess and compare the different response variables. The multivariate approach is seen to be far superior to the usual methods. Recommendations are made for routine monitoring and the practical implications are discussed.
A comparison of different matching designs in case‐control studies: An empirical example using continuous exposures, continuous confounders and incidence of myocardial infarction
Authors: Friedlander Yechiel , Merom Dafna Lev , Kark Jeremy D. 
Published: April 2007

AbstractThe paper presents a case‐control study involving a disease, exposures and several continuous confounders. The relative efficiency and validity of a fully matched design is compared with random sampling of controls. We test a viable option of a partially matched design when inability to match all study subjects on all confounders occurs. The degree of bias in the odds ratios introduced by the different designs and by the different analytic models is assessed in comparison with the estimates obtained from a total cohort, from which both cases and controls were selected. Matched designs and analytic strategies are also evaluated in terms of the variances of the odds ratios. The results indicate that matching on continuous variables may lead to a more precise estimate of odds ratio than statistical control of confounding in unmatched designs. Partial selection of controls by matching may be a useful strategy when complete matching cannot be achieved; in practice, partial matching achieves most of the benefits of full matching.
Reducing attrition bias with an instrumental variable in a regression model: Results from a panel of rheumatoid arthritis patients
Authors: Leigh J. Paul , Ward Michael M. , Fries James F. 
Published: April 2007

AbstractThis study proposes an econometric technique to reduce attrition bias in panel data. In the simplest case, one estimates two regressions. The first is a probit regression based on sociodemographic and clinical characteristics measured at baseline. The probit regression estimates the probability that subjects stay or leave over the duration of the study. We insert the predicted probabilities from the probit regression into an inverse Mills ratio (IMR) or hazard rate to form an instrumental variable. We use this instrumental variable subsequently as an additional covariate in a second regression model that attempts to explain fluctuations in the dependent variable. The second regression, which is linear, includes only subjects who remained in the study. In alternative models, instrumental variables are created using predicted values from least squares and logit regressions estimating the probability that subjects stay or leave. The use of the instrumental variables reduces the effects of attrition bias in the linear regression model We applied the technique to a panel of patients with rheumatoid arthritis (RA) enrolled in 1981 and followed through 1990. We attempted to predict values for a measure of functional disability recorded in 1990 with use of covariates measured in 1981. The dependent variable was an index of disability in 1990 and the independent variables (covariates) included the disability index from 1981, the years of duration of RA, gender, martial status, education, and age in 1981. The correction technique suggested that ignoring attrition bias would underestimate the strength of associations between being female and the subsequent disability index, and overestimate the strength of associations between being married spouse present, age, and the initial disability index on the one hand and the subsequent disability index on the other.
Calculation of power and sample size with bounded outcome scores
Authors: Lesaffre Emmanuel , Scheys Ilse , Fröhlich Jürgen , Bluhmki Erich 
Published: April 2007

AbstractThe two‐sample Wilcoxon rank sum test is the most popular non‐parametric test for the comparison of two samples when the underlying distributions are not normal. Although the underlying distributions need not be known in detail to calculate the null distribution of the test statistic, parametric assumptions are often made to determine the power of the test or the sample size. We encountered difficulties with this approach in the planning of a recent clinical trial in stroke patients. It is shown that, for power and sample size estimation, it can be dangerous to apply the classical formulae routinely, especially with outcome scores having a U‐shaped or a J‐shaped distribution. As an example we have taken the Barthel index, a quality‐of‐life outcome measure in stroke patients. Further, we have investigated alternative methods by means of Monte Carlo simulation. The distributional characteristics of the estimated powers were compared. Our findings suggest more appropriate computer software is necessary for the calculation of power and sample size when efficacy is measured by a non‐parametric method.