bluuebunny/crossref_metadata_2025 huggingface dataset:
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

This 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

This 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

For 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

The 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

We 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

This 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

Harmonic 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

We 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

We 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

Bivariate 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

In 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

The 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

The 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

This 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

The 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.
Sample size determinations using logistic regression with pilot data
Authors: Flack Virginia F. , Eudey T. Lynn
Published: April 2007

Suppose the goal of a projected study is to estimate accurately the value of a ‘prediction’ proportionpthat is specific to a given set of covariates. Available pilot data show that (1) the covariates are influential in determining the value ofpand (2) their relationship topcan be modelled as a logistic regression. A sample size justification for the projected study can be based on the logistic model; the resulting sample sizes not only are more reasonable than the usual binomial sample size values from a scientific standpoint (since they are based on a model that is more realistic), but also give smaller prediction standard errors than the binomial approach with the same sample size. In appropriate situations, the logistic‐based sample sizes could make the difference between a feasible proposal and an unfeasible, binomial‐based proposal. An example using pilot study data of dental radiographs demonstrates the methods.
The continual reassessment method in cancer phase i clinical trials: A simulation study
Authors: Chevret Sylvie
Published: April 2007

In cancer studies, the aim of phase I clinical trials is to identify an appropriate dose for experimentation in phase II and III studies. The continual reassessment method (CRM) has been developed recently and presented as the method of choice in the design and analysis of such phase I studies. However, to implement the method, some methodological and practical considerations must be addressed. This paper examines, through a simulation study, the sensitivity of CRM both to the initial modelling of the dose‐toxicity relationship and the prior. It appears that the performance of CRM can be improved by using vague priors and initial tuning of the model to allow flexibility.
A note on the application of simple linear regression methods for trend detection at multiple sites and visits
Authors: Lui Kung‐Jong , Steffey Duane
Published: April 2007

In comparing running median, tolerance, cusum, and regression methods for trend detection over a small number of visits, Yang et al. found that application of multiple Z‐tests on the basis of a simple linear regression for each site separately was the most efficient for detection of trends at several sites simultaneously. Because the use of multiple Z‐tests completely ignores the covariance among measurements taken from different sites, to improve the power we propose a global χ2‐test. Assuming the covariance matrix known, we have found that the proposed χ2‐test procedure is more powerful than multiple Z‐tests for two‐sided alternatives when both the correlation among measurements and the number of sites are small. We also have found that the former procedure can have power uniformly larger than the latter when ratios of slopes to standard deviations of measurements at different sites vary and the number of sites is large. In fact, in the latter situation, the proposed global χ2‐test procedure, usually used only for two‐sided alternatives, can even have power larger than that of multiple Z‐tests for one‐sided alternatives. In the situation where the ratios of slopes to standard deviations of measurements are all equal, however, the proposed multivariate approach based on the χ2‐test distribution is the least efficient, especially when the number of sites and the correlation are moderate or large. Finally, to account for the effect of multiple tests over a series of visits on the overall α‐level, on the basis of Monte Carlo simulations, we compute critical values for sequential use of the proposed multivariate test procedure.
A bayesian approach to logistic regression models having measurement error following a mixture distribution
Authors: Schmid Christopher H. , Rosner Bernard
Published: April 2007

To estimate the parameters in a logistic regression model when the predictors are subject to random or systematic measurement error, we take a Bayesian approach and average the true logistic probability over the conditional posterior distribution of the true value of the predictor given its observed value. We allow this posterior distribution to consist of a mixture when the measurement error distribution changes form with observed exposure. We apply the method to study the risk of alcohol consumption on breast cancer using the Nurses Health Study data. We estimate measurement error from a small subsample where we compare true with reported consumption. Some of the self‐reported non‐drinkers truly do not drink. The resulting risk estimates differ sharply from those computed by standard logistic regression that ignores measurement error.
Use of the gibbs sampler to estimate transition rates between grades of coronary disease following cardiac transplantation
Authors: Sharples Linda D.
Published: April 2007

Coronary occlusive disease following cardiac transplantation is monitored using serial angiography, and graded on a 3 point scale according to the amount of narrowing observed in major vessels. Disease progression is modelled as a continuous time Markov process. The Gibbs sampler is used to estimate the marginal posterior distributions of the transition rates between grades of disease and from each grade to death.
An empirical bayes approach to the estimation of the incidence curve of HIV infection
Authors: Commenges D. , Etcheverry B.
Published: April 2007

A new approach is proposed for estimating the incidence curve of HIV infection and obtaining short term prediction of AIDS incidence. It is based on the method of back calculation which utilizes the fact that AIDS incidence is generated from HIV infection incidence by convolution with the incubation period distribution, but avoids the difficulties associated with approximating the infection incidence curve by a class of step functions. Instead, the infection incidence is modelled as a simple stochastic epidemic process which ensures smoothness of the estimate. We first derive the distribution of AIDS incidence knowing infection incidence. The best linear estimators of infection incidence as well as future AIDS incidence are given; a smoothed past AIDS incidence is also obtained. The parameters of the stochastic infection process can be estimated by maximum likelihood using a normal approximation to the marginal distribution of the AIDS incidence. This approach is applied to AIDS incidence data in the United States up to mid 1987. We find that the epidemic started in mid 1976, peaked at the end of 1983 and dropped afterwards.
The value of randomization and control in clinical trials
Authors: Urbach Peter
Published: April 2007

This paper examines the two principal justifications that have been offered for the standard conditions that clinical trials be randomized and controlled, with the conclusion that, strictly speaking, neither justification is valid. It is argued, on the other hand, that a Bayesian analysis of clinical trials affords a valid, intuitively plausible rationale for selective controls, and marks out a more limited role for randomization than it is generally accorded. The feasibility of retrospective trials is then considered in the light of these conclusions.
The role ofP‐values in analysing trial results
Authors: Freeman Peter R.
Published: April 2007

The current widespread practice of usingp‐values as the main means of assessing and reporting the results of clinical trials cannot be defended. Reasons for grave concern over the present situation range from the unsatisfactory nature ofp‐values themselves, their very common misunderstanding by statisticians as well as by clinicians and their serious distorting influence on our perception of the very nature of clinical trials. It is argued, however, that only by fully understanding the reasons why they have become so universally popular can we hope to change opinion and introduce more sensible ways of summarizing and reporting results. Some of the ways in which this might happen are discussed.
A regression model for the rate of success of in vitro fertilization
Authors: Baeten S. , Bouckaert A. , Loumaye E. , Thomas K.
Published: April 2007

The treatment of infertility by in vitro fertilization involves several aspects of human reproductive physiology. Since failures are still numerous and since improved techniques are becoming increasingly available, it is important to develop a model where the components of success are identified, allowing estimation of the occurrence of these components of success in the infertile population. The model of Speirs reduces these components to two: uterine receptivity and embryo viability. The probabilities of uterine receptivity and embryo viability were estimated here in a retrospective study of patients in a Belgian hospital sample. For comparison, Australian and U.S. data were used. It was shown that the estimate of uterine receptivity for the Belgian sample is located between the estimates for Australian and U.S. samples. In view of the psychological problem of repeated unsuccessful attempts at in vitro fertilization in some patients, it is important to be able to single out some prognostic factors that would allow more precise estimation of the probability for success in each case. A regression method was developed for that purpose. Only the effect of age, in the Belgian sample, was found to influence uterine receptivity significantly.
Further results on the non‐parametric linear regression model in survival analysis
Authors: Aalen Odd O.
Published: April 2007

This paper gives further developments of a non‐parametric linear regression model in survival analysis. Three subjects are studied. First, martingale residuals, originally developed for the Cox model, are introduced for our linear model. Their theory is developed and they are shown to be useful for judging goodness of fit. The second focus of the paper is on the use of bootstrap replications to judge which features of the cumulative regression plots are likely to reflect real phenomena and not merely random variation. In particular, this is applied to judging whether the effect of a covariate disappears over time, a problem for which no formal test exists. The third subject is density type, or kernel, estimation of the regression functions themselves. This might give more direct information than the cumulative plots. The approaches are illustrated by data from a clinical trial of carcinoma of the oropharynx, and by survival times of grafts in renal patients.
Dispersion tests and adjustments for survival and case‐control studies
Authors: Smith Philip J.
Published: April 2007

Cox and Snell have pointed out that when the empirical variance of a measured response y is quite different from its nominal variance under an assumed probability model, standard errors that are not adjusted for the discrepancy may yield misleading inferences. This article describes a test for assessing departures from nominal dispersion in survival analysis and case‐control studies. Also, a robust procedure for adjusting standard errors for these models is given.
Conditional independence models for epidemiological studies with covariate measurement error
Authors: Richardson Sylvia , Gilks Walter R.
Published: April 2007

We construct a unifying representation of the structure of measurement error problems with particular reference to situations commonly encountered in epidemiological studies, and outline how estimation of the parameters of interest can be carried out in a Bayesian framework using Gibbs sampling. We show how this approach can be implemented for designs involving continuous measurement errors assessed through a validation substudy, and discuss our results on simulated data.
A comparison of the generalized estimating equation approach with the maximum likelihood approach for repeated measurements
Authors: Park Taesung
Published: April 2007

Liang and Zeger proposed an extension of generalized linear models to the analysis of longitudinal data. Their approach is closely related to quasi‐likelihood methods and can handle both normal and non‐normal outcome variables such as Poisson or binary outcomes. Their approach, however, has been applied mainly to non‐normal outcome variables. This is probably due to the fact that there is a large class of multivariate linear models available for normal outcomes such as growth models and random‐effects models. Further‐more, there are many iterative algorithms that yield maximum likelihood estimators )MLEs( of the model parameters. The multivariate linear model approach, based on maximum likelihood )ML( estimation, specifies the joint multivariate normal distribution of outcome variables, while the approach of Liang and Zeger, based on the quasi‐likelihood, specifies only the marginal distributions. In this paper, I compare the approach of Liang and Zeger and the ML approach for the multivariate normal outcomes. I show that the generalized estimating equation )GEE( reduces to the score equation only when the data do not have missing observations and the correlation is unstructured. In more general cases, however, the GEE estimation yields consistent estimators that may differ from the MLEs. That is, the GEE does not always reduce to the score equation even when the outcome variables are multivariate normal. I compare the small sample properties of the GEE estimators and the MLEs by means of a Monte Carlo simulation study.
Approaches to disease cluster investigations in a state health department
Authors: Smith Daniel , Neutra Raymond
Published: April 2007

Classical tests for clustering rarely have a major role in the investigation of disease clusters at the neighbourhood level performed by a health department. When examining a single cluster, the boundaries are often ill‐defined, and the dimension )geographic, occupational, iatrogenic( is rarely obvious. Furthermore, using cluster tests to routinely scan disease registries looking for clusters is undesirable for several reasons. A better approach is to be vigilant for unusual environmental exposures, and to evaluate the impact they may have. In this context, cluster techniques may serve as part of a larger investigation that includes other epidemiologic approaches.
Estimating kappa from binocular data and comparing marginal probabilities
Authors: Schouten Hubert J. A.
Published: April 2007

Suppose that two graders classify all eyes in a sample of patients for the presence or absence of a specified abnormality. In the statistical analysis of the data, possible correlation between the observations in the right and left eyes should be taken into account. Recently, general methods have been developed to analyse grader agreement in rating paired body structures. In the present paper, an alternative procedure is proposed that utilizes existing formulae for the computation of )weighted( kappa and its standard error. In addition, the McNemar test is generalized to compare the probabilities of a positive judgement by the two graders.
Use of spending functions for occasional or continuous monitoring of data in clinical trials
Authors: Lan K. K. Gordon , Rosenberger William F. , Lachin John M.
Published: April 2007

In many clinical trials, data are monitored periodically by an external data monitoring committee )DMC(. Usually the frequency of these interim ‘looks’ at the data is prespecified. However, the progress of a clinical trial is unpredictable; often the schedule of looks must be modified. The Lan–DeMets procedure provides a spending function approach which does not require prespecification of the frequency or timing of interim looks. The procedure was developed based on the principle of a continuous Brownian motion process. In this paper we employ more elementary concepts to describe a procedure which is based upon the continuous monitoring of emerging data. The approach is flexible in that it applies to both continuous data monitoring and occasional interim monitoring. Examples are given from real clinical trials.
Comparison of two tests useful in situations where treatment is expected to increase variability relative to controls
Authors: Blair R. Clifford , Sawilowsky Shlomo
Published: April 2007

The type I error and power characteristics of the modified t test were compared with those of the generalized t test. Results suggested that, in contrast to the generalized t test, the modified t test can be seriously non‐robust to departures from population normality, with such departures often producing anti‐conservative results. Neither test held an absolute power advantage over the other when responses were from normal distributions, but the modified t test was generally more powerful for these conditions. In comparison with the pooled samples t test, both tests were usually much more efficient when treatment caused increases both in mean response and between‐subject variance, and suffered only small disadvantages when between‐subject variance was unchanged by treatment. Given these results and other considerations, recommendations for use of these recently devised tests are given.
A bivariate approach to meta‐analysis
Authors: Van Houwelingen Hans C. , Zwinderman Koos H. , Stijnen Theo
Published: April 2007

The usual meta‐analysis of a sequence of randomized clinical trials only considers the difference between two treatments and produces a point estimate and a confidence interval for a parameter that measures this difference. The usual parameter is the log)odds ratio( linked to Mantel–Haenszel methodology. Inference is made either under the assumption of homogeneity or in a random effects model that takes account of heterogeneity between trials. This paper has two goals. The first is to present a likelihood based method for the estimation of the parameters in the random effects model, which avoids the use of approximating Normal distributions. The second goal is to extend this method to a bivariate random effects model, in which the effects in both groups are supposed random. In this way inference can be made about the relationship between improvement and baseline effect. The method is demonstrated by a meta‐analysis dataset of Collins and Langman.
Relative risk, risk difference and rate difference models for sparse stratified data: A pseudo likelihood approach
Authors: Stijnen Theo , Van Houwelingen Hans C.
Published: April 2007

We consider a relative risk and a risk difference model for binomial data, and a rate difference model for Poisson )person year( data. It is assumed that the data are stratified in a large number of small strata. If each stratum has its own parameter in the model, then, due to the large number of parameters, straightforward maximum likelihood leads to inconsistent estimates of the relevant parameters. By contrast to the logistic model, conditioning on the number of events per stratum does not help in eliminating the stratum nuisance parameters. We propose a pseudo likelihood method to overcome these consistency problems. The resulting pseudo maximum likelihood estimates can easily be computed with standard statistical software. Our approach gives a more general framework for the Mantel–Haenszel type estimators proposed in the literature. In the special case of a series of 2 × 2 tables, for the risk and rate difference models, our approach yields exactly these ad hoc Mantel–Haenszel estimators, while for the relative risk model it gives a close approximation of the Mantel–Haenszel relative risk estimator. For the regression models corresponding to the association measures relative risk, risk difference and rate difference, our method provides analogues of conditional logistic regression, which were not previously available.
Plotting summary predictions in multistate survival models: Probabilities of relapse and death in remission for bone marrow transplantation patients
Authors: Klein John P. , Keiding Niels , Copelan Edward A.
Published: April 2007

Multistate survival analysis usually involves a series of detailed regression analyses describing transitions between various states. There is an often neglected need for the many estimates resulting from such an analysis to be re‐synthesized into summary statements, such as prediction of various outcomes from specified patient histories. Arjas and Eerola recently proposed a framework for dynamic probabilistic causality which has calculation of such prediction statements as a central tool. We illustrate these procedures on data from a multicentre bone marrow transplantation study, with death while in remission and relapse as terminal events and recovery of the patients's platelets to a normal level and the onset of acute graft‐versus‐host disease as intermediate events, using Cox regression models throughout. Among the features illustrated by the resulting plots is a strong effect on death while in remission if the platelets do not recover within the first three months.
Analysis of case‐crossover designs
Authors: Marshall Roger J. , Jackson Rodney T.
Published: April 2007

The case‐crossover design provides a means to study the effects of transient exposures on the risk of acute illness, for example, the effects of drinking alcohol on the immediate risk of a heart attack. Only cases are required by the design, since each case is effectively its own control; what a case was doing at the time of an acute event is compared with what the case would have been doing usually. Maclure has described an approach based on the Mantel–Haenszel method of analysis. It is shown here how the analysis of case‐crossover designs can be achieved by a method of maximum likelihood. The method is quite general and, in principle, can be used to analyse the joint effects of many transient exposures. For binary exposures the Mantel–Haenszel approach is an approximate solution to the likelihood equations.

In practice, case‐crossover designs are limited by the information available on each case's ‘usual’ behaviour. Extracting such information requires in‐depth questioning, but, in principle, it can be obtained. To do so requires careful questionnaire design. The approach is illustrated by analysis of 24 hour alcohol consumption and the risk of myocardial infarction. The problem with this analysis is how to estimate the probability of what a case would ‘usually’ have been doing from information on drinking frequency.
Dynamic balanced randomization for clinical trials
Authors: Signorini D. F. , Leung O. , Simes R. J. , Beller E. , Gebski V. J. , Callaghan T.
Published: April 2007

Common methods of treatment allocation for multi‐centre and/or stratified randomized clinical trials can result in substantial differences between the number of patients allocated to each treatment arm. This can occur in the overall trial for a permuted block design or within individual institutions/strata when using a minimization scheme. This may lead to a bias in the result. Also, these procedures can be predictable, with the possibility of an investigator‐introduced selection bias. An easily implemented method of randomization is proposed which attempts to overcome these problems by balancing treatment allocations both within strata and across the trial as a whole. The method keeps a running tally on total treatment allocation numbers at all stratification levels. When a patient accrues a hierarchical decision rule is applied, and the allocation is deterministic if certain pre‐defined limits are exceeded, and random otherwise. The method is an extension of the big stick design of Soares and Wu, and is related to both Zelen's key number randomization methods and the schemes of Nordle and Brantmark. Simulation studies are used to demonstrate that major imbalances possible with other schemes do not occur using this method, and that the potential for selection bias is much reduced.
Comparison of the cox model and the regression tree procedure in analysing a randomized clinical trial
Authors: Schmoor Claudia , Ulm Kurt , Schumacher Martin
Published: April 2007

In a clinical trial comparing different treatments the patients may be rather heterogeneous with regard to their natural prognosis. Simple overall comparison of the treatment groups may lead to a biased estimate of the treatment effect even in a well‐balanced randomized study, at least when survival time is the outcome. An adequate analysis of the treatment effect is only feasible in a multivariate framework where the important prognostic factors are accounted for and, additionally, treatment‐covariate interactions may be evaluated. Analyses using the Cox model are compared with alternative approaches based on the Classification and Regression Tree )CART( technique. Basic differences between these approaches are outlined and discussed in the context of a randomized clinical trial of chemotherapy in patients with brain tumours.
The relative importance of prognostic factors in studies of survival
Authors: Schemper M.
Published: April 2007

The relative importance of prognostic factors in regression can be measured either by standardized regression coefficients or by percentages of explained variation in a dependent variable. One advantage of using explained variation is the direct comparability of qualitative prognostic factors with others, or of groups of prognostic factors. The description of relative importance can be accomplished within marginal or partial effects analyses. It is demonstrated that it is possible not only to provide a descriptive ranking of prognostic factors according to their statistically determined importance, but also to make inferences concerning their relative importance, employing bootstrap techniques and procedures for multiple comparisons. The methods presented, which are new in the context of Cox regression, are exemplified by analyses of studies of lung cancer and breast cancer.
Relationships between incidence and mortality in non‐reversible diseases
Authors: Capocaccia Riccardo
Published: April 2007

The estimation of the incidence of chronic diseases from mortality and survival rates is shown to be attainable, under some simplifying hypotheses, through the solution of a convolution equation. Exact solutions are derived for the cases in which survival times of incident cases are exponential or gamma distributed. Applications to breast cancer, HIV infection and stomach cancer incidence in the Italian population are presented.
Job stress related to glyco‐lipid allostatic load, adiponectin and visfatin
Authors: Li Wei , Zhang Jun‐Quan , Sun Jing , Ke Ji‐Hong , Dong Zhi‐Yuan , Wang Sheng
Published: April 2007

Job stress has been associated with an increased risk for glyco‐lipid metabolic dysfunction, which can lead to coronary artery disease, diabetes and other adverse health outcomes. We undertook a cross‐sectional study to evaluate the relationship between job stress and glyco‐lipid metabolic change in healthy industrial employees in China. Volunteers (n = 504) were investigated during the routine annual health examination period between June and August 2006. Job stress was assessed by the 22‐item Job Content Questionnaire (JCQ), which included three dimensions: job control, job demand and social support. Glyco‐lipid metabolic status was determined by the glyco‐lipid allostatic load based on the allostasis paradigm and the concept of allostatic load. There were 11 parameters comprising the glyco‐lipid allostatic load: body mass index (BMI); waist to hip ratio (WHR); high‐density lipoprotein (HDL); low‐density lipoprotein (LDL); total cholesterol (TC); triglyceride (TG); glycosylated haemoglobin (HbA1C); homeostasis model assessment‐insulin resistance (HOMA‐IR); HOMA‐β‐cell function; plasma concentrations of adiponectin; and visfatin. Score points were given for values in the high‐risk quartile (0–11). Multivariate analysis of variance (MANOVA) revealed that BMI, WHR, TG, adiponectin, visfatin and glyco‐lipid allostatic load were significantly different between high and low job stress groups after being adjusted for demographic characteristics and health‐related behaviour. Plasma adiponectin levels were negatively correlated with BMI, WHR, HbA1C, TG, IR and visfatin, and positively correlated with HDL. Plasma concentration of visfatin was positively correlated with BMI, WHR, TC and IR. Multiple stepwise linear regression analysis indicated that job control was significantly related to glyco‐lipid allostatic load, plasma adiponectin concentration and plasma visfatin concentration after controlling for demographic characteristics and health‐related behaviour. In conclusion, we found association between job stress, job control and the glyco‐lipid metabolism summary score. Plasma adiponectin and visfatin concentrations related with job stress and job control. Copyright © 2007 John Wiley & Sons, Ltd.
International arbitration
Authors: Faure Eric
Published: April 2007

The English arbitration procedures in GAFTA and FOSFA have been developed over 125 years, during which time contracts and procedures have been continuously revised. They have stood the test of time and, in the author's view, work well. It would be unfortunate to have to change the procedures radically, and indeed this action might seriously impair their efficiency. While it would be ideal to achieve international uniformity, there would appear to be almost insuperable obstacles in the way at the present time.
Processing peanuts and cottonseed
Authors: Garcia M. E. J.
Published: April 2007

Two cases are presented to show how the elimination of bad operational habits and the use of controlling equipment can improve the efficiency of an oil mill. The cleaning of peanuts (farmer's stock) is presented as an example of the need to use proper equipment for a given operation. A supply of good quality seed, together with the use of modern equipment, has improved the yields of oil and protein to the present values of 91% and 96% recovery, respectively. A growing fats and oils market in the Central American area and a limited supply of cottonseed are forcing the mills of Nicaragua to look for new oilseeds.