bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sample size determinations using logistic regression with pilot data
Authors: Flack Virginia F. , Eudey T. Lynn 
Published: April 2007

AbstractSuppose 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

AbstractIn 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

AbstractIn 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

AbstractTo 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

AbstractCoronary 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

AbstractA 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

AbstractThis 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

AbstractThe 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

AbstractThe 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

AbstractThis 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

AbstractCox 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

AbstractWe 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

AbstractLiang 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

AbstractClassical 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

AbstractSuppose 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

AbstractIn 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

AbstractThe 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

AbstractThe 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

AbstractWe 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

AbstractMultistate 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

AbstractThe 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

AbstractCommon 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.