bluuebunny/crossref_metadata_2025_split huggingface dataset:
On closed test procedures for dose‐response analysis
Authors: Rom Dror M. , Costello Robert J. , Connell Laura T. 
Published: April 2007

AbstractThis paper concerns the testing of a dose‐response effect in medical studies. We consider two situations. The first is when the drug effect on a parameter of interest is likely to increase (or decrease) with increasing doses. We propose a procedure based on the closure principle of Peritz combined with an application of a closed testing procedure of Marcus et al. to a test proposed by Tukey et al. The second situation is the analysis of a drug effect where one might observe a reversal at higher doses. For this situation, we propose simultaneous examination of contrasts among the doses at each of several stages of the testing scheme. We calculate critical values for this procedure incorporating the correlation structure among the contrasts. Both procedures strongly control the family‐wise error rate at the pre‐determined α level. They also provide information about the shape of the dose response lacking in other commonly used procedures for testing against an ordered alternative hypothesis. We illustrate the procedures on two datasets.
The application of REML in clinical trials
Authors: Brown H. K. , Kempton R. A. 
Published: April 2007

AbstractResidual maximum likelihood (REML) is a technique for estimating variance components in multi‐classified data. In contrast to analysis of variance it can be routinely applied to unbalanced data and avoids some of the problems of biased variance estimates found with standard maximum likelihood estimation. The full REML method is of particular value for the analysis of unbalanced clinical trials as it allows recovery of all the available information on treatment effects which can lead to significant improvements in their precision. The use of REML has until recently been limited by heavy computational requirements and lack of readily available software. This is no longer such a restriction, however, as REML procedures are now available in several widely‐used statistical packages, including BMDP, Genstat and SAS. This paper describes the REML technique and discusses its application to three common types of clinical trial: crossover, repeated measures and multicentre.
Statistical analysis of proliferative index data in clinical trials
Authors: Grambsch Patricia , Louis Thomas A. , Bostick Roberd M. , Grandits Greg A. , Fosdick Lisa , Darif Mohamed , Potter John D. 
Published: April 2007

AbstractMeasurements of proliferative activity in colonic epithelial cells are being used as surrogate endpoints in clinical trials for colon cancer prevention. Proliferative index data exemplify an important type of clinical trial endpoint. The outcome variable is a proportion in which the denominator is an ancillary statistic and in which measurement error and technician judgement are important sources of variability. The paper proposes a statistical model for a repeated measures clinical trial with this type of endpoint, in the context of proliferative activity data. The model is a two‐stage random effects linear model in the log scale. In addition to fixed effects covariates, it explicitly incorporates two major sources of variability: the number of epithelial cells counted and the reader effect. Although the resulting likelihood is complicated, one can fit an approximate likelihood with minimal loss of efficiency using standard packages. We apply the model to a pilot randomized clinical trial.
Median analysis of blood pressure for a sample including treated hypertensives
Authors: White Ian R. , Chaturvedi Nishi , McKeigue Paul M. 
Published: April 2007

AbstractWe consider the analysis of epidemiological studies in which the outcome of interest is a physiological disturbance (such as blood pressure) which may have been corrected by medical intervention (such as antihypertensive treatment). It is important to take account of such intervention in studying the determinants of hypertension. We recommend using the median as a measure of location and assuming that the underlying values of subjects under intervention lie above the median. Some well‐established exact methods for simple problems are presented. An example is given in which the blood pressure differences between Afro‐Caribbeans and Europeans are underestimated by about one‐quarter if adjustment is not made for treated hypertensives.
Multilevel time series models with applications to repeated measures data
Authors: Goldstein Harvey , Healy Michael J. R. , Rasbash Jon 
Published: April 2007

AbstractThe analysis of repeated measures data can be conducted efficiently using a two‐level random coefficients model. A standard assumption is that the within‐individual (level 1) residuals are uncorrelated. In some cases, especially where measurements are made close together in time, this may not be reasonable and this additional correlation structure should also be modelled. A time series model for such data is proposed which consists of a standard multilevel model for repeated measures data augmented by an autocorrelation model for the level 1 residuals. First‐ and second‐order autoregressive models are considered in detail, together with a seasonal component. Both discrete and continuous time are considered and it is shown how the autocorrelation parameters can themselves be structured in terms of further explanatory variables. The models are fitted to a data set consisting of repeated height measurements on children.
A simulation study comparing two approximations for a quasi t‐quantile, used in repeated measures anova
Authors: Silverberg Arthur R. 
Published: April 2007

AbstractIn the analysis of variance it is not unusual to form a denominator of an approximate t‐statistic from a linear combination of mean squares. Two examples include the Behrens‐Fisher problem and the repeated measures analysis of variance. One solution to the problem of finding the appropriate degrees of freedom is to use Satterthwaite's approximation while another solution, due to Cochran, is to form a weighted t‐statistic. Based upon computer simulations I have found that the magnitude of the bias of the Satterthwaite approximation was less than that of the Cochran approximation in 68/75 cases considered. When the bias of the Cochran approximation was smaller than the bias of the Satterthwaite approximation, I found that the estimated bias of the Satterthwaite approximation no more than 0.5 per cent in the cases considered. I recommend performing the additional calculations required for the Satterthwaite approximations when combining two mean squares, especially when one mean square is based upon 12 or fewer degrees of freedom.
Alternative models for ordinal logistic regression
Authors: Greenland Sander 
Published: April 2007

AbstractArmstrong and Sloan have reviewed two types of ordinal logistic models for epidemiologic data: the cumulative‐odds model and the continuation‐ratio model. I review here certain aspects of these models not emphasized previously, and describe a third type, the stereotype model, which in certain situations offers greater flexibility coupled with interpretational advantages. I illustrate the models in an analysis of pneumoconiosis among coal miners.
A simple frailty model for family studies with covariates
Authors: Babiker Abdel , Cuzick Jack 
Published: April 2007

AbstractA simple, easily analysed frailty model based on logistic regression and employing only binary frailties is developed and analysed. Methods for testing for frailty and estimates of frailty parameters and covariate effects are presented. The effects of ignoring or mis‐specifying frailty are examined in detail and shown to downwardly bias the estimates of covariate effects in a variety of situations, even when frailty is overestimated. When the true frailty is large it can be difficult to estimate accurately because of the flatness of the likelihood. However, it is shown that this does not have a large influence on the estimates of the effects of the observed covariates. Methods for dealing with multiple frailties, for example, sibships and spouse pairs, are also developed. The results are illustrated on a large data set examining the risk factors associated with palmar keratoses.
Helping doctors to draw appropriate inferences from the analysis of medical studies
Authors: Burton Paul R. 
Published: April 2007

AbstractMost clinicians and many medical statisticians interpret standard frequentist confidence intervals by invoking the Bayesian concept of subjective probability. Fortunately, the assumptions that render this interpretation acceptable are often quite reasonable in the setting of the practical day‐to‐day analysis of medical data. This article takes the subjective interpretation of confidence intervals to its logical conclusion and argues that the inferential understanding of clinicians and public health physicians could potentially be improved if, where it was appropriate, standard inferential statements ‐ point estimates, 95 per cent confidence intervals and P‐values ‐ were supplemented by estimates of the subjective posterior probability, assuming a uniform prior density, that the true value of a parameter to be estimated exceeds one or a series of thresholds that are clinically critical or easily interpretable. Many decision makers in the health care arena draw totally inappropriate inferences from analyses where the point estimate indicates a clinically valuable effect but the null hypothesis cannot formally be rejected, and, although the proposed approach could be of potential value in a range of settings, it is argued that it could be of particular use in the rational interpretation of underpowered studies that must inform critical clinical or public health decisions.
Testing for baseline balance in clinical trials
Authors: Senn Stephen 
Published: April 2007

AbstractOnce the data from a clinical trial are available for analysis it is common practice to carry out ‘tests of baseline homogeneity’ on prognostic covariates before proceeding to analyse the effects of treatment on outcome variables. It is argued that this practice is philosophically unsound, of no practical value and potentially misleading. Instead it is recommended that prognostic variables be identified in the trial‐plan and fitted in an analysis of covariance regardless of their baseline distribution (statistical significance).
An optimal three‐stage design for phase II clinical trials
Authors: Ensign Lisa Garnsey , Gehan Edmund A. , Kamen Douglas S. , Thall Peter F. 
Published: April 2007

AbstractA phase II clinical trial in cancer therapeutics is usually a single‐arm study to determine whether an experimental treatment (E) holds sufficient promise to warrant further testing. When the criterion of treatment efficacy is a binary endpoint (response/no response) with probability of response p, we propose a three‐stage optimal design for testing H0: p ≤ p0 versus H1: p ≥ p1, where p1 and p0 are response rates such that E does or does not merit further testing at given levels of statistical significance (α) and power (1 − β). The proposed design is essentially a combination of earlier proposals by Gehan and Simon. The design stops with rejection of H1 at stage 1 when there is an initial moderately long run of consecutive treatment failures; otherwise there is continuation to stage 2 and (possibly) stage 3 which have decision rules analogous to those in stages 1 and 2 of Simon's design. Thus, rejection of H1 is possible at any stage, but acceptance only at the final stage. The design is optimal in the sense that expected sample size is minimized when p = p0, subject to the practical constraint that the minimum stage 1 sample size is at least 5. The proposed design has greatest utility when the true response rate of E is small, it is desirable to stop early if there is a moderately long run of early treatment failures, and it is practical to implement a three‐stage design. Compared to Simon's optimal two‐stage design, the optimal three‐stage design has the following features: stage 1 is the same size or smaller and has the possibility of stopping earlier when 0 successes are observed; the expected sample size under the null hypothesis is smaller; stages 1 and 2 generally have more patients than stage 1 of the two‐stage design, but a higher probability of early termination under H0; and the total sample size and criteria for rejection of H1 at stage 3 are similar to the corresponding values at the end of stage 2 in the two‐stage optimal design.
Effect of verification bias on positive and negative predictive values
Authors: Zhou Xiao‐Hua 
Published: April 2007

AbstractThe pairing of sensitivity and specificity expresses the efficacy of a test, and positive and negative predictive values measure the accuracy of a diagnostic test when applied to a particular patient. To calculate these measures, one has to know the true disease status of each patient. In practice, however, some patients may not be selected for verification of disease status. It has been shown that the estimated sensitivity and specificity may be biased if one includes in the study sample only the patients with verified disease statuses. This paper concerns the properties of the estimators of positive and negative predictive values using only patients with verified disease statuses. First, I show that these estimators are unbiased and provide consistent estimators for the variances of these estimators under the assumption that the probability of selecting a patient for a disease verification procedure does not depend directly on the true disease status of the patient. Then, I use the ML method to study the sensitivity of the naive estimators to the departure from the conditional independence assumption.
Analysing survival in the presence of an auxiliary variable
Authors: Finkelstein Dianne M. , Schoenfeld David A. 
Published: April 2007

AbstractA primary endpoint of AIDS trials is the length of survival. Often there is auxiliary information available on measures of disease progression, which significantly alter the risk of mortality. This paper explores the use of this information in obtaining a refined estimate of survival, and a test based on this estimate. The methods are applied to an AIDS clinical trial, and results of simulations are provided which compare the approach to standard methods for survival analysis.
Projection of incidence rates to a larger population using ecologic variables
Authors: Frey Carolin M. , Feuer Eric J. , Timmel Marilyn J. 
Published: April 2007

AbstractThere is wide acceptance of direct standardization of vital rates to adjust for differing age distributions according to the representation within age categories of some referent population. One can use a similar process to standardize, and subsequently project vital rates with respect to continuous, or ratio scale ecologic variables. We obtained from the National Cancer Institute's Surveillance, Epidemiology and End Results (SEER) programme, a 10 per cent subset of the total U.S. population, country‐level breast cancer incidence during 1987‐1989 for white women aged 50 and over. We applied regression coefficients that relate ecologic factors to SEER incidence to the full national complement of county‐level information to produce an age and ecologic factor adjusted rate that may be more representative of the U.S. than the simple age‐adjusted SEER incidence. We conducted a validation study using breast cancer mortality data available for the entire U.S. and which supports the appropriateness of this method for projecting rates.
Interval censoring in longitudinal data of respiratory symptoms in aluminium potroom workers: A comparison of methods
Authors: Samuelsen Sven Ove , Kongerud Johny 
Published: April 2007

AbstractIn a longitudinal study of workers in seven Norwegian aluminium plants, the time to development of asthmatic symptoms could only be determined to lie in the interval between two consecutive health examinations. In a previous paper we analysed the data by survival techniques for interval censored data. In the present paper the data are reanalysed in two ways and compared to the previous analyses. First, it is assumed that occurrence of symptoms coincided with reporting, in which case the data can be analysed as right censored. Secondly, the follow‐up times are completely disregarded and the effects of covariates are analysed on the binary outcomes of symptoms. Comparing the estimated effects of the covariates between the three approaches, only minor differences were found. However, the estimates on incidence were strongly influenced by whether the data were analysed as right or interval censored.
Standardized rates of recurrent outcomes
Authors: Stukel Thérèse A. , Glynn Robert J. , Fisher Elliott S. , Sharp Sandra M. , Lu‐Yao Grace , Wennberg John E. 
Published: April 2007

AbstractLongitudinal studies are often concerned with estimating the rate of an event that may recur. Examples are nonmelanoma skin cancer rates, screening rates for breast cancer using mammography and hospital admission rates. We propose simple estimators for directly and indirectly standardized summary rates and relative rates of recurrent events and their variances. We also develop an estimator of the excess rate in an area if the rate in another area applied. For non‐recurrent events, the estimators are identical to the usual standardized summary rates.5 The estimators are independent of the underlying distribution of the event of interest and allow for unequal follow‐up times and event rate heterogeneity among individuals. The method is not computationally intensive and does not require specialized software. We illustrate the application of the method in a retrospective cohort study of hospital utilization patterns of Medicare enrollees in Boston and New Haven over a three and a half year period.
A comparison of two phase I trial designs
Authors: Korn Edward L. , Midthune Douglas , Chen T. Timothy , Rubinstein Lawrence V. , Christian Michaele C. , Simon Richard M. 
Published: April 2007

AbstractPhase I cancer chemotherapy trials are designed to determine rapidly the maximum tolerated dose of a new agent for further study. A recently proposed Bayesian method, the continual reassessment method, has been suggested to offer an improvement over the standard design of such trials. We find the previous comparisons did not completely address the relative performance of the designs as they would be used in practice. Our results indicate that with the continual reassessment method, more patients will be treated at very high doses and the trials will take longer to complete. We offer some suggested improvements to both the standard design and the Bayesian method.
Reduction in burden of illness: A new efficacy measure for prevention trials
Authors: Chang M. N. , Guess H. A. , Heyse J. F. 
Published: April 2007

AbstractA new efficacy measure is developed for use in prevention trials of interventions which may affect both disease incidence and disease severity. We assign a severity score to each incident case and sum severity scores over all incident cases within each treatment group to create a burden‐of‐illness score for each treatment group. Efficacy is evaluated by the difference between the burden‐of‐illness per randomized subject in the two randomized treatment groups. Since the numbers of summands in each burden‐of‐illness score is a random variable, standard methods of analysis are not directly applicable. The asymptotic distribution and sampling properties of the net reduction in the burden‐of‐illness score are derived for trials designed to stop either after a fixed length of follow‐up or after the occurrence of a fixed number of cases. We illustrate the method with data from a clinical trial of a human rotavirus vaccine.
A unified method for monitoring and analysing controlled trials
Authors: Grossman Jason , Parmar Mahesh K. B. , Spiegelhalter David J. , Freedman Laurence S. 
Published: April 2007

AbstractGroup sequential methods are becoming increasingly popular for monitoring and analysing large controlled trials, especially clinical trials. They not only allow trialists to monitor the data as it accumulates, but also reduce the expected sample size. Such methods are traditionally based on preserving the overall type I error by increasing the conservatism of the hypothesis tests performed at any single analysis. Using methods which are based on hypothesis testing in this way makes point estimation and the calculation of confidence intervals difficult and controversial. We describe a class of group sequential procedures based on a single parameter which reflects initial scepticism towards unexpectedly large effects. These procedures have good expected and maximum sample sizes, and lead to natural point and interval estimates of the treatment difference. Hypothesis tests, point estimates and interval estimates calculated using this procedure are consistent with each other, and tests and estimates made at the end of the trial are consistent with interim tests and estimates. This class of sequential tests can be considered in both a traditional group sequential manner or as a Bayesian solution to the problem.
Deviance estimates of sample size for equivalence tests in vaccine trials
Authors: Garner J. Barry 
Published: April 2007

AbstractThis paper proposes a sample size procedure for both equivalence and conventional tests for the comparison of two binomial proportions, based on the signed square root of the deviance. When the comparison is based on the odds ratio, I describe an alternate ‘close’ conditional exact method that gives results that support those given by the deviance method. I summarize the advantages of the deviance‐based method and also show that in general equivalence situations the sample size estimate depends upon the measure of comparison selected, odds ratio, risk ratio or risk difference.
Bayesian hierarchical analysis of within‐units variances in repeated measures experiments
Authors: Have Thomas R. Ten , Chinchilli Vernon M. 
Published: April 2007

AbstractWe develop hierarchical Bayesian models for biomedical data that consist of multiple measurements on each individual under each of several conditions. The focus is on investigating differences in within‐subject variation between conditions. We present both population‐level and individual‐level comparisons. We extend the partial likelihood models of Chinchilli et al. with a unique Bayesian hierarchical framework for variance components and associated degrees of freedom. We use the Gibbs sampler to estimate posterior marginal distributions for the parameters of the Bayesian hierarchical models. The application involves a comparison of two cholesterol analysers each applied repeatedly to a sample of subjects. Both the partial likelihood and Bayesian approaches yield similar results, although confidence limits tend to be wider under the Bayesian models.
Stratification of summary statistic tests according to missing data patterns
Authors: Dawson Jeffrey D. 
Published: April 2007

AbstractSummary statistics, such as slope or area under the time‐response curve, reduce the dimensionality of repeated measures data and can thereby simplify the comparison of groups in longitudinal studies. Since summary statistic distributions vary according to the amount, timing, and type of any missingness that occurs, one must choose between analysing the data unconditionally or conditionally on the missingness patterns. This paper uses simulations to compare such unstratified and stratified summary statistic analyses with respect to their size and power under models that allow for both non‐informative and informative missingness mechanisms. Of particular interest is the robustness of these methods to violations of the assumptions that one must make if they are to have proper test size. It is found that stratification of the analysis tends to result in an increase of power, and improves the robustness to violations of missing data assumptions.