bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of longitudinal data: Random coefficient regression modelling
Authors: Rutter Carolyn M. , Elashoff Robert M. 
Published: April 2007

AbstractWe review random coefficient regression (RCR) models and methods for fitting these models from an applications perspective. Methods for data with exponential family distributions are presented with the Gaussian distribution as a special case. Attention is given to interpretation of fixed effects and the correlation structures implied by RCR models. Estimation methods are presented with computational approaches. Problems associated with testing fixed effects include accurate variance estimation and robustness to misspecification of the covariance structure. Methods for model selection and assessment are presented. An example is used to demonstrate recommended approaches.
Sample size for repeated measures studies with binary responses
Authors: Lipsitz Stuart R. , Fitzmaurice Garrett M. 
Published: April 2007

AbstractWe consider the sample size required for repeated measures studies when the response variable is binary. We propose the use of weighted least squares (WLS) for calculating the minimum sample size required to detect some minimum clinically important treatment effect. We provide tabulated values of the estimated sample sizes for a simple example and we discuss some practical considerations in determination of sample size with repeated binary responses.
Estimation and sample size considerations for clustered binary responses
Authors: Lee Eric W. , Dubin Neil 
Published: April 2007

AbstractAlthough there is much literature on sample size determination for clinical trials or experiments with independent responses, there is a lack of methodology to obtain sample sizes for dependent outcomes. This paper presents a simple way to calculate sample size for estimating treatment effects and diagnostic accuracy in the case of correlated binary outcomes. The proposed weighted procedure also has use in estimation, whose advantages we demonstrate through simulation. Recommendations are made for practical application.
Measurement error, instrumental variables and corrections for attenuation with applications to meta‐analyses
Authors: Carroll R. J. , Stefanski L. A. 
Published: April 2007

AbstractMacMahon et al. present a meta‐analysis of the effect of blood pressure on coronary heart disease, as well as new methods for estimation in measurement error models for the case when a replicate or second measurement is made of the fallible predictor. The correction for attenuation used by these authors is compared to others already existing in the literature, as well as to a new instrumental variable method. The assumptions justifying the various methods are examined and their efficiencies are studied via simulation. Compared to the methods we discuss, the method of MacMahon et al. may have bias in some circumstances because it does not take into account: (i) possible correlations among the predictors within a study; (ii) possible bias in the second measurement; or (iii) possibly differing marginal distributions of the predictors or measurement errors across studies. A unifying asymptotic theory using estimating equations is also presented.
The chart trials: Bayesian design and monitoring in practice
Authors: Parmar Mahesh K. B. , Spiegelhalter David J. , Freedman Laurence S. ,   Chart Steering Committee
Published: April 2007

AbstractIn this paper we describe the design and monitoring of two large randomized trials being conducted through the British Medical Research Council (MRC) Cancer Trials Office. In particular we discuss the issues involved in the design of the trials, specifying the null and alternative hypotheses and how the design has influenced the monitoring procedure used. From these two examples, and some other MRC experiences, we suggest some basic considerations which could be applied to many MRC and other trials.
Stopping a clinical trial early: Frequentist and bayesian approaches applied to a CALGB trial in non‐small‐cell lung cancer
Authors: George Stephen L. , Li Chengchang , Berry Donald A. , Green Mark R. 
Published: April 2007

AbstractIn May 1984, the Cancer and Leukemia Group B (CALGB) opened a phase III clinical trial for patients with stage III non‐small‐cell lung cancer (NSCLC). The experimental design entailed randomization of 240 patients equally to one of two treatments: radiotherapy alone or chemotherapy followed by radiotherapy. The original design was a fixed sample size design with the intent to analyse the results after 190 deaths. Shortly after the trial began, it was decided to apply group sequential concepts by using a truncated O'Brien‐Fleming stopping rule, implemented via a Lan‐DeMets α‐spending function. A study monitoring committee was established to review the analyses as they were produced. The study was stopped at the fifth interim analysis in May 1987 after 155 eligible patients had been entered. This paper reviews the statistical and other considerations leading to this decision and presents later follow‐up information on these patients. Some Bayesian alternatives to the standard frequentist approaches are also explored and it is demonstrated how these alternatives provide a natural way to address many of the issues raised in monitoring clinical trials.
A reversal of fortune: Practical problems in the monitoring and interpretation of an EORTC breast cancer trial
Authors: Sylvester Richard , Bartelink Harry , Rubens Robert ,   Eortc Breast Cancer Cooperative Group
Published: April 2007

AbstractAn example of some of the practical problems associated with the interim monitoring of accumulating time to event data is illustrated through an EORTC clinical trial in locally advanced breast cancer which studied the effects of both hormonal therapy and chemotherapy using a 2 × 2 factorial design. The data monitoring process is described and summaries of the interim statistical analyses and the analysis for publication of the clinical results are provided. The results 2.5 years after closing the trial to patient entry differed quite markedly from those observed during the period of patient accrual, thus showing the potential dangers of drawing conclusions based on only short term follow‐up data.
Timing the collaborative analysis of three trials comparing 5‐FU plus folinic acid (FUFA) to surgery alone in the management of resected colorectal cancer: A national cancer institute of canada clinical trials group (NCIC‐CTG) perspective
Authors: Pater Joseph L. 
Published: April 2007

AbstractThree cancer trials groups have decided to combine data from their separate trials examining the role of chemotherapy with 5‐FU and folinic acid in the adjuvant treatment of resected colorectal carcinoma. Decisions regarding the timing of the analyses of these data have had to consider the same issues that surround early termination of ongoing trials. This paper reviews this experience from the perspective of one of the groups. It emphasizes the significant impact on decision‐making of international differences of opinion concerning treatment standards.
Interim analysis: The alpha spending function approach
Authors: Demets David L. , Lan K. K. Gordon 
Published: April 2007

AbstractInterim analysis of accumulating data in a clinical trial is now an established practice for ethical and scientific reasons. Repeatedly testing interim data can inflate false positive error rates if not handled appropriately. Group sequential methods are a commonly used frequentist approach to control this error rate. Motivated by experience of clinical trials, the alpha spending function is one way to implement group sequential boundaries that control the type I error rate while allowing flexibility in how many interim analyses are to be conducted and at what times. In this paper, we review the alpha spending function approach, and detail its applicability to a variety of commonly used statistical procedures, including survival and longitudinal methods.
Sequential methods based on the boundaries approach for the clinical comparison of survival times
Authors: Whitehead John 
Published: April 2007

AbstractThe earliest formal sequential procedure, the sequential probability ratio test, involved the plotting of certain test statistics and comparison with straight line parallel boundaries. The boundaries approach can now be used with a wide variety of test statistics, including those appropriate to the analysis of survival data. The boundaries can take various forms, although the use of straight lines still eases the underlying mathematical theory while at least approximating to the requirements of the majority of clinical trials. The implementation of sequential methods needs to be made flexibly and sensitively, with each clinical trial meriting an individualized approach.
The what, why and how of bayesian clinical trials monitoring
Authors: Freedman Laurence S. , Spiegelhalter David J. , Parmar Mahesh K. B. 
Published: April 2007

AbstractWe discuss the advantages and limitations of group sequential methods for monitoring clinical trials data. We describe a Bayesian approach, based upon the use of sceptical prior distributions, that avoids some of the limitations of group sequential methods. We illustrate its use with data from a trial of levamisole plus 5‐Fluorouracil for colorectal cancer.
Data monitoring committees and early stopping guidelines: The southwest oncology group experience
Authors: Crowley J. , Green S. , Liu P. Y. , Wolf M. 
Published: April 2007

AbstractData Monitoring Committees in the Southwest Oncology Group comprise the study leadership and the leadership of the Group, augmented by some outside representation. The early stopping guidelines used by these committees are explicit in each protocol, and can be characterized as specifying a few interim analyses at conservative statistical levels. We describe our ten years of experience with this system, and give examples of different trials which have stopped either for positive or negative results.
Some practical aspects of the interim monitoring of clinical trials
Authors: Simon Richard 
Published: April 2007

AbstractThe decision to stop accrual early to a clinical trial is often difficult and multifaceted. Interim monitoring boundaries have been found useful in U.S. oncology trials for such decisions for reasons described here. This paper also discusses rationale that lead to more conservative approaches to early stopping decisions than are currently employed. A recent initiative of the National Cancer Institute to achieve the objectives of independent data monitoring committees in the phase III clinical trials which it sponsors is also described.
Surrogate markers in aids and cancer trials
Authors: Fleming Thomas R. 
Published: April 2007

AbstractThere is significant need for rapid yet reliable evaluation of promising new interventions for the treatment of patients with cancer or HIV infection. Considerable attention has been given to identifying replacement or ‘surrogate’ endpoints for the true clinical efficacy endpoints, in order to reduce the cost, size and duration of clinical trials. We discuss issues which affect the validity of surrogate markers. The reliability of the CD4 lymphocyte count marker is carefully considered in clinical trials of anti‐retroviral agents in HIV infected individuals. The nature of surrogate markers and their reliability is discussed in cancer prevention, screening and treatment trials. Some suggested uses of marker information are also considered.
Monitoring multi‐armed trials
Authors: Proschan Michael A. , Follmann Dean A. , Geller Nancy L. 
Published: April 2007

AbstractWe propose and discuss several methods of monitoring multi‐armed trials comparing means or survival. These methods combine multiple comparison procedures such as Fisher's LSD, Newman‐Keuls and Tukey's with monitoring boundaries such as those of O'Brien and Fleming and Lan and DeMets. Tables of boundaries are provided for the equal variance or equal censoring distribution case.
Stopping when the experimental regimen does not appear to help
Authors: Wieand Sam , Schroeder Georgene , O'Fallon Judith Rich 
Published: April 2007

AbstractWe discuss a procedure that offers the possibility of reducing the number of patients assigned to experimental regimens which are no more effective than a standard regimen. The procedure is useful in advanced cancer trials in which the length of the accrual period might be more than twice the median survival time for the standard regimen. The procedure is intuitively appealing and offers a 50 per cent chance of stopping accrual to ineffective therapies, yet is associated with a loss of power of less than 0.02.
Early stopping rules ‐ clinical perspectives and ethical considerations
Authors: Baum M. , Houghton J. , Abrams K. 
Published: April 2007

AbstractA clinical trial should only be launched in the presence of equipoise both amongst clinicians responsible for treating the disease and their patients. However, during the period of patient recruitment the chances are that the levels of equipoise will modify. In some cases, where toxicity or inferiority of efficacy are readily demonstrable, the trial needs to be stopped prematurely to prevent harm to patients. In other cases, the degree of equipoise may be increased by the very existence of the trial or from conflicting evidence reported from other studies. By reference to four specific trials it is argued that instead of definitive rules, the decision as to whether recruitment should be continued must be the role of a Data Monitoring Committee which is able to consider and respond to all the available evidence.
Early stopping of prevention trials when multiple outcomes are of interest: A discussion
Authors: Green Sylvan B. , Freedman Laurence S. 
Published: April 2007

AbstractIn large prevention and screening trials, we want to answer multiple questions simultaneously. When monitoring prevention trials with multiple disease outcomes, composite measures may be useful, such as a count of important events, or a comparison of expected mortality, There are advantages to investigating multiple interventions in the same prevention or screening trial, using all‐versus‐none, factorial, or reciprocal control designs. In these situations it may be important to answer some questions early while others are still being investigated.
Conclusions. 1: The relationship between clinical trials and clinical practice: The risks of underestimating its complexity
Authors: Liberati Alessandro 
Published: April 2007

AbstractTwo main points are addressed in the following remarks. The first is that the survival of randomized clinical trials (RCTs) as the gold standard by which to assess the effectiveness of medical technology is being seriously challenged. It is incumbent upon methodologists and clinical researchers to understand why this is happening and commit themselves to improving the process through which new possibilities for improved treatments are selected, prioritized and implemented. The second point is that trialists should abandon the idea that the availability of relevant information is a sufficient condition for a timely and widespread acceptance of RCTs results. Contrary to what health services research has repeatedly indicated, trialists still, by and large, seem to believe in a simplistic model that assumes a one‐way linear and rapid relationship between scientific knowledge and clinical practice. In the concluding section the paper pinpoints some issues to be further discussed from the point of view of monitoring and conducting trials.
Conclusions. 2
Authors: O'Neill Robert T. 
Published: April 2007

AbstractI have been asked to take an overall view of the presentations in this ‘early stopping rules’ workshop and to extract some messages from them. in attempting to meet the charge to summarize what i have heard and what i have concluded over the last two days, i will focus on the following issues: the underlying theme of the workshop concerns whether the statistical strategies proposed for monitoring trials are a help or a hindrance to the successful completion of clinical trials; how much of a problem is this and what should be done about it; whether the current diversity in practical operational aspects of data monitoring and analysis is responsible for some trials being stopped earlier than planned and thus should we begin to develop some consensus on ‘good practices and procedures’ in this area; and finally to place this topic within the broader public health framework of clinical trials of treatments for any life‐threatening disease, what are the regulatory impacts and consequences of early stopping, especially when decisions regarding the licensure and marketing of treatments depends upon results from monitored clinical trials that might be stopped early.
Assessment of predictive models for binary outcomes: An empirical approach using operative death from cardiac surgery
Authors: Marshall Guillermo , Grover Frederick L. , Henderson William G. , Hammermeister Karl E. 
Published: April 2007

AbstractPredictive models in medical research have gained popularity among physicians as an important tool in medical decision making. Eight methodological strategies for creating predictive models are compared in a large, complex data base consisting of preoperative risk and operative outcome data on 12, 712 patients undergoing coronary artery bypass grafting and entered into the Department of Veterans Affairs Cardiac Surgery Risk Assessment Program between April 1987 and March 1990. The models under consideration were developed to predict operative death (any death within 30 days following the surgical procedure or later if the result of a perioperative complication). The two strategies with the best predictive power among the eight examined were stepwise logistic regression alone and data reduction by cluster analysis combined with clinical judgement followed by a logistic regression model. The additive model based on unadjusted relative risks, the model based on Bayes' Theorem, and the logistic model using all candidate variables were good alternatives. Whether or not we imputed values did not have a significant impact on the predictive power of the models.
Precision of incidence predictions based on poisson distributed observations
Authors: Hakulinen Timo , Dyba Tadeusz 
Published: April 2007

AbstractDisease incidence predictions are useful for a number of administrative and scientific purposes. The simplest ones are made using trend extrapolation, on either an arithmetic or a logarithmic scale. This paper shows how approximate confidence prediction intervals can be calculated for such predictions, both for the total number of cases and for the age‐adjusted incidence rates, by assuming Poisson distribution of the age and period specific numbers of incident cases. Generalizations for prediction models, for example, using power families and extra‐Poisson variation, are also presented. Cancer incidence predictions for the Stockholm‐Gotland Oncological Region in Sweden are used as an example.
Monte carlo methods for exloring sensitivity to distributional assumptions in a bayesian analysis of a series of 2 × 2 tables
Authors: Raghunathan T. E. 
Published: April 2007

AbstractThis paper develops Monte Carlo methods for a Bayesian analysis of a series of 2 × 2 tables under a variety of distributional assumptions. I assume that the data in each table were generated from a pair of binomial distributions and the logarithm of odds of a favourable response follows a bivariate distribution with means that are linear functions of covariates and an arbitrary covariance matrix. I use Gibbs and importance sampling methods to obtain various characteristics of the posterior distribution of the quantities of interest. I apply the method to analyse the data from a population case‐control study. Given the size of the population at risk I also derive the posterior distribution of the risk difference defined as the difference in the probabilities of disease development in the exposed and unexposed groups.
Efficient power computation for exact and mid‐P tests for the common odds ratio in several 2 × 2 tables
Authors: Hirji Mkarim F. , Tang Man‐Lai , Vollset Stein E. , Elashoff Robert M. 
Published: April 2007

AbstractWhen designing a study that may generate a set of sparse 2 × 2 tables, or when confronted with ‘negative’ results upon exact analysis of such tables, we need to compute the power of exact tests. In this paper we provide an efficient approach for computing exact unconditional power for four exact tests on the common odds ratio in a series of 2 × 2 tables. These tests are the traditional exact test; a test based on a probability ordering of the sample space; and two tests based on ordering the sample space according to distance from the mean, or median. For each test, we consider both a conservative version and a mid‐P adjusted version. We explore three computational options for power determination: exact power computation, calculation of exact upper and lower bounds for power, and Monte Carlo confidence bounds for power. We present an interactive program implementing these options. For study design, the program may be run several times to arrive at a sample configuration with adequate power.
Efficient confidence bounds for ROC curves
Authors: Schafer Helmut 
Published: April 2007

AbstractThe specificity and sensitivity of a quantitative diagnostic marker depends on the selected cut‐off point. The ROC curve is generated by plotting sensitivity against specificity as the cut‐off point runs through the whole range of possible marker values. Confidence bounds for ROC curves may be constructed by combining two separate confidence intervals for sensitivity and specificity. In the present paper, a more efficient method, which is based on a statistical test introduced by Greenhouse and Mantel, is presented. The resulting 95 per cent confidence intervals are up to 40 per cent smaller. A formula is given to calculate the sample size necessary to obtain a confidence interval of a stipulated length at a specified point of the ROC curve. As an example, the performance of a tumour marker in the diagnosis of bone marrow metastases is investigated.
Sampling guidelines for testing secondary attack rates associated with short‐latency infectious diseases
Authors: Schork Nicholas J. 
Published: April 2007

AbstractThis paper discusses power and efficiency properties of the model proposed by Longini and Koopman for quantifying and testing secondary attack rates gathered from final size distribution data on infections in households. Sample size requirements are offered for a generalized likelihood ratio test that makes use of the secondary attack rate parameter implicated in the Longini‐Koopman model. The appropriateness of this statistic is also considered. In addition, a sequential likelihood ratio test of a secondary attack rate parameter is derived for infectious diseases with short latency or asymptomatic periods. A comparison of the proposed sequential and fixed sample tests is described. Finally, applications involving final size distributions of household influenza infections are used to illustrate the practical merits of the proposed tests.
The choice of cancer treatment based on covariate information
Authors: Yakovlev Andrej Yu. , Goot Roman E. , Osipova Tamara T. 
Published: April 2007

AbstractThis paper is concerned with methods for choosing optimal treatment for particular patients; the central idea is that information contained in covariates may be useful for this kind of decision making. For each patient estimated conditional survivor functions for each mode of treatment may be compared and the optimal treatment may be defined as the one providing higher predicted survival. Regression survival models form the basis for a pertinent statistical decision rule, and a procedure for retrospective validation of such a rule is given. The usefulness of the proposed approach is illustrated with an application to the treatment of patients with cancer of cervix uteri. The results of data analysis suggest that the choice of optimal treatment based on covariate information is of considerable promise for practical use.