bluuebunny/crossref_metadata_2025_split huggingface dataset:
The use of external monitoring committees in clinical trials of the national institute of allergy and infectious diseases
Authors: Ellenberg Susan S. , Myers Maureen W. , Blackwelder William C. , Hoth Daniel F. 
Published: April 2007

AbstractRandomized clinical trials are being conducted and/or sponsored by all scientific divisions of the National Institute of Allergy and Infectious Diseases (NIAID). External committees to review the progress of ongoing trials and to make recommendations to the Institute concerning continuation or termination are an integral part of many of these trials. These committees have evolved considerably from the ad hoc committees, called together when a need arose, which were used beginning in the mid 1970s. Currently, there are many monitoring committees operating for NIAID‐sponsored trials. They function in a variety of ways, based partially on historical precedent and partially on the specialized requirements of the particular trial; there is no ‘standard operating procedure’ for the Institute, or even for divisions within the Institute. One of the major issues faced in establishing the data and safety monitoring board for AIDS treatment trials was access to the meetings of this board and to the reports of interim data that the board reviewed. After much discussion, procedures were established that restrict such access to a very limited group of programme and statistical centre staff. These procedures, while remaining controversial, appear necessary to ensure confidentiality and the integrity of the clinical trials process.
Data monitoring and interim analyses in the pharmaceutical industry: Ethical and logistical considerations
Authors: Rockhold Frank W. , Enas Gregory G. 
Published: April 2007

AbstractThe characteristics of data monitoring and the need for the use of data monitoring committees in clinical trials sponsored by the pharmaceutical industry differ from those of trials sponsored by government. Data monitoring is a continuous process in industry trials due to the regulatory requirements and the need to more thoroughly evaluate safety of new compounds. As part of this process, interim analyses are employed to make decisions about treatment effects. In some cases, such analyses may require the use of an external data monitoring committee to assist in the data review, analysis and decision making. A number of examples of interim analyses, with and without data monitoring committees, are discussed. Issues surrounding the need for external data monitoring committees and recommendations are presented. In particular the issues of sponsor participation in the data monitoring committee and controls of the decision making process are considered.
Monitoring of clinical trials and interim analyses from a drug Sponsor's point of view
Authors: Williams George W. , Davis Robert L. , Getson Albert J. , Gould A. Lawrence , Hwang Irving K. , Matthews Holly , Shih Weichung Joseph , Snapinn Steven M. , Walton‐Bowen Karen L. 
Published: April 2007

AbstractThis paper illustrates aspects of data monitoring of clinical trials in the pharmaceutical industry. Formal interim analyses are performed at least in part to address the question of whether the trial should proceed or whether there should be an early termination of the trial. For formal interim analyses, frequently independent data and safety monitoring committees are utilized for monitoring clinical trials, and adjustments to nominal significance levels for test statistics are required. Various statistical methods developed during the last fifteen years are utilized. Administrative interim analyses are those analyses that are performed without any intention to stop the trial as a consequence of those analyses. For administrative interim analyses, adjustments to significance levels may not be required, but results must still be carefully interpreted. Regardless of the interim analyses performed, it is critical that the plans for interim analyses be identified in the study protocol, and the dissemination of interim results be carefully restricted. The following clinical trials sponsored by Merck Sharp and Dohme Research Laboratories (MSDRL) will illustrate these points: CONSENSUS; CONSENSUS II; 4S; Haemophilus influenza type b efficacy trial; famotidine in upper gastrointestinal haemorrhage, and a phase II analgesic study. It is anticipated that data monitoring and interim analysis activities will increase for future clinical trials due to the availability of appropriate statistical methods and improved data management systems.
Monitoring clinical trials: Experience of, and proposals under consideration by, the cancer therapy committee of the British medical research council
Authors: Parmar Mahesh K. B. , Machin David 
Published: April 2007

AbstractThe accumulating data from all randomized trials conducted by the Cancer Therapy Committee (CTC) of the British Medical Research Council are monitored on a regular basis. However, for important practical reasons the form of this data monitoring may vary from trial to trial. Thus a trial addressing what is considered a major question in the treatment of cancer patients, (a ‘pivotal’ or ‘high profile’ trial), has a formal data monitoring committee (DMC). This is usually made up of two clinicians and one statistician who are completely independent of the trial organization and do not enter patients into the trial. Other trials, which constitute the majority, are monitored by a less formal trial progress group made up of the clinical co‐ordinator and trial statistician, sometimes supplemented by a trial participant. Experience with this dual system has led to a new proposal: if the trial progress group wish to modify or stop a trial then they are required to set up and consult an ad hoc DMC for independent advice. This proposal has many advantages, including maximizing the use of resources available, while achieving the degree of objectivity in decision‐making required for the many different types of cancer trials conducted by the CTC.
The use of data monitoring committees in canadian trial groups
Authors: Pater Joseph L. 
Published: April 2007

AbstractFollowing a brief overview of the funding and structure of clinical trials research in Canada, experience with data monitoring committees (DMCs) in four clinical trials organizations is reviewed. Whether committees are required, their mode of selection, composition, degree of independence, mandate and reporting relationships have varied substantially among the groups. Overall the Canadian experience with DMCs has been representative, although formalization of the role of these committees has not proceeded as far as it has in some US agencies.
Interim analyses, stopping rules and data monitoring in clinical trials in europe
Authors: Buyse Marc 
Published: April 2007

AbstractA survey was carried out to identify the current policies of European cooperative groups with respect to interim analyses, stopping rules and data monitoring of ongoing clinical trials. The policies differ widely, from informal interim analyses distributed among all participating investigators, to planned interim analyses carried out by an independent statistician and scrutinized by a data monitoring committee. Different situations clearly call for different policies: for instance, trials of new drugs in AIDS need to be monitored more closely than trials of non‐toxic adjuvant therapies for cancer. Likewise, trials with an immediately measurable end‐point (such as the large‐scale trials in myocardial infarction) need more intensive monitoring than those in which the outcome assessment requires prolonged follow‐up. In all cases, however, it seems useful to articulate explicit data monitoring procedures in the trial protocol. In general, an independent data monitoring committee is essential to advise on the desirability to continue accrual into the trial, or to stop it early.
Practical issues in data monitoring of clinical trials: Summary of responses to a questionnaire at nih
Authors: Geller Nancy L. , Stylianou Mario 
Published: April 2007

AbstractA targeted poll was undertaken to compare and contrast models of data monitoring of randomized clinical trials sponsored by the National Institutes of Health. In an attempt to represent the institutes which conduct clinical trials, twelve individuals were selected and asked to respond to a questionnaire specifically prepared for this workshop. The response rate was 100 per cent. Most of the large trials sponsored by the institutes have independent, formally constituted data monitoring committees. There was one institute which does not have any data monitoring committees. The questionnaire is described in detail and a summary of the results is given.
Data monitoring boards in the pharmaceutical industry
Authors: Herson Jay 
Published: April 2007

AbstractThis paper describes the characteristics and responsibilities of data monitoring boards (DMBs) employed in clinical trials sponsored by pharmaceutical firms. These DMBs are compared with DMBs in clinical trials sponsored by the National Institutes of Health (NIH). All trials discussed are assumed to be phase III pre‐market clinical trials. It is seen that DMBs in pharmaceutical industry trials have more limited responsibilities than DMBs in NIH clinical trials. Pharmaceutical industry DMBs are employed to add credibility to trials by certifying critical clinical trial processes such as patient evaluability, interim analyses, evaluation of efficacy endpoints, and safety monitoring. An example of a pharmaceutical industry DMB's charter and operations is presented for a clinical trial for the prevention of stress ulcers. The specific problems encountered when a pharmaceutical industry clinical trial is conducted by NIH under the auspices of an NIH‐appointed DMB are discussed. New uses for DMBs in the pharmaceutical industry are introduced and future trends in the industry with respect to DMBs are examined.
Data monitoring committees and capturing relevant information of high quality
Authors: Fleming Thomas R. 
Published: April 2007

AbstractThe integrity and credibility of randomized clinical trials, designed to provide a definitive evaluation of therapeutic interventions, can be substantially enhanced if periodic review of results on the relative efficacy of treatments is conducted by a data monitoring committee (DMC). The role of the DMC can include some important activities which promote obtaining relevant information of high quality. In the final stage of study development, these include review of protocol‐specified study objectives and design, and review of procedures planned for capturing high‐quality data and for reporting to the DMC. At analyses during the trial, these include careful review of data completeness and accuracy to assist in early detection and resolution of evolving problems.
Data monitoring committees: The moral case for maximum feasible independence
Authors: Walters Leroy 
Published: April 2007

AbstractSince data monitoring committees (DMCs) began to be established in the 1970s, they have reviewed the clinical outcome data emerging from ongoing trials. The thesis of this essay is that DMCs most effectively promote the validity and credibility of randomized clinical trials when they perform this function with the maximum feasible independence. The essay argues that four types of individuals and groups should be excluded from the interim monitoring of outcome data: (1) investigators entering patients into a trial; (2) principal investigators or study chairs; (3) representatives of commercial firms connected with a trial; and (4) representatives of regulatory agencies. The role of statistical groups and officials of government funding agencies in the review of outcome data is also discussed.
Some FDA perspectives on data monitoring in clinical trials in drug development
Authors: O'neill Robert T. 
Published: April 2007

AbstractThe FDA's interest in data monitoring of clinical trials derives from its public health responsibility to assure the safety and efficacy of new drugs based on evidence from adequate and well‐controlled studies. Therefore the FDA is concerned that clinical trials of new drugs are designed and carried out in a manner that will insure the integrity and validity of study inferences. The FDA regulation and guidelines recognize the role of data monitoring and the variety and diversity of situations utilizing a data monitoring process in clinical studies. This paper describes relevant aspects of the regulations and guidelines, some concerns the FDA has with regard to monitoring of both government‐ and industry‐sponsored trials and the consequences of early termination of trials of new drugs in the investigational and marketed stages. Comments include advice on communication between the FDA and data monitoring committees.
Use of backcalculation for estimation of the probability of progression from hiv infection to aids
Authors: Marion Stephen A. , Schechter Martin T. 
Published: April 2007

AbstractBackcalculation has been used to estimate the rate of past HIV infection and to predict future AIDS incidence. In this study we examine another use: estimating the probability of progression from HIV infection to AIDS as a function of time from infection. Given observed AIDS incidence data, the technique of backcalculation estimates the most likely number of persons infected with HIV in the past. Assumptions about probability of progression from HIV infection to AIDS are necessary. By varying these assumptions and examining the resulting goodness of fit to the AIDS incidence data, we can theoretically estimate parameters of progression. We report on implementation of this method and examine its practical utility in deciding among four competing progression models specified on a priori grounds. The four specific models comprise three Weibull distributions with medians of 8, 10, and 12 years, respectively, and one model that beings as a Weibull with 8 year median but where the hazard is level after 3.5 years. To employ asymptotic maximum likelihood methods, we define a two parameter family of progression models that includes all four a priori models. One parameter sets the scale for an initial Weibull progression (the shape parameter being fixed for all models), and the other specifies a levelling point after which the hazard remains constant.AIDS incidence data from Canada's national surveillance system provided the empiric data for this evaluation. First we corrected these data for reporting delay by Poisson modelling of the delay distribution. We used three parametric families of infection curves: step‐function, log‐logistic, and logistic.The results support the hypothesis of an early levelling of the hazard function. When we fixed the scale parameter to that of the Weibull curve with 8 year median, the maximum likelihood estimate of the levelling point was 2.7 years, and a clearly superior fit was produced compared to a pure Weibull progression with the same scale parameter (likelihood ratio chi‐square of 10.6 on 1 degree of freedom, p = 0.001). The maximum was indistinguishable in fit from the levelling point of 3.5 years hypothesized in advance (chi‐square = 0.30, d.f. = 1, p = 0.58). Backcalculation, however, could not determine the Weibull scale parameter itself because the likelihood was quite flat as a function of this parameter. We conclude that one must determine the parameters governing the initial shape of the hazard function from other kinds of data.We interpret the model with levelling of the hazard as an empirical description of progression in a real population rather than as natural history in a homogeneous population. We make comparisons with other recently proposed models of progression.
Using orthogonal polynomial scores in summarizing and evaluating longitudinal data collected in phase I and II clinical pharmacology studies
Authors: Bradstreet Thomas E. 
Published: April 2007

AbstractOrthogonal polynomial scores (OPS) is a simple, biologically meaningful approach to characterize longitudinal data in phase I and II clinical pharmacology trials. It describes average, linear, quadratic and higher order polynomial characteristics of each subject's response over time with use of composite scores computed from linear combinations of the observed data. The statistical evaluation of the composite scores is univariate. For studies with a small number of experimental units and with many repeated measures, OPS may offer advantages over the use of summary measures such as the maximum response (MAX), the time at which MAX occurred (TMAX), or the area under the response curve (AUC), and other popular approaches such as time‐point‐by‐time‐point, split‐plot, and multivariate analyses.
Two‐stage tests for studying monotherapy and combination therapy in two‐by‐two factorial trials
Authors: Hung H. M. James 
Published: April 2007

AbstractTwo‐stage testing involves a preliminary test of a nuisance parameter prior to testing main hypothesis. In a two‐by‐two factorial trial, the treatment interaction is the nuisance to the inference about the efficacy of one of the treatments given alone. In comparing a combination therapy to both of its component therapies, the nuisance parameter is the difference in the component effects. When the preliminary test is an integral part of inference about the main parameter, the actual level of significance for the two‐stage test procedure can be much higher than the desired nominal level. If one places no restriction on the value of the nuisance parameter, then any two‐stage test with its significance level properly controlled has undesirable properties. This applies to comparative studies of combination agents relative to the component agents. When the interaction with an ineffective treatment is null, two‐stage testing may have some power advantage for assessing monotherapy efficacy.
Markov chain modelling of bioassay toxicity procedures
Authors: Hadjinicola George , Goldstein Larry 
Published: April 2007

AbstractMany bioassay procedures to test a new substance for toxicity, like some phase I clinical trials, can be modelled as first‐order Markov chains with absorbing states. We provide a general framework for such procedures. We show how one may calculate under any dose response curve assumption final absorption probabilities, and a number of total average costs such as time required until trial completion, number of subjects treated, number of subjects exhibiting unaceptable levels of toxicity, and amount of medication administered. One may use the above information associated with the trial for an a priori comparison between competing designs under consideration. We provide an example of such a comparison.
Confidence limits based on the first occurrence of an event
Authors: George Varghese T. , Elston Robert C. 
Published: April 2007

AbstractConfidence limits for population prevalence based on the first occurrence of an item in a medical database, or for incidence based on time to first occurrence, should be based on the geometric or exponential distributions, respectively. These intervals are presented and compared with the corresponding intervals based on the binomial and Poisson distributions. The lower confidence limits are shown to be the same, but the upper limits are smaller, hence leading to shorter intervals. Applications of these intervals are also presented.
Sample size and power for prospective analysis of relative risk
Authors: Blackwelder William C. 
Published: April 2007

AbstractIn a placebo‐controlled vaccine efficacy trial of a trial of equivalence of vaccines, one may wish to show that relative risk of disease is less than a specified value R0, not equal to one. This paper compares three methods for estimating relative risk in the binomial setting, based on a logarithmic transformation, likelihood scores, and a Poisson approximation. Exact power and size of test are calculated by enumeration of possible binomial outcomes, and power is approximated from asymptotic formulations. Although the score method is generally preferable, for most studies of practical interest the log and score methods are comparable, and the Poisson method is also appropriate for small risks, up to about 0.05. When true and null relative risks are less than one, unequal allocation of study individuals can increase power, and the asymptotic formula for the log method may substantially understimate power; in such a study the power approximation for the score method is more reliable, even if the log method is used in analysis. Exact power calculations are helpful in planning studies. The log and Poisson methods, but not the score method, apply readily in the case of unequal follow‐up.
Methods for epidemiologic analyses of multiple exposures: A review and comparative study of maximum‐likelihood, preliminary‐testing, and empirical‐bayes regression
Authors: Greenland Sander 
Published: April 2007

AbstractMany epidemiologic investigations are designed to study the effects of multiple exposures. Most of these studies are analysed either by fitting a risk‐regression model with all exposures forced in the model, or by using a preliminary‐testing algorithm, such as stepwise regression, to produce a smaller model. Research indicates that hierarchical modelling methods can outperform these conventional approaches. I here review these methods and compare two hierarchical methods, empirical‐Bayes regression and a variant I call ‘semi‐Bayes’ regression, to full‐model maximum likelihood and to model reduction by preliminary testing. I then present a simulation study of logistic‐regression analysis of weak exposure effects to illustrate the type of accuracy gains one may expect from hierarchical methods. Finally, I compare the performance of the methods in a problem of predicting neonatal mortality rates. Based on the literature to date, I suggest that hierarchical methods should become part of the standard approaches to multiple‐exposure studies.
Kaplan—meier, marginal or conditional probability curves in summarizing competing risks failure time data?
Authors: Pepe Margaret Sullivan , Mori Motomi 
Published: April 2007

AbstractIn the context of competing risks the Kaplan—Meier estimator is often unsuitable for summarizing failure time data. We discuss some alternative descriptive methods including marginal probability and conditional probability estimators. Two‐sample test statistics are also presented.
Sequential monitoring of clinical trials: The role of information and brownian motion
Authors: Lan K. K. Gordon , Zucker David M. 
Published: April 2007

AbstractSequential monitoring has been a topic of major interest in clinical trials methodology over the past two decades. This paper presents a unified conceptual framework for sequential monitoring that covers a wide variety of monitoring procedures in a wide variety of clinical trial settings. The central elements of this framework consist of a suitable concept of statistical information and a scheme for using this concept as a basis for summarizing the accumulating results of a trial in a standardized form, through a stochastic process that can be shown to approximate classical Brownian motion. The ideas are developed in a simple step‐by‐step fashion and illustrated by several practical examples.
Conditioned life tables from registries with unidentified random losses
Authors: Tallis G. M. , Leppard P. , O'neill T. J. 
Published: April 2007

AbstractWe identify two types of random loss that may afflict registries which use ‘passive follow‐up’. The effects of these errors on estimates related to survival are examined mathematically and numerically. A procedure for correcting the attendant biases is suggested, and its strengths and limitations are explored.