Early Stopping Of Clinical Trials Research Articles

Clinical Trial Design in Acute Lung Injury—Issues and Controversies

The evolution of scientific investigation in acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) is replete with examples of effective bench-to-bedside transitions, pioneering research methodology, and international collaboration. This article highlights for clinicians a selection of challenges and controversies of clinical trial design in ALI and ARDS. A section on Patient Selection highlights controversial trade-offs between homogeneous and heterogeneous study populations. A section on Clinical Protocols covers blinding, process-of-care interventions, usual-care control groups, and standardized protocols for adjunctive care. The third section discusses the quest for ideal Study Outcomes. The final section presents Interpretation Controversies, such as early stopping of clinical trials.

Meta‐analysis of clinical trials with early stopping: an investigation of potential bias

Clinical trials that stop early for benefit have a treatment difference that overestimates the true effect. The consequences of this fact have been extensively debated in the literature. Some researchers argue that early stopping, or truncation, is an important source of bias in treatment effect estimates, particularly when truncated studies are incorporated into meta-analyses. Such claims are bound to lead some systematic reviewers to consider excluding truncated studies from evidence synthesis. We therefore investigated the implications of this strategy by examining the properties of sequentially monitored studies conditional on reaching the final analysis. As well as estimation bias, we studied information bias measured by the difference between standard measures of statistical information, such as sample size, and the actual information based on the conditional sampling distribution. We found that excluding truncated studies leads to underestimation of treatment effects and overestimation of information. Importantly, the information bias increases with the estimation bias, meaning that greater estimation bias is accompanied by greater overweighting in a meta-analysis. Simulations of meta-analyses confirmed that the bias from excluding truncated studies can be substantial. In contrast, when meta-analyses included truncated studies, treatment effect estimates were essentially unbiased. Previous analyses comparing treatment effects in truncated and non-truncated studies are shown not to be indicative of bias in truncated studies. We conclude that early stopping of clinical trials is not a substantive source of bias in meta-analyses and recommend that all studies, both truncated and non-truncated, be included in evidence synthesis.

Early Stopping of Clinical Trials: Charting the Ethical Terrain

The decision to terminate a clinical trial earlier than planned is often described as ethically problematic, but it is rarely systematically analyzed as an ethical issue in its own right. This paper provides an overview of the main ethical considerations at stake in such decisions and of the main tensions between these considerations. Arguments about informed consent and the impact of early stopping on research and society are explored. We devote particular attention to a familiar conflict that arises with special urgency when early data suggest that the experimental treatment is superior. Should the trial be stopped so that participants in the control group will not be allocated a seemingly inferior treatment, or should it continue in pursuit of evidence conclusive enough to improve the care of future patients? We scrutinize three ways to address this problem. Rather than dissolving the tension, they represent different trade-offs between the respective welfare interests of subjects and future patients.

A systematic review of the reporting of Data Monitoring Committees' roles, interim analysis and early termination in pediatric clinical trials

BackgroundDecisions about interim analysis and early stopping of clinical trials, as based on recommendations of Data Monitoring Committees (DMCs), have far reaching consequences for the scientific validity and clinical impact of a trial. Our aim was to evaluate the frequency and quality of the reporting on DMC composition and roles, interim analysis and early termination in pediatric trials.MethodsWe conducted a systematic review of randomized controlled clinical trials published from 2005 to 2007 in a sample of four general and four pediatric journals. We used full-text databases to identify trials which reported on DMCs, interim analysis or early termination, and included children or adolescents. Information was extracted on general trial characteristics, risk of bias, and a set of parameters regarding DMC composition and roles, interim analysis and early termination.Results110 of the 648 pediatric trials in this sample (17%) reported on DMC or interim analysis or early stopping, and were included; 68 from general and 42 from pediatric journals. The presence of DMCs was reported in 89 of the 110 included trials (81%); 62 papers, including 46 of the 89 that reported on DMCs (52%), also presented information about interim analysis. No paper adequately reported all DMC parameters, and nine (15%) reported all interim analysis details. Of 32 trials which terminated early, 22 (69%) did not report predefined stopping guidelines and 15 (47%) did not provide information on statistical monitoring methods.ConclusionsReporting on DMC composition and roles, on interim analysis results and on early termination of pediatric trials is incomplete and heterogeneous. We propose a minimal set of reporting parameters that will allow the reader to assess the validity of trial results.

The conscientious judgement of a DSMB – statistical stopping rules re-examined

In the light of the recent failure of a large cardiovascular mortality and morbidity study with torcetrapib, we have undertaken a post hoc review of the decisions taken by the Data and Safety Monitoring Board (DSMB) in that study. A number of other studies in which complex decisions were made by DSMBs are also reviewed. The examples illustrate the complexities involved in the decision-making process by DSMBs and indicate that too much reliance on formal statistical stopping rules should be avoided due to a risk of delaying the identification of an unacceptable emergent safety signal. The review was based on information submitted by the sponsors of the studies to the Medical Products Agency. Multiple approaches to assessing the efficacy and safety in clinical trials need to be considered in order to facilitate early stopping of clinical trials with a negative risk benefit balance. Such systems may, for example, include the use of p-value flags and/or a complementary statistical analysis of the likelihood of achieving the study objective based on the data obtained to date.

Sequential analysis of latent variables using mixed‐effect latent variable models: Impact of non‐informative and informative missing data

Sequential methods allowing for early stopping of clinical trials are widely used in various therapeutic areas. These methods allow for the analysis of different types of endpoints (quantitative, qualitative, time to event) and often provide, in average, substantial reductions in sample size as compared with single-stage designs while maintaining pre-specified type I and II errors. Sequential methods are also used when analysing particular endpoints that cannot be directly measured, such as depression, quality of life, or cognitive functioning, which are often measured through questionnaires. These types of endpoints are usually referred to as latent variables and should be analysed with latent variable models. In addition, in most clinical trials studying such latent variables, incomplete data are not uncommon and the missing data process might also be non-ignorable. We investigated the impact of informative or non-informative missing data on the statistical properties of the double triangular test (DTT), combined with the mixed-effects Rasch model (MRM) for dichotomous responses or the traditional method based on observed patient's scores (S) to the questionnaire. The achieved type I errors for the DTT were usually close to the target value of 0.05 for both methods, but increased slightly for the MRM when informative missing data were present. The DTT was very close to the nominal power of 0.95 when the MRM was used, but substantially underpowered with the S method (reduction of about 23 per cent), irrespective of whether informative missing data were present or not. Moreover, the DTT using the MRM allowed for reaching a conclusion (under H(0) or H(1)) with fewer patients than the S method, the average sample number for the latter increasing importantly when the proportion of missing data increased. Incorporating MRM in sequential analysis of latent variables might provide a more powerful method than the traditional S method, even in the presence of non-informative or informative missing data.

Early stopping of clinical trials

Early stopping of clinical trials in favour of a new treatment creates ethical and scientific difficulties, which are different from those associated with early stopping due to toxicity or futility. Two major breast cancer trials have recently taken such a decision, and the problem is relevant for several ongoing trials. Here we argue that such a decision should be taken with the utmost gravity and should be based on a clear overall clinical benefit for the new treatment, and not as an automatic response to crossing a predefined threshold. Predefined rules can be used to trigger a debate within the Independent Data Monitoring and Safety Committee (IDMC) about early stopping, but the IDMC should retain the responsibility of assessing overall clinical benefit in making its recommendation.

Early stopping of clinical trials in lupus and other uncommon rheumatologic diseases.

Use of statistical methods for early stopping of clinical trials allows more efficient and ethical utilization of subjects. In uncommon diseases, where the pool of potential subjects is limited, these methods provide a way to maximize the information gathered from trials. For trials in lupus, methods for early stopping should be more widely utilized. We discuss the ethics, practical aspects, pros and cons, and statistical foundations of some established methods for early stopping, with an emphasis on those that have available implementation in computer software. An example of the design and analysis of a treatment trial in systemic lupus erythematosus with the triangular test method of early stopping is also given.

A method for deciding early stopping of inconclusive case-control studies in settings where data are stratified.

Statistical methodology for early stopping of clinical trials is well developed. However, in epidemiologic studies, methods for deciding early stopping have rarely been considered. In contrast to clinical trials, termination due to an early significant result is indeed seldom relevant, because further expenditure is then likely to be worthwhile in order to obtain a more precise effect estimate and to more precisely account for confounders. On the other hand, if data obtained early indicate an inconclusive final result and if continuation of the epidemiologic study is expensive, then it might be desirable to stop the study early. The present paper proposes a method for evaluating the possibility that an ongoing case-control study will produce an inconclusive final result. The method is developed for stratified case-control data. In principle, probable final results, given the available data, are predicted by repeatedly simulating the remaining data using plausible assumptions on the true exposure effect. These assumptions involve the current effect estimate as well as some alternatives, chosen with respect to the investigator's prior hypothesis concerning a harmful exposure effect, which are in reasonable agreement with the available data.