Article

Stopping Randomized Trials Early for Benefit: A Protocol of the Study Of Trial Policy Of Interim Truncation-2 (STOPIT-2)

Trials (Impact Factor: 2.12). 07/2009; 10. DOI: 10.1186/1745-6215-10-49
Source: OAI

ABSTRACT Background: Randomized clinical trials (RCTs) stopped early for benefit often receive great attention and affect clinical practice, but pose interpretational challenges for clinicians, researchers, and policy makers. Because the decision to stop the trial may arise from catching the treatment effect at a random high, truncated RCTs (tRCTs) may overestimate the true treatment effect. The Study Of Trial Policy Of Interim Truncation (STOPIT-1), which systematically reviewed the epidemiology and reporting quality of tRCTs, found that such trials are becoming more common, but that reporting of stopping rules and decisions were often deficient. Most importantly, treatment effects were often implausibly large and inversely related to the number of the events accrued. The aim of STOPIT-2 is to determine the magnitude and determinants of possible bias introduced by stopping RCTs early for benefit. Methods/Design: We will use sensitive strategies to search for systematic reviews addressing the same clinical question as each of the tRCTs identified in STOPIT-1 and in a subsequent literature search. We will check all RCTs included in each systematic review to determine their similarity to the index tRCT in terms of participants, interventions, and outcome definition, and conduct new meta-analyses addressing the outcome that led to early termination of the tRCT. For each pair of tRCT and systematic review of corresponding non-tRCTs we will estimate the ratio of relative risks, and hence estimate the degree of bias. We will use hierarchical multivariable regression to determine the factors associated with the magnitude of this ratio. Factors explored will include the presence and quality of a stopping rule, the methodological quality of the trials, and the number of total events that had occurred at the time of truncation.Finally, we will evaluate whether Bayesian methods using conservative informative priors to "regress to the mean" overoptimistic tRCTs can correct observed biases. Discussion: A better understanding of the extent to which tRCTs exaggerate treatment effects and of the factors associated with the magnitude of this bias can optimize trial design and data monitoring charters, and may aid in the interpretation of the results from trials stopped early for benefit.

Full-text

Available from: German Malaga, Apr 17, 2015
0 Followers
 · 
164 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Monitoring ongoing clinical trials for early signs of effectiveness is an option for improving cost-effectiveness of trials that is becoming increasingly common. Alongside the obvious advantages made possible by monitoring, however, there are some downsides. In particular, there is growing concern in the medical community that trials stopped early for benefit tend to overestimate treatment effect. In this paper, I examine this problem from the point of view of statistical methodology, starting from the observation that the overestimation is caused by the statistical method adopted. Consequently, I argue that some modifications can and should be made to the present statistical framework in order not to miss the advantages the possibility of monitoring can grant.
    Theoretical Medicine and Bioethics 07/2013; DOI:10.1007/s11017-013-9264-2 · 0.78 Impact Factor
  • FMC - Formación Médica Continuada en Atención Primaria 09/2009; 16(7). DOI:10.1016/S1134-2072(09)71971-1
  • [Show abstract] [Hide abstract]
    ABSTRACT: BACKGROUND: Systematic reviewers may encounter a multiplicity of outcome data in the reports of randomised controlled trials included in the review (e.g. multiple measurement instruments measuring the same outcome, multiple time points, and final and change from baseline values). The primary objectives of this study are to investigate in a cohort of systematic reviews of randomised controlled trials of interventions for rheumatoid arthritis, osteoarthritis, depressive disorders and anxiety disorders: (i) how often there is multiplicity of outcome data in trial reports; (ii) the association between selection of trial outcome data included in a meta-analysis and the magnitude and statistical significance of the trial result, and; (iii) the impact of the selection of outcome data on meta-analytic results.Methods/design: Forty systematic reviews (20 Cochrane, 20 non-Cochrane) of RCTs published from January 2010 to January 2012 and indexed in the Cochrane Database of Systematic Reviews (CDSR) or PubMed will be randomly sampled. The first meta-analysis of a continuous outcome within each review will be included. From each review protocol (where available) and published review we will extract information regarding which types of outcome data were eligible for inclusion in the meta-analysis (e.g. measurement instruments, time points, analyses). From the trial reports we will extract all outcome data that are compatible with the meta-analysis outcome as it is defined in the review and with the outcome data eligibility criteria and hierarchies in the review protocol. The association between selection of trial outcome data included in a meta-analysis and the magnitude and statistical significance of the trial result will be investigated. We will also investigate the impact of the selected trial result on the magnitude of the resulting meta-analytic effect estimates. DISCUSSION: The strengths of this empirical study are that our objectives and methods are pre-specified and transparent. The results may inform methods guidance for systematic review conduct and reporting, particularly for dealing with multiplicity of randomised controlled trial outcome data.
    04/2013; 2(1):21. DOI:10.1186/2046-4053-2-21