Stopping randomized trials early for benefit: A protocol of the Study Of Trial Policy Of Interim Truncation-2 (STOPIT-2)

Matthias Briel, Melanie Lane, Victor M. Montori*, Dirk Bassler, Paul Glasziou, German Malaga, Elie A. Akl, Ignacio Ferreira-Gonzalez, Pablo Alonso-Coello, Gerard Urrutia, Regina Kunz, Carolina Ruiz Culebro, Suzana Alves da Silva, David N. Flynn, Mohamed B. Elamin, Brigitte Strahm, Mohammad Hassan Murad, Benjamin Djulbegovic, Neill K J Adhikari, Edward J. MillsFemida Gwadry-Sridhar, Haresh Kirpalani, Heloisa P. Soares, Nisrin O. Abu Elnour, John J. You, Paul J. Karanicolas, Heiner C. Bucher, Julianna F. Lampropulos, Alain J. Nordmann, Karen E A Burns, Sohail M. Mulla, Heike Raatz, Amit Sood, Jagdeep Kaur, Clare R. Bankhead, Rebecca J. Mullan, Kara A. Nerenberg, Per Olav Vandvik, Fernando Coto-Yglesias, Holger Schünemann, Fabio Tuche, Pedro Paulo M Chrispim, Deborah J. Cook, Kristina Lutz, Christine M. Ribic, Noah Vale, Patricia J. Erwin, Rafael Perera, Qi Zhou, Diane Heels-Ansdell, Tim Ramsay, Stephen D. Walter, Gordon H. Guyatt

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

24 Citations (Scopus)


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.

Original languageEnglish
Article number49
Publication statusPublished - 6 Jul 2009
Externally publishedYes


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