Impact of lack-of-benefit stopping rules on treatment effect estimates of two-arm multi-stage (TAMS) trials with time to event outcome

Babak Choodari-Oskooei, Mahesh K B Parmar, Patrick Royston, Jack Bowden

Research output: Contribution to journalArticle (Academic Journal)peer-review

17 Citations (Scopus)


BACKGROUND: In 2011, Royston et al. described technical details of a two-arm, multi-stage (TAMS) design. The design enables a trial to be stopped part-way through recruitment if the accumulating data suggests a lack of benefit of the experimental arm. Such interim decisions can be made using data on an available 'intermediate' outcome. At the conclusion of the trial, the definitive outcome is analyzed. Typical intermediate and definitive outcomes in cancer might be progression-free and overall survival, respectively. In TAMS designs, the stopping rule applied at the interim stage(s) affects the sampling distribution of the treatment effect estimator, potentially inducing bias that needs addressing.

METHODS: We quantified the bias in the treatment effect estimator in TAMS trials according to the size of the treatment effect and for different designs. We also retrospectively 'redesigned' completed cancer trials as TAMS trials and used the bootstrap to quantify bias.

RESULTS: In trials in which the experimental treatment is better than the control and which continue to their planned end, the bias in the estimate of treatment effect is small and of no practical importance. In trials stopped for lack of benefit at an interim stage, the treatment effect estimate is biased at the time of interim assessment. This bias is markedly reduced by further patient follow-up and reanalysis at the planned 'end' of the trial.

CONCLUSIONS: Provided that all patients in a TAMS trial are followed up to the planned end of the trial, the bias in the estimated treatment effect is of no practical importance. Bias correction is then unnecessary.

Original languageEnglish
Pages (from-to)23
Publication statusPublished - 2013


  • Clinical Trials as Topic
  • Computer Simulation
  • Data Interpretation, Statistical
  • Disease-Free Survival
  • Early Termination of Clinical Trials
  • Endpoint Determination
  • Evidence-Based Medicine
  • Female
  • Humans
  • Ovarian Neoplasms
  • Proportional Hazards Models
  • Reproducibility of Results
  • Research Design
  • Retrospective Studies
  • Sample Size
  • Selection Bias
  • Time Factors
  • Treatment Outcome


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