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'Optimism bias' in contemporary national clinical trial network phase III trials: are we improving?

Kaveh Zakeri1, Sonal Noticewala2, Lucas Vitzthum2

  • 1Radiation Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, USA; Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, USA.

Abstract

Insights

Optimism bias in cancer trials is reduced but still present. Most National Clinical Trials Network (NCTN) trials do not show significant treatment benefits, highlighting the need for better effect size rationalization in protocols.

Area of Science:

  • Oncology
  • Clinical Trials
  • Biostatistics

Background:

  • Overestimating treatment effects, known as optimism bias, has been linked to underpowered clinical trials.
  • The prevalence of optimism bias in contemporary National Clinical Trials Network (NCTN) cancer clinical trials remains largely unknown.

Purpose of the Study:

  • To determine the prevalence of optimism bias in NCTN phase III cancer clinical trials.
  • To assess the correlation between trial-related factors and study outcomes.
  • To evaluate the provision of rationales for hypothesized effect sizes in trial protocols.

Main Methods:

  • Systematic review of NCTN phase III randomized trials published between January 2007 and January 2017.
  • Comparison of hypothesized versus observed treatment effects.
  • Review of trial protocols for rationales supporting effect size estimations.

Main Results:

  • Of 130 eligible trials, 21.5% showed statistically significant benefits for experimental treatments.
  • The median ratio of observed-to-expected hazard ratios was 1.07 for significant trials, compared to 1.32 for non-significant trials.
  • Only 9.8% of trials observed an effect size as large as projected, and 64.6% lacked a rationale for the proposed effect size.

Conclusions:

  • Contemporary NCTN trials exhibit reduced optimism bias compared to earlier periods.
  • However, most NCTN phase III trials still fail to demonstrate statistically significant benefits for new cancer therapies.
  • Improved rationalization of proposed effect sizes in research protocols is crucial for future trial design and success.

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