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A burn-in(g) question: How long should an initial equal randomization stage be before Bayesian response-adaptive

Edwin Yn Tang1, Stef Baas2, Daniel Kaddaj3

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Statistical Methods in Medical Research
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Summary
This summary is machine-generated.

Response-adaptive randomization (RAR) trials benefit from a burn-in period, but its optimal length is unclear. This study introduces an exact evaluation method showing burn-in length significantly impacts trial power and accuracy, necessitating careful selection for optimal results.

Keywords:
Conditional exact testbinary outcomesexact operating characteristicstwo-arm trialunconditional exact test

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Bayesian Statistics

Background:

  • Response-adaptive randomization (RAR) enhances participant benefit in clinical trials but complicates statistical analysis.
  • A non-adaptive 'burn-in' period is often used to mitigate these complexities, yet optimal duration guidance is lacking.

Purpose of the Study:

  • To introduce an exact evaluation approach for assessing the impact of burn-in length on statistical operating characteristics in two-arm binary Bayesian RAR (BRAR) designs.
  • To provide guidance on selecting optimal burn-in periods for BRAR trials.

Main Methods:

  • Developed an exact evaluation approach to analyze statistical operating characteristics of BRAR designs with varying burn-in lengths.
  • Investigated the effects of burn-in duration on type I error rates, power, and estimation bias.
  • Utilized exact tests conditioning on total successes and compared them with calibration and asymptotic tests.

Main Results:

  • Common calibration and asymptotic tests exhibit type I error rate inflation in BRAR designs without a burn-in period.
  • Increasing burn-in length reduces but does not eliminate type I error inflation, highlighting the need for exact tests.
  • Exact tests conditioning on total successes demonstrate superior average and minimum power across various burn-in lengths.
  • Burn-in length significantly affects power and participant benefit, with optima often occurring at intermediate lengths.
  • Test statistics influence type I error rates and power; estimation bias varies with treatment effect size and trial size.

Conclusions:

  • The choice of burn-in length is critical for the statistical validity and efficiency of BRAR trials.
  • Exact evaluation methods are essential for accurate assessment of BRAR designs, particularly concerning type I error rates.
  • Optimal burn-in periods are not necessarily the shortest or longest, emphasizing the need for tailored design considerations.