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Sample Size Determination for Response-Adaptive Randomization With Recurrent Event Responses and Unequal Follow-Up

Junjiang Zhong1, Xianggao Hu2, Jingya Gao3

  • 1School of Mathematics and Statistics, Xiamen University of Technology, Xiamen, China.

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|May 31, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces sample size calculation methods for clinical trials using a doubly biased coin design (DBCD). The new procedure accounts for unequal follow-up times and patient dropouts, preventing underestimation of required sample sizes.

Keywords:
allocation functiondoubly biased coin designnegative binomial modelrecurrent eventsunequal follow‐up time

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Medical Research Methodology

Background:

  • Recurrent event data are common in clinical trials, and the doubly biased coin design (DBCD) has shown benefits.
  • Existing literature lacks comprehensive methods for sample size determination specifically for DBCD.
  • Patient dropout and unequal follow-up times can significantly impact trial outcomes and sample size calculations.

Purpose of the Study:

  • To develop and present methods for calculating sample size for clinical trials employing DBCD.
  • To propose a sample size determination procedure that accommodates unequal follow-up times due to patient dropout.
  • To quantify the impact of dropouts on sample size and treatment allocation.

Main Methods:

  • Development of theoretical methods for sample size computation under DBCD.
  • Incorporation of unequal follow-up times and patient dropout into the sample size determination procedure.
  • Derivation of theoretical results to assess the influence of dropouts on sample size and allocation ratios.

Main Results:

  • The proposed methods provide accurate sample size calculations for DBCD, considering unequal follow-up.
  • Failure to account for patient dropout leads to an underestimation of the required sample size.
  • Theoretical results quantify the impact of dropouts on both sample size and treatment allocation proportions.

Conclusions:

  • The developed sample size determination procedure is crucial for the validity of clinical trials using DBCD.
  • The procedure accurately accounts for patient dropout and unequal follow-up, ensuring adequate statistical power.
  • Simulation studies and a clinical example demonstrate the practical utility and advantages of the proposed method.