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Sample size determination for a study with variable follow-up time.

Guogen Shan1, Yahui Zhang1, Xinlin Lu1

  • 1Department of Biostatistics, University of Florida, Gainesville, Florida, USA.

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|February 27, 2025
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Summary
This summary is machine-generated.

A new statistical model enhances sample size calculations for clinical trials by accurately assessing treatment effects over time, even with varying follow-up schedules. This method proves more powerful for non-linear disease progression compared to existing approaches.

Keywords:
Clinical trialpre-test and post-test designrandomized studysample sizespline functionstatistical power

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Modeling

Background:

  • Pre-test and post-test designs are common for assessing treatment-control differences in clinical studies.
  • Existing sample size calculation methods (subtraction, ANCOVA, linear mixed model) have limitations regarding follow-up time variability and assumptions of constant treatment effects.

Purpose of the Study:

  • To develop a novel statistical model for comparing treatment-control differences at planned follow-up times.
  • To account for variations in follow-up time and improve accuracy in sample size calculations.
  • To compare the performance of the new model against existing methods.

Main Methods:

  • Proposed a new statistical model utilizing spline functions to estimate treatment and control arm trajectories.
  • Compared the new method with subtraction, ANCOVA, and linear mixed models.
  • Evaluated performance based on type I error rate, statistical power, and sample size requirements under various conditions.
  • Applied the new method to data from an Alzheimer's disease trial.

Main Results:

  • All four methods effectively controlled the type I error rate.
  • The new method and ANCOVA demonstrated higher statistical power compared to subtraction and linear mixed models.
  • The new method showed superior power over ANCOVA in cases of non-linear disease progression.
  • The proposed model accurately estimates treatment-control differences while managing follow-up time variations.

Conclusions:

  • The developed statistical model offers an effective approach for sample size calculation in clinical trials with variable follow-up times.
  • The new method provides enhanced statistical power, particularly for studies exhibiting non-linear disease progression.
  • This approach improves the precision of treatment effect assessment in longitudinal studies.