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Internal pilot design for balanced repeated measures.

Xinrui Zhang1, Keith E Muller2, Maureen M Goodenow3

  • 1Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA.

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|November 23, 2017
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Summary
This summary is machine-generated.

Accurate sample size calculation in repeated measures studies is crucial. This research introduces methods for sample size recalculation using internal pilot designs in linear mixed models, improving efficiency and participant safety.

Keywords:
UNIREPfixed effects inferencelinear mixed modelpowersample size

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Epidemiology

Background:

  • Repeated measures are fundamental in clinical trials and epidemiological research.
  • Accurate variance and correlation specifications are vital for appropriate sample size selection.
  • Inadequate or excessive sample sizes lead to wasted resources and increased participant risk.

Purpose of the Study:

  • To develop theoretical results for sample size recalculation in linear mixed models with repeated measures.
  • To account for the stochastic nature of final sample size in internal pilot designs.
  • To improve the accuracy of sample size determination for longitudinal studies.

Main Methods:

  • Developed theoretical results for sample size adjustments in linear mixed models.
  • Utilized an internal pilot design framework for estimating nuisance parameters.
  • Conducted simulations to assess the impact of covariance matrix misspecification and approximation accuracy.

Main Results:

  • The proposed methods accurately control type I error rates and achieve target power.
  • Simulations demonstrated the robustness of the approximations to covariance matrix misspecification.
  • The theoretical results are applicable to longitudinal studies with balanced designs.

Conclusions:

  • Internal pilot designs offer a robust approach for sample size recalculation in studies with repeated measures.
  • The developed methods enhance the efficiency and reliability of sample size planning in biostatistical research.
  • Applied to a study on early antiretroviral therapy for youth living with HIV, demonstrating practical utility.