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Related Experiment Videos

The design of multicentre trials.

Valerii Fedorov1, Byron Jones

  • 1Research Statistics Unit, GlaxoSmithKline Pharmaceuticals, Philadelphia, PA, USA.

Statistical Methods in Medical Research
|June 23, 2005
PubMed
Summary

Multicentre trials require combining data using combined response to treatment (CRT). A random-effects model is preferred for accurate analysis, preventing underpowered trials due to variability.

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology

Background:

  • Multicentre trials present challenges in combining data for overall treatment evaluation.
  • Existing methods may not adequately account for sources of variability.

Purpose of the Study:

  • To propose and evaluate the combined response to treatment (CRT) as an overall measure for multicentre trials.
  • To introduce the expected combined response to treatment (ECRT) within a random-effects model.
  • To develop methods for optimizing trial design, including the number of centres and patients.

Main Methods:

  • Comparison of fixed-effects and random-effects models for CRT estimation.
  • Analysis of variance inflation due to patient enrollment variability.
  • Development of optimization methods using random-effects model properties and a loss function.

Main Results:

  • Random-effects models are preferred for estimating CRT (ECRT).
  • Variability in patient enrollment and enrolment rates inflates ECRT variance.
  • Failure to account for variability can lead to underpowered trials.

Conclusions:

  • The random-effects model provides a robust framework for analysing multicentre trial data.
  • Proper accounting for all sources of variability is crucial for trial power.
  • Proposed methods can optimize the number of centres and patients to minimize costs and maximize trial efficiency.

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