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Is the 'simple carry-over' model useful?

S J Senn1

  • 1Medical Department, CIBA-GEIGY Ltd, Basle, Switzerland.

Statistics in Medicine
|April 1, 1992
PubMed
Summary
This summary is machine-generated.

Standard statistical models for carry-over effects in cross-over trials are often unreasonable. For dose-finding trials, these models offer no guaranteed protection against significant carry-over effects, questioning their reliability.

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Pharmacometrics

Background:

  • Cross-over trial designs are frequently used in clinical research to assess treatment effects.
  • Carry-over effects, where the effect of a previous treatment persists into the next period, can bias results.
  • Existing statistical models for handling carry-over effects are commonly applied but their validity is questioned.

Purpose of the Study:

  • To critically evaluate the adequacy of standard statistical models used for analyzing carry-over effects in cross-over trials.
  • To demonstrate the limitations of these models using a specific example of a dose-finding trial with a non-linear dose-response.

Main Methods:

  • Development of a theoretical argument against the reasonableness of typical carry-over effect models.

Related Experiment Videos

  • Illustration using a dose-finding trial scenario employing a Williams square design.
  • Analysis of a non-linear dose-response relationship to highlight model vulnerabilities.

    • Main Results:

    • The study demonstrates that standard statistical models offer no guaranteed protection against carry-over effects when they are present to an appreciable degree.
    • The example of a non-linear dose-response in a Williams square design highlights the potential for significant bias.
    • The effectiveness of usual statistical models is shown to be compromised under conditions of substantial carry-over.

    Conclusions:

    • The most defensible assumption in cross-over trials is that no important carry-over effects have occurred.
    • When the assumption of no significant carry-over cannot be reasonably defended, statistical models do not provide a satisfactory alternative.
    • Researchers should prioritize designs and protocols that minimize or eliminate carry-over effects rather than relying solely on statistical adjustments.