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Power considerations for clinical trials using multivariate time-to-event data

M D Hughes1

  • 1Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.

Statistics in Medicine
|April 30, 1997
PubMed
Summary
This summary is machine-generated.

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This study derives approximate power for clinical trials analyzing multiple event times using multivariate proportional hazards models. It highlights how design choices impact trial power for comparing treatments, especially in bivariate analyses.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Survival Analysis

Background:

  • Clinical trials often collect data on multiple event times, such as recurrent events or different event types.
  • Multivariate proportional hazards models are suitable for analyzing such complex event time data.
  • Software for these models is increasingly accessible, facilitating their use in research.

Purpose of the Study:

  • To derive the approximate statistical power of clinical trials that utilize multivariate event time data.
  • To examine the influence of various design parameters on trial power, particularly in the context of comparing two treatments.
  • To discuss conceptual considerations crucial for designing trials with multivariate event time data.

Main Methods:

  • Derivation of approximate power for clinical trials using multivariate proportional hazards models.

Related Experiment Videos

  • Focus on the bivariate case to validate the power approximation.
  • Analysis of how design parameters influence statistical power.

    • Main Results:

    • The derived approximate power formula is applicable to clinical trials with multivariate event time data.
    • The approximation demonstrates good performance in the bivariate case.
    • Key design parameters significantly affect the power of treatment comparisons.

    Conclusions:

    • The study provides a method for estimating the power of clinical trials employing multivariate event time data.
    • Understanding the impact of design choices is essential for optimizing trial efficiency and interpretability.
    • Careful consideration of conceptual issues in trial design is paramount for robust multivariate survival analysis.