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Related Experiment Videos

Sample size calculations for the two-sample problem using the multiplicative intensity model.

M V Bernardo1, D P Harrington

  • 1Department of Biostatistics, Harvard School of Public Health, USA. bernardo@hsph.harvard.edu

Statistics in Medicine
|February 27, 2001
PubMed
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This study introduces formulas for calculating expected events or trial duration in two-group clinical trials with multiple events, using multiplicative intensity (MI) models. The methods are validated with real-world data for improved clinical trial design and analysis.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Epidemiology

Background:

  • Clinical trials often involve patients experiencing multiple events.
  • Accurate sample size and duration calculations are crucial for efficient trial design.
  • Multiplicative intensity (MI) models are suitable for analyzing recurrent event data.

Purpose of the Study:

  • To propose novel formulae for calculating expected events and required trial duration.
  • To develop methods for clinical trials with multiple events analyzed using MI models.
  • To provide practical tools for biostatisticians and clinical researchers.

Main Methods:

  • Utilized a partial likelihood-based approach for statistical inference.
  • Examined two specific MI models: one with a binary treatment covariate and a three-state Markov model with a time-varying covariate.

Related Experiment Videos

  • Derived formulae for expected number of events and trial duration.

    • Main Results:

    • The derived formulae provide a method for sample size and duration calculations in recurrent event trials.
    • For a simpler model, the proposed formulae align with results from full likelihood methods.
    • Demonstrated the application of the formulae using chronic granulomatous disease and breast cancer data.

    Conclusions:

    • The proposed formulae offer a robust approach for planning clinical trials with recurrent events.
    • The methods are applicable to various clinical settings, enhancing trial efficiency.
    • The study contributes valuable tools for the statistical analysis of complex clinical trial data.