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Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

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Published on: August 8, 2019

Analyzing biological rhythms in clinical trials.

Naser B Elkum1, James D Myles, Pranesh Kumar

  • 1King Faisal Specialist Hospital and Research Center, Kingdom of Saudi Arabia.

Contemporary Clinical Trials
|June 24, 2008
PubMed
Summary

This study introduces a novel method to analyze biological rhythms in failure time data, adapting the Cosinor method for cancer clinical trials. The findings suggest optimal timing for biopsies in breast cancer patients, improving survival analysis.

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Area of Science:

  • Biostatistics
  • Chronobiology
  • Oncology

Background:

  • Human biological rhythms, such as daily, seasonal, and menstrual cycles, are well-documented.
  • Traditional sine/cosine models are unsuitable for analyzing biological rhythms in failure time data.
  • Existing methods like Cosinor rhythmometry are not applicable to failure time data.

Purpose of the Study:

  • To develop and present a statistical method for analyzing biological rhythms in clinical trials with failure time data.
  • To estimate the time of minimum hazard and its confidence interval for biological rhythm analysis.
  • To illustrate the methodology using a clinical trial of pre-menopausal breast cancer patients.

Main Methods:

  • Adaptation of the Cosinor method to the Weibull proportional hazards model for survival data.
  • Estimation of the time of minimum hazard and its associated confidence interval.
  • Application to a clinical trial dataset of adjuvant therapy in breast cancer patients.

Main Results:

  • The adapted Cosinor method effectively models biological rhythm data within a Weibull proportional hazards framework.
  • Optimal timing for pre-resection biopsy in a 28-day cycle for breast cancer patients was identified as day 8 (95% CI: 5-10), associated with the lowest recurrence rate.
  • Key prognostic factors for longer relapse-free survival include older age, fewer positive lymph nodes, smaller tumor size, and experimental treatment.

Conclusions:

  • The proposed adaptation of the Cosinor method to the Weibull proportional hazard model is a viable approach for analyzing biological rhythms in failure time data.
  • This method overcomes limitations of traditional approaches for survival data analysis.
  • The methodology is broadly applicable to any biological rhythms associated with right-censored data, extending beyond breast cancer studies.