Analyzing biological rhythms in clinical trials

Naser Elkum, James D. Myles, Pranesh Kumar

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Background: The human body exhibits a variety of biological rhythms. There are patterns that correspond, among others, to the daily wake / sleep cycle, a yearly seasonal cycle and, in women, the menstrual cycle. Sine/cosine functions are often used to model biological patterns for continuous data, but this model is not appropriate for analysis of biological rhythms in failure time data. Methods: We consider a method appropriate for analysis of biological rhythms in clinical trials. We present a method to provide an estimate and confidence interval of the time when the minimum hazard is achieved. A motivating example from a clinical trial of adjuvant of pre-menopausal breast cancer patients provides an important illustration of the methodology in practice. Results: Adapting the Cosinor method to the Weibull proportional hazards model is proposed as useful way of modeling the biological rhythm data. It presents a method to estimate the time that achieves the minimum hazard along with its associated confidence interval. The application of this technique to the breast cancer data revealed that the optimal day for pre-resection incisional or excisional biopsy of 28-day cycle (i.e. the day associated with the lowest recurrence rate) is day 8 with 95% CI 5-10. We found that older age, fewer positive nodes, smaller tumor size, and experimental treatment are important prognostic factors of longer relapse-free survival. Conclusions: The analysis of biological/circadian rhythms is usually handled by Cosinor rhythmometry method. However, in FTD this is simply not possible. In this case, we propose to adapt the Cosinor method to the Weibull proportional hazard model. The advantage of the proposed method is its ability to model survival data. This method is not limited to breast cancer data, and may be applied to any biological rhythms linked to right censored data.

Original languageEnglish
Pages (from-to)720-726
Number of pages7
JournalContemporary Clinical Trials
Volume29
Issue number5
DOIs
Publication statusPublished - Sep 2008
Externally publishedYes

Fingerprint

Periodicity
Clinical Trials
Breast Neoplasms
Proportional Hazards Models
Confidence Intervals
Recurrence
Biological Models
Survival
Menstrual Cycle
Circadian Rhythm
Human Body
Sleep
Biopsy

Keywords

  • Biological rhythms
  • Breast cancer
  • Cosinor rhythmometry
  • Cox model
  • Failure time data
  • Menstrual cycle
  • Weibull distribution

ASJC Scopus subject areas

  • Pharmacology

Cite this

Analyzing biological rhythms in clinical trials. / Elkum, Naser; Myles, James D.; Kumar, Pranesh.

In: Contemporary Clinical Trials, Vol. 29, No. 5, 09.2008, p. 720-726.

Research output: Contribution to journalArticle

Elkum, Naser ; Myles, James D. ; Kumar, Pranesh. / Analyzing biological rhythms in clinical trials. In: Contemporary Clinical Trials. 2008 ; Vol. 29, No. 5. pp. 720-726.
@article{91f3f9d71d7f4f0dbe8c538fb8c22e8e,
title = "Analyzing biological rhythms in clinical trials",
abstract = "Background: The human body exhibits a variety of biological rhythms. There are patterns that correspond, among others, to the daily wake / sleep cycle, a yearly seasonal cycle and, in women, the menstrual cycle. Sine/cosine functions are often used to model biological patterns for continuous data, but this model is not appropriate for analysis of biological rhythms in failure time data. Methods: We consider a method appropriate for analysis of biological rhythms in clinical trials. We present a method to provide an estimate and confidence interval of the time when the minimum hazard is achieved. A motivating example from a clinical trial of adjuvant of pre-menopausal breast cancer patients provides an important illustration of the methodology in practice. Results: Adapting the Cosinor method to the Weibull proportional hazards model is proposed as useful way of modeling the biological rhythm data. It presents a method to estimate the time that achieves the minimum hazard along with its associated confidence interval. The application of this technique to the breast cancer data revealed that the optimal day for pre-resection incisional or excisional biopsy of 28-day cycle (i.e. the day associated with the lowest recurrence rate) is day 8 with 95{\%} CI 5-10. We found that older age, fewer positive nodes, smaller tumor size, and experimental treatment are important prognostic factors of longer relapse-free survival. Conclusions: The analysis of biological/circadian rhythms is usually handled by Cosinor rhythmometry method. However, in FTD this is simply not possible. In this case, we propose to adapt the Cosinor method to the Weibull proportional hazard model. The advantage of the proposed method is its ability to model survival data. This method is not limited to breast cancer data, and may be applied to any biological rhythms linked to right censored data.",
keywords = "Biological rhythms, Breast cancer, Cosinor rhythmometry, Cox model, Failure time data, Menstrual cycle, Weibull distribution",
author = "Naser Elkum and Myles, {James D.} and Pranesh Kumar",
year = "2008",
month = "9",
doi = "10.1016/j.cct.2008.05.001",
language = "English",
volume = "29",
pages = "720--726",
journal = "Contemporary Clinical Trials",
issn = "1551-7144",
publisher = "Elsevier Inc.",
number = "5",

}

TY - JOUR

T1 - Analyzing biological rhythms in clinical trials

AU - Elkum, Naser

AU - Myles, James D.

AU - Kumar, Pranesh

PY - 2008/9

Y1 - 2008/9

N2 - Background: The human body exhibits a variety of biological rhythms. There are patterns that correspond, among others, to the daily wake / sleep cycle, a yearly seasonal cycle and, in women, the menstrual cycle. Sine/cosine functions are often used to model biological patterns for continuous data, but this model is not appropriate for analysis of biological rhythms in failure time data. Methods: We consider a method appropriate for analysis of biological rhythms in clinical trials. We present a method to provide an estimate and confidence interval of the time when the minimum hazard is achieved. A motivating example from a clinical trial of adjuvant of pre-menopausal breast cancer patients provides an important illustration of the methodology in practice. Results: Adapting the Cosinor method to the Weibull proportional hazards model is proposed as useful way of modeling the biological rhythm data. It presents a method to estimate the time that achieves the minimum hazard along with its associated confidence interval. The application of this technique to the breast cancer data revealed that the optimal day for pre-resection incisional or excisional biopsy of 28-day cycle (i.e. the day associated with the lowest recurrence rate) is day 8 with 95% CI 5-10. We found that older age, fewer positive nodes, smaller tumor size, and experimental treatment are important prognostic factors of longer relapse-free survival. Conclusions: The analysis of biological/circadian rhythms is usually handled by Cosinor rhythmometry method. However, in FTD this is simply not possible. In this case, we propose to adapt the Cosinor method to the Weibull proportional hazard model. The advantage of the proposed method is its ability to model survival data. This method is not limited to breast cancer data, and may be applied to any biological rhythms linked to right censored data.

AB - Background: The human body exhibits a variety of biological rhythms. There are patterns that correspond, among others, to the daily wake / sleep cycle, a yearly seasonal cycle and, in women, the menstrual cycle. Sine/cosine functions are often used to model biological patterns for continuous data, but this model is not appropriate for analysis of biological rhythms in failure time data. Methods: We consider a method appropriate for analysis of biological rhythms in clinical trials. We present a method to provide an estimate and confidence interval of the time when the minimum hazard is achieved. A motivating example from a clinical trial of adjuvant of pre-menopausal breast cancer patients provides an important illustration of the methodology in practice. Results: Adapting the Cosinor method to the Weibull proportional hazards model is proposed as useful way of modeling the biological rhythm data. It presents a method to estimate the time that achieves the minimum hazard along with its associated confidence interval. The application of this technique to the breast cancer data revealed that the optimal day for pre-resection incisional or excisional biopsy of 28-day cycle (i.e. the day associated with the lowest recurrence rate) is day 8 with 95% CI 5-10. We found that older age, fewer positive nodes, smaller tumor size, and experimental treatment are important prognostic factors of longer relapse-free survival. Conclusions: The analysis of biological/circadian rhythms is usually handled by Cosinor rhythmometry method. However, in FTD this is simply not possible. In this case, we propose to adapt the Cosinor method to the Weibull proportional hazard model. The advantage of the proposed method is its ability to model survival data. This method is not limited to breast cancer data, and may be applied to any biological rhythms linked to right censored data.

KW - Biological rhythms

KW - Breast cancer

KW - Cosinor rhythmometry

KW - Cox model

KW - Failure time data

KW - Menstrual cycle

KW - Weibull distribution

UR - http://www.scopus.com/inward/record.url?scp=49749123009&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=49749123009&partnerID=8YFLogxK

U2 - 10.1016/j.cct.2008.05.001

DO - 10.1016/j.cct.2008.05.001

M3 - Article

VL - 29

SP - 720

EP - 726

JO - Contemporary Clinical Trials

JF - Contemporary Clinical Trials

SN - 1551-7144

IS - 5

ER -