Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Biological Clocks and Seasonal Responses

Biological Clocks and Seasonal Responses

The circadian—or biological—clock is an intrinsic, timekeeping, molecular mechanism that allows plants to coordinate physiological activities over 24-hour cycles called circadian rhythms. Photoperiodism is a collective term for the biological responses of plants to variations in the relative lengths of dark and light periods. The period of light-exposure is called the photoperiod.

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...

Introduction To Survival Analysis

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...

Assumptions of Survival Analysis

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.

Exponential Equations for Modeling Growth

Exponential Equations for Modeling Growth

Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is the relative...

Modeling with Differential Equations

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

The peroxisome proliferator activated receptor-γ pioglitazone improves vascular function and decreases disease activity in patients with rheumatoid arthritis.

Journal of the American Heart Association·2013

Same author

Contrast material-induced nephrotoxicity and intravenous low-osmolality iodinated contrast material: risk stratification by using estimated glomerular filtration rate.

Radiology·2013

Same author

Suboptimal cardiovascular risk factor identification and management in patients with rheumatoid arthritis: a cohort analysis.

Arthritis research & therapy·2012

Same author

Self-expanding metal stents (SEMS) provide superior outcomes compared to plastic stents for pancreatic cancer patients undergoing neoadjuvant therapy.

Journal of gastrointestinal oncology·2012

Same author

Determining the preferred modality for real-time biofeedback during balance training.

Gait & posture·2012

Same author

Proximal thoracic aortic diameter measurements at CT: repeatability and reproducibility according to measurement method.

The international journal of cardiovascular imaging·2012

Search research articles

Related Experiment Video

Updated: Jul 19, 2026

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

Modeling biological rhythms in failure time data.

Naser B Elkum¹, James D Myles

¹Department of Biostatistics, Epidemiology, and Scientific Computing, King Faisal Specialist Hospital & Research Center, Riyadh 11211, Saudi Arabia. nkum@kfshrc.edu.sa

Journal of Circadian Rhythms

|November 9, 2006

Summary

This study introduces a new statistical method to analyze biological rhythms in failure time data, finding day 8 of a 28-day cycle is optimal for breast cancer surgery, reducing recurrence risk.

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

A Computational Method to Quantify Fly Circadian Activity

A Computational Method to Quantify Fly Circadian Activity

Published on: October 28, 2017

Related Experiment Videos

Last Updated: Jul 19, 2026

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

A Computational Method to Quantify Fly Circadian Activity

A Computational Method to Quantify Fly Circadian Activity

Published on: October 28, 2017

Area of Science:

Biostatistics
Chronobiology
Oncology

Background:

Biological systems exhibit various rhythms, including daily, seasonal, and menstrual cycles.
Traditional sine/cosine models are unsuitable for analyzing biological rhythms in failure time data.
Failure time data analysis requires methods that can accommodate time-to-event outcomes and underlying cyclical patterns.

Purpose of the Study:

To adapt the cosinor method for proportional hazards models to analyze biological rhythms in failure time data.
To develop a method for estimating the time of minimum hazard within a biological cycle.
To assess the optimal timing for breast cancer surgery within the menstrual cycle to minimize recurrence.

Main Methods:

Adapted the cosinor method to the proportional hazards model.
Developed an estimation and confidence interval method for the time of minimum hazard.
Applied the model to clinical trial data of pre-menopausal breast cancer patients.

Main Results:

The adapted model identified day 8 (95% CI: 4-12 days) of a 28-day cycle as the optimal time for pre-resection biopsy to minimize recurrence.
Older age, fewer positive lymph nodes, smaller tumor size, and experimental treatment were associated with longer relapse-free survival.
The cosinor-proportional hazards model effectively handles right-censored data in survival analysis.

Conclusions:

A novel statistical method was developed for modeling failure time data with underlying biological rhythms.
The cosinor-proportional hazards model is advantageous for analyzing right-censored data with cyclical patterns.
This method is applicable beyond breast cancer, extending to any biological rhythm analysis with right-censored data.