Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

542
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...
542
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

174
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.
174
Survival Curves01:18

Survival Curves

253
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
253
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

342
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...
342
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

249
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
249
Survival Tree01:19

Survival Tree

132
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
132

You might also read

Related Articles

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

Sort by
Same author

A Simulation and Case Study to Evaluate the Extrapolation Performance of Flexible Bayesian Survival Models when Incorporating Real-World Data.

Medical decision making : an international journal of the Society for Medical Decision Making·2026
Same author

Clinical and Psychological Outcomes After Monoclonal Gammopathy Screening: A Population-Based Screening Study and Subsequent Randomized Trial of Follow-Up.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same author

CD8<sup>+</sup>CD38<sup>+</sup> T cells identify functional high-risk multiple myeloma after autologous transplant: BMT CTN 0702 correlates.

Blood neoplasia·2026
Same author

Genomic features do not account for differences in multiple myeloma risk by ancestry.

Blood cancer discovery·2026
Same author

Monoclonal gammopathy of undetermined significance is not associated with increased risk of skin cancer.

The British journal of dermatology·2026
Same author

Incorporating the Next Generation of Immunotherapies Into the Treatment of Multiple Myeloma.

Journal of the National Comprehensive Cancer Network : JNCCN·2026

Related Experiment Video

Updated: Aug 22, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

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

Published on: December 9, 2015

10.7K

Modelling multiple time-scales with flexible parametric survival models.

Nurgul Batyrbekova1,2, Hannah Bower3, Paul W Dickman4

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden. nurgul.batyrbekova@ki.se.

BMC Medical Research Methodology
|November 10, 2022
PubMed
Summary

Flexible parametric survival models offer an efficient alternative for analyzing multiple time-scales in survival analysis. This approach avoids data splitting, reducing computational load and potential errors for hazard rate and survival proportion modeling.

Keywords:
Cohort studiesEpidemiological methodsFlexible parametric survival modelsMatched cohortMultiple time-scalesTime-varying covariate

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K
Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

8.5K

Related Experiment Videos

Last Updated: Aug 22, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

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

Published on: December 9, 2015

10.7K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.3K
Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

8.5K

Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Modeling multiple time-scales in survival analysis often requires time-splitting datasets, leading to computational challenges.
  • Traditional Cox or Poisson models on time-split data can be computationally intensive, especially for continuous hazard rate or survival changes.
  • Existing methods face limitations with large datasets and potential for errors due to data segmentation.

Purpose of the Study:

  • To introduce flexible parametric survival models as an alternative for analyzing multiple time-scales.
  • To demonstrate a method that avoids time-splitting of data in survival analysis.
  • To provide a computationally efficient approach for modeling complex time-scale interactions.

Main Methods:

  • Utilized flexible parametric survival models on the log hazard scale.
  • Re-parameterized multiple time-scales as a function of a reference time-scale to avoid data splitting.
  • Applied the method to case studies for demonstration and graphical representation.

Main Results:

  • Flexible parametric models provided nearly identical results to traditional Poisson models.
  • The proposed method successfully avoided the need for time-splitting datasets.
  • Graphical representations of estimated hazard rates and survival proportions were effectively generated.

Conclusions:

  • Flexible parametric survival models are a powerful and efficient tool for multiple time-scale survival analysis.
  • This method eliminates the need for data time-splitting, saving computational time and reducing errors.
  • The approach overcomes technological limitations associated with large, segmented datasets.