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

1.3K
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...
1.3K
Cancer Survival Analysis01:21

Cancer Survival Analysis

843
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
843
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

1.2K
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
1.2K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

979
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...
979
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

490
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.
490
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.9K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.9K

You might also read

Related Articles

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

Sort by
Same author

Perspectives on the definition of 'evidence': a qualitative interview study among interest-holders.

BMJ evidence-based medicine·2026
Same author

Which randomized controlled trial do we need? Routine replacement versus clinically indicated replacement for peripheral venous catheters for reducing bloodstream infections: a cluster-randomized controlled trial.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases·2026
Same author

Be Wary of Low-Novelty Research Based on GBD Data: Regulating Public Database Utilization via Prospective Registration.

Journal of evidence-based medicine·2026
Same author

Reporting Interest-Holder Engagement in Practice Guidelines: The RIGHT-MuSE Checklist.

Annals of internal medicine·2026
Same author

Preparing the Update of the Reporting Items for Practice Guidelines in HealThcare (RIGHT) Statement: Analysis of Comments and Suggestions From the Scientific Literature.

Journal of evidence-based medicine·2026
Same author

Large language models for systematic reviews were reported to perform well but rarely with verifiable safeguards: a cross-sectional study.

Journal of clinical epidemiology·2026

Related Experiment Video

Updated: Apr 10, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

3.1K

gems: An R Package for Simulating from Disease Progression Models.

Nello Blaser1, Luisa Salazar Vizcaya1, Janne Estill1

  • 1Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.

Journal of Statistical Software
|June 12, 2015
PubMed
Summary

The R package gems simulates disease progression using generalized multistate models. This tool aids in predicting health intervention outcomes and disease progression by modeling events like diagnosis and death.

Keywords:
Monte Carlo simulationcompartmental modelmultistate modelpredictionsurvival analysis𝖱

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.8K
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

11.1K

Related Experiment Videos

Last Updated: Apr 10, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

3.1K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.8K
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

11.1K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Computational Biology

Background:

  • Mathematical models are crucial for predicting disease progression and evaluating health interventions.
  • Existing tools may have limitations in flexibility and handling complex disease pathways.

Purpose of the Study:

  • To introduce the R package gems, a novel tool for simulating disease progression and intervention effects.
  • To provide a flexible framework for generalized multistate modeling in R.

Main Methods:

  • The gems package utilizes directed acyclic graphs to represent disease states and events.
  • It employs generalized multistate models with user-defined, continuous transition-specific hazard functions.
  • The model accommodates parameter uncertainty and can incorporate event history, moving beyond standard Markov assumptions.

Main Results:

  • gems enables the simulation of disease progression based on specified hazard functions and model structures.
  • The package allows for the prediction of patient outcomes under various intervention scenarios.
  • Demonstrates flexibility in handling complex, non-Markovian disease processes.

Conclusions:

  • The gems R package offers a powerful and flexible tool for simulating disease progression and evaluating health interventions.
  • Its capabilities extend beyond medical applications to any field requiring multistate simulation.
  • Facilitates robust epidemiological analysis and planning through advanced modeling techniques.