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

Introduction to Epidemiology01:26

Introduction to Epidemiology

568
Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
568
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

250
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:
250
Actuarial Approach01:20

Actuarial Approach

49
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
49
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

97
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
97
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

134
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...
134
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

62
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
62

You might also read

Related Articles

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

Sort by
Same author

Comparative impacts and cost-effectiveness of tuberculosis systematic screening strategies in prisons in Brazil, Colombia, and Peru: A mathematical modeling study.

PLoS medicine·2026
Same author

Potential Paths Forward from "On Representations and Quantifications of Uncertainty".

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

Cost-effectiveness of fecal immunochemical testing for colorectal cancer in Mexico City: A microsimulation modeling study.

Journal of medical screening·2026
Same author

A Tutorial on Discrete Event Simulation Models Using a Cost-Effectiveness Analysis Example in R.

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

A Microsimulation-Based Approach for Mitigating Societal Bias in Chronic Kidney Disease Data.

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

PRE-CISE: A PRE-calibration Coverage, Identifiability, and SEnsitivity analysis workflow to streamline model calibration.

medRxiv : the preprint server for health sciences·2026
Same journal

From Chaos to Care: Personalized AI for Early Cardiac Arrhythmia Warning.

medRxiv : the preprint server for health sciences·2026
Same journal

Large distant deletion disrupts CDKN2A enhancer and predisposes to melanoma.

medRxiv : the preprint server for health sciences·2026
Same journal

Artificial Intelligence-Based Chatbots in Genetic Counseling Practice: Current Uptake, Utilization, and Perspectives.

medRxiv : the preprint server for health sciences·2026
Same journal

Longitudinal MAP-MRI-based Assessment of Tissue Microstructural Alterations in Acute mTBI.

medRxiv : the preprint server for health sciences·2026
Same journal

A class of deep intronic <i>IGHMBP2</i> variants activate a shared cryptic splice donor, enabling correction of select variants with a single antisense oligonucleotide.

medRxiv : the preprint server for health sciences·2026
Same journal

Global Socioeconomic Context and Brain Ageing in Epilepsy: an ENIGMA-Epilepsy study.

medRxiv : the preprint server for health sciences·2026
See all related articles

Related Experiment Video

Updated: May 16, 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

10.6K

Calculating epidemiological outcomes from simulated longitudinal data.

Selina Pi1, Jeremy D Goldhaber-Fiebert2, Fernando Alarid-Escudero2

  • 1Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.

Medrxiv : the Preprint Server for Health Sciences
|May 9, 2025
PubMed
Summary
This summary is machine-generated.

Microsimulation models can now calculate epidemiological outcomes from long-term individual data. This report offers methods and R code for accurate population-level health metrics, enhancing model validation.

Keywords:
epidemiologyincidencemicrosimulationprevalence

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

2.0K
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

10.0K

Related Experiment Videos

Last Updated: May 16, 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

10.6K
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

2.0K
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

10.0K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Computational Biology

Background:

  • Microsimulation models generate individual life trajectories for population-level analysis.
  • Calculating epidemiological outcomes from long-term longitudinal data is challenging due to data rarity.
  • Existing formulas are limited for long-term studies covering the human lifespan.

Purpose of the Study:

  • To present methods for calculating epidemiological outcomes from simulated longitudinal data.
  • To provide open-source R code for calculating various population-level health metrics.
  • To enhance transparency in reporting microsimulation model outcomes.

Main Methods:

  • Developed methods to calculate prevalence, incidence, and lifetime risk from simulated longitudinal data.
  • Incorporated longitudinal disease and exposure durations into outcome calculations.
  • Created an open-source R codebase for calculating epidemiological and cancer-related outcomes.

Main Results:

  • Provided functions to calculate prevalence, incidence, age-conditional risk, lifetime risk, and disease-specific mortality.
  • Offered guidance and code for cancer-specific outcomes like stage distribution and lesion multiplicity.
  • Demonstrated methods for calculating mean dwell and sojourn times.

Conclusions:

  • The report facilitates the calculation of epidemiological outcomes from both simulated and real-world longitudinal data.
  • Increased transparency in reporting outcome derivations from microsimulation models is crucial.
  • The provided R code and methods support robust model calibration and validation.