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

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

126
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
126
Longitudinal Research02:20

Longitudinal Research

12.0K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
12.0K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

126
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.
126
Longitudinal Studies01:26

Longitudinal Studies

160
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
160

You might also read

Related Articles

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

Sort by
Same author

Methodological innovations to advance substance use disorder research: Proceedings of a NIDA workshop on target trial emulation and translational testing of digital health tools.

Journal of substance use and addiction treatment·2026
Same author

The effect of selective decontamination on antimicrobial resistance in intensive care patients: a systematic review and meta-analysis.

Scientific reports·2026
Same author

Psychometric evaluation and community norms of the Somatic Symptom Scale-8 based on a representative German sample.

Frontiers in psychiatry·2026
Same author

Dispensing of benzodiazepines and benzodiazepine-related drugs in Estonia, Latvia and Lithuania: a cross-national drug utilisation study.

Nordic journal of psychiatry·2026
Same author

Available guidance for ethical challenges in learning health systems: an integrative literature review.

Health research policy and systems·2026
Same author

How to design a ROCI (Response Over Continuous Intervention) randomised trial: guidance and a case study.

BMC medical research methodology·2026

Related Experiment Video

Updated: Jul 1, 2025

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan
11:58

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Published on: June 29, 2018

9.4K

It's time! Ten reasons to start replicating simulation studies.

Anna Lohmann1, Oscar L O Astivia2, Tim P Morris3

  • 1Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, Netherlands.

Frontiers in Epidemiology
|March 8, 2024
PubMed
Summary
This summary is machine-generated.

Replication of simulation studies is crucial for ensuring reliable data analysis. Ensuring the reproducibility of these simulations is essential for sound statistical decision-making in research.

Keywords:
data analysisreplicationreproductionresearch statisticssimulation study

More Related Videos

An Open Source Technology Platform to Manufacture Hydrogel-Based 3D Culture Models in an Automated and Standardized Fashion
08:29

An Open Source Technology Platform to Manufacture Hydrogel-Based 3D Culture Models in an Automated and Standardized Fashion

Published on: March 31, 2022

4.5K
Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.5K

Related Experiment Videos

Last Updated: Jul 1, 2025

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan
11:58

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Published on: June 29, 2018

9.4K
An Open Source Technology Platform to Manufacture Hydrogel-Based 3D Culture Models in an Automated and Standardized Fashion
08:29

An Open Source Technology Platform to Manufacture Hydrogel-Based 3D Culture Models in an Automated and Standardized Fashion

Published on: March 31, 2022

4.5K
Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

4.5K

Area of Science:

  • Quantitative Methodology
  • Statistical Computing
  • Empirical Research

Background:

  • Quantitative analysis is fundamental to empirical research.
  • Computer simulations evaluate statistical method performance.
  • Simulation studies significantly impact subsequent empirical analyses.

Purpose of the Study:

  • Advocate for the replication of simulation studies.
  • Emphasize the responsibility accompanying the power of simulation studies.
  • Highlight the importance of replication for robust data analysis.

Main Methods:

  • The study argues for the necessity of replication based on the impact of simulation studies.
  • It draws parallels between simulation studies and other experimental research.
  • The core method is a conceptual argument for increased attention to replication.

Main Results:

  • Simulation studies, like other empirical research, are susceptible to human error and require replication.
  • Replication ensures the integrity of simulation-based findings.
  • The potential for replication in simulation studies is an underutilized opportunity.

Conclusions:

  • Replication of simulation studies is a critical responsibility.
  • Quantitative methodology should prioritize the replicability of simulation studies.
  • Ensuring reproducibility strengthens the foundation for data analytical decisions.