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

Interference and Diffraction02:18

Interference and Diffraction

51.9K
Interference is a characteristic phenomenon exhibited by waves. When two electromagnetic waves interact with their peaks and troughs coinciding, a resulting wave with enhanced amplitude is produced. This is known as constructive interference. In this case, the two waves interacting are in phase with each other.
51.9K
RNA Interference01:23

RNA Interference

27.9K
RNA interference (RNAi) is a process in which a small non-coding RNA molecule blocks the post-transcriptional expression of a gene by binding to its messenger RNA (mRNA) and preventing the protein from being translated.
This process occurs naturally in cells, often through the activity of genomically-encoded microRNAs. Researchers can take advantage of this mechanism by introducing synthetic RNAs to deactivate specific genes for research or therapeutic purposes. For example, RNAi could be used...
27.9K
Decision Making01:20

Decision Making

926
Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
926
Interference and Decay01:16

Interference and Decay

433
Forgetting is a complex cognitive phenomenon influenced by several factors, among which interference and decay are particularly prominent. These processes explain why individuals often struggle to retrieve specific information from memory, leading to lapses in recall that can be observed in everyday situations.
Interference occurs when competing memories hinder the retrieval of particular information. It can be classified into two types: proactive and retroactive interference. Proactive...
433
What are Estimates?01:06

What are Estimates?

8.2K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
8.2K
Decision Making: P-value Method01:09

Decision Making: P-value Method

6.8K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
6.8K

You might also read

Related Articles

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

Sort by
Same author

Identification of a master regulator Msd1 that governs meiotic entry in a global basidiomycete pathogen.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Photothermal Amplification via Nanorobotic Swarming Dynamics.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Mechanism of Structure and Property Evolution of ABS During Multiple Extrusion and Aging Degree Prediction via Image Recognition Technology.

Polymers·2026
Same author

Antifungal and immunomodulatory activities of dihydroartemisinin-loaded chitosan nanoparticles against fluconazole‑resistant Candida tropicalis.

International journal of antimicrobial agents·2026
Same author

Multicentre prospective cohort study to develop and validate a machine learning-based model for predicting 6-month all-cause mortality in elderly patients with advanced chronic obstructive pulmonary disease in China: study protocol.

BMJ open·2026
Same author

Ultrasound-sensitive microrobotic sensor with robust anchoring for long-term digestive lesion assessment.

National science review·2026
Same journal

On the Connections Among Three Transfer Learning Paradigms.

Stat (International Statistical Institute)·2025
Same journal

Accelerating Resident Research within Quantitative Collaboration Units in Academic Healthcare.

Stat (International Statistical Institute)·2025
Same journal

Multivariate differential association analysis.

Stat (International Statistical Institute)·2024
Same journal

Developing partnerships for academic data science consulting and collaboration units.

Stat (International Statistical Institute)·2024
Same journal

Deep learning models to predict primary open-angle glaucoma.

Stat (International Statistical Institute)·2024
Same journal

What is it that you say you do here? Advocating for the critical role of data scientists in research infrastructure.

Stat (International Statistical Institute)·2024
See all related articles

Related Experiment Video

Updated: Jan 23, 2026

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management
05:35

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

1.3K

Modelling and estimation for optimal treatment decision with interference.

Lin Su1, Wenbin Lu1, Rui Song1

  • 1Department of Statistics, North Carolina State University, Raleigh, North Carolina.

Stat (International Statistical Institute)
|June 11, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new network regression model to determine optimal individualized treatments, even with network interference. The derived optimal treatment strategy is independent of interference, enhancing practical application in network studies.

Keywords:
A-learningQ-learninginterferencenetworkoptimal treatment regimen

More Related Videos

Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
09:21

Optimized Management of Endovascular Treatment for Acute Ischemic Stroke

Published on: January 18, 2018

12.5K
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

14.1K

Related Experiment Videos

Last Updated: Jan 23, 2026

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management
05:35

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

1.3K
Optimized Management of Endovascular Treatment for Acute Ischemic Stroke
09:21

Optimized Management of Endovascular Treatment for Acute Ischemic Stroke

Published on: January 18, 2018

12.5K
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

14.1K

Area of Science:

  • Network analysis
  • Statistical modeling
  • Causal inference

Background:

  • Network-based interventions can cause interference, where one person's treatment affects others.
  • Optimal individualized treatment strategies are unknown when network interference is present.

Purpose of the Study:

  • To propose a novel network-based regression model accounting for outcome-treatment interactions within a network.
  • To develop Q-learning and A-learning methods for deriving optimal treatment regimens under network interference.

Main Methods:

  • Developed a network-based regression model to capture treatment-outcome interactions.
  • Derived Q-learning and A-learning algorithms for treatment optimization.
  • Established asymptotic properties for the proposed estimators.

Main Results:

  • The proposed model effectively accounts for interference in network data.
  • Derived optimal treatment regimens are independent of network interference.
  • Simulation studies and real-world data application demonstrate model performance.

Conclusions:

  • The novel network regression model provides a feasible approach for individualized treatment in the presence of network interference.
  • The independence of the optimal treatment from interference simplifies practical implementation.
  • The methods are validated through simulations and a mobile game network dataset.