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

Mechanical Efficiency of Real Machines01:14

Mechanical Efficiency of Real Machines

1.3K
The mechanical efficiency of a machine is a fundamental concept that describes how effectively a machine can convert input work into output work. According to this concept, the efficiency of a machine is equal to the ratio of the output work to the input work. An ideal machine, meaning a machine that has no energy losses, has an efficiency of one. This implies that the input work and the output work are equal.
However, in reality, no machine can be truly ideal, and all of them experience some...
1.3K
The Evidence for Evolution02:55

The Evidence for Evolution

47.7K
Genetic variations accumulating within populations over generations give rise to biological evolution. Evolutionary changes can result in the formation of novel varieties and entire new species. These changes are responsible for the diverse forms of life inhabiting the planet. The evidence for evolution suggests that all living organisms descended from common ancestors.
47.7K
Causality in Epidemiology01:21

Causality in Epidemiology

1.5K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
1.5K
Machines01:19

Machines

563
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
563
Theory of Attribution I: Correspondent Inference Theory01:15

Theory of Attribution I: Correspondent Inference Theory

500
Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...
500
Machines: Problem Solving II01:30

Machines: Problem Solving II

652
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
652

You might also read

Related Articles

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

Sort by
Same author

Research Method, Conduct, and Reporting Considerations for Improving the Quality of Non-Hypothesis-Evaluating Treatment Effectiveness Analyses Using Real-World Data: An ISPOR Special Interest Group Report.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research·2025
Same author

Derivation of an Annualized Claims-Based Major Adverse Cardiovascular Event Estimator in Type 2 Diabetes.

JACC. Advances·2024
Same author

Effectiveness of glucose-lowering medications on cardiovascular outcomes in patients with type 2 diabetes at moderate cardiovascular risk.

Nature cardiovascular research·2024
Same author

American clusters: using machine learning to understand health and health care disparities in the United States.

Health affairs scholar·2024
Same author

Analysis of maternal and child health spillover effects in PEPFAR countries.

BMJ open·2023
Same author

Quality of life burden on United States infants and caregivers due to lower respiratory tract infection and adjusting for selective testing: Pilot prospective observational study.

Health science reports·2023

Related Experiment Video

Updated: Jan 24, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K

Real-World Evidence, Causal Inference, and Machine Learning.

William H Crown1

  • 1OptumLabs,(®) Cambridge, MA, USA.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|May 21, 2019
PubMed
Summary

Real world evidence (RWE) quality is improving due to advances in observational research and data availability. Machine learning offers new tools for analyzing complex health data, though causal inference applications are still developing.

Keywords:
big datacausal inferenceeconometricsepidemiologymachine learningreal-world evidencetargeted maximum likelihood estimator

More Related Videos

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

2.4K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

952

Related Experiment Videos

Last Updated: Jan 24, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.9K
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

2.4K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

952

Area of Science:

  • Health services research
  • Data science
  • Epidemiology

Background:

  • Focus on real world evidence (RWE) driven by converging trends.
  • Advancements in observational research design and methods.
  • Growth of large observational healthcare databases globally.

Purpose of the Study:

  • Examine implications of improved observational methods and data availability on RWE quality.
  • Assess the role of machine learning in analyzing complex health datasets.
  • Evaluate the evolving use of machine learning in causal inference.

Main Methods:

  • Review of advancements in observational research design.
  • Analysis of large observational healthcare databases.
  • Exploration of machine learning applications for complex data analysis.

Main Results:

  • Improvements in observational methods and data availability positively impact RWE quality.
  • Traditional statistical methods struggle with unstructured and sparse data.
  • Machine learning methods show promise for analyzing massive, complex datasets.

Conclusions:

  • Machine learning excels at prediction, valuable for RWE.
  • Causal inference using machine learning is an evolving field.
  • Emerging methods like targeted maximum likelihood integrate machine learning with causal inference.