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

Decision Making: P-value Method01:09

Decision Making: P-value Method

5.8K
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...
5.8K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.3K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.3K
Decision Making01:20

Decision Making

1.1K
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...
1.1K
Survival Tree01:19

Survival Tree

496
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
496
Reason and Intuition01:37

Reason and Intuition

5.9K
The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
5.9K
Cancer Survival Analysis01:21

Cancer Survival Analysis

855
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...
855

You might also read

Related Articles

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

Sort by
Same author

Statin Use and Liver Cancer Risk: A Meta-Epidemiological Study of Retrospective Cohort Studies by the Types of Constructed Cohort.

Asian Pacific journal of cancer prevention : APJCP·2024
Same author

Trends of legionellosis reported in Jeju Province, Republic of Korea, 2015-2022.

Osong public health and research perspectives·2023
Same author

Epidemiological investigation of a food-borne outbreak in a kindergarten, Jeju Province, Korea.

Epidemiology and health·2023
Same author

Statin Intake and Gastric Cancer Risk: An Updated Subgroup Meta-analysis Considering Immortal Time Bias.

Journal of preventive medicine and public health = Yebang Uihakhoe chi·2022
Same author

Diabetes History and Gastric Cancer Risk: Different Results by Types of Follow-Up Studies.

Asian Pacific journal of cancer prevention : APJCP·2022
Same author

Circulating 25-hydroxyvitamin D levels and hypertension risk after adjusting for publication bias.

Clinical hypertension·2022
Same journal

Inequalities in facility-based delivery in Indonesia: evidence from the 2023 Indonesian Health Survey based on Andersen's health services utilization framework.

Epidemiology and health·2026
Same journal

Korea Youth Risk Behavior Survey: achievements in the last 20 years and future directions.

Epidemiology and health·2026
Same journal

Beyond cohort versus case-control: the primacy of exposure assessment over study design in occupational cancer epidemiology.

Epidemiology and health·2026
Same journal

Spatiotemporal analysis of sudden infant death syndrome incidence in South Korea, 2013-2023.

Epidemiology and health·2026
Same journal

Cohort profile: a multicenter hospital-based cohort of tuberculosis and nontuberculous mycobacterial pulmonary disease in Korea, 2015-2024.

Epidemiology and health·2026
Same journal

The impact of health behavior trajectories on all-cause and cause-specific mortality: an analysis using the National Health Insurance Service-Health Screening Cohort (NHIS-HEALS).

Epidemiology and health·2026
See all related articles

Related Experiment Video

Updated: Apr 21, 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

7.0K

The clinical decision analysis using decision tree.

Jong-Myon Bae1

  • 1Department of Preventive Medicine, Jeju National University School of Medicine, Jeju, Korea.

Epidemiology and Health
|November 1, 2014
PubMed
Summary
This summary is machine-generated.

Clinical Decision Analysis (CDA) aids objective medical decision-making by applying evidence-based medicine. Reviewing CDA

Keywords:
Decision analysisDecision support techniquesDecision-makingEvidence-base medicine

More Related Videos

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

5.2K

Related Experiment Videos

Last Updated: Apr 21, 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

7.0K
Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

5.2K

Area of Science:

  • Medical Decision Making
  • Health Informatics
  • Evidence-Based Practice

Background:

  • Clinical decision analysis (CDA) addresses complexity and uncertainty in medical problem-solving.
  • CDA provides a structured framework for objective clinical decision-making.
  • Evidence-based medicine principles are integral to CDA.

Purpose of the Study:

  • To review the utility and limitations of Clinical Decision Analysis.
  • To outline the six key steps involved in conducting CDA.
  • To emphasize the importance of shared decision-making with patient values.

Main Methods:

  • Literature review on Clinical Decision Analysis methodologies.
  • Analysis of the six-step process for conducting CDA.
  • Evaluation of CDA's application in clinical practice.

Main Results:

  • CDA is a valuable tool for navigating complex medical scenarios.
  • Understanding the limitations of CDA is crucial for effective application.
  • The six-step process provides a systematic approach to CDA.

Conclusions:

  • Clinical Decision Analysis enhances objective decision-making in healthcare.
  • Effective application requires awareness of CDA's strengths and weaknesses.
  • Integrating patient values into shared decision-making is essential for optimal outcomes.