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

Dose-Response Relationship: Overview01:03

Dose-Response Relationship: Overview

3.2K
Agonists can bind with and activate receptors, resulting in the formation of drug-receptor complexes. Once formed, these complexes catalyze many biochemical processes at the cellular level and subsequently induce a pharmacologic response. The degree of response is directly proportional to the fraction of activated receptors, which in turn, depends on the concentration of the drug at the receptor site as well as the sensitivity of the receptor. An increase in the administered dose contributes to...
3.2K
Relative Risk01:12

Relative Risk

208
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
208
Dose-Response Relationship: Potency and Efficacy01:22

Dose-Response Relationship: Potency and Efficacy

4.5K
The potency of a drug is the measure of its ability to produce a biological response and can be compared by looking at the half-maximum effective concentration or EC50 values of different drugs. A lower EC50 value indicates higher potency of the drug. In the dose–response curve of two antihypertensive drugs, candesartan and irbesartan, a significant difference is observed in their EC50 values. A lower EC50 value for candesartan indicates that it is more potent than irbesartan, as it...
4.5K
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

1.0K
The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
1.0K
Odds Ratio01:09

Odds Ratio

163
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
163
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.4K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Reducing Risk Misinformation and Miscommunication: A Sheaf-Theoretic Perspective.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same author

Necessary conditions for valid causal inference from observational data.

Critical reviews in toxicology·2026
Same author

Integrating Fragmented Risk Knowledge: Sheaf Theory for Risk Analysts.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same author

Combining Diverse Expert Opinions in Risk Analysis Using Relative Causal Knowledge.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same author

Improving the design of epidemiology studies that use biomonitoring for exposure assessment: a SciPinion panel recommendation.

BMC medical research methodology·2026
Same author

Living with risk, then and now: A dual review of Cam Grey's Living with Risk in the Late Roman World and of current AI-assisted book reviewing.

Risk analysis : an official publication of the Society for Risk Analysis·2025

Related Experiment Video

Updated: Jul 18, 2025

Extracellular Multi-Unit Recording from the Olfactory Nerve of Teleosts
07:02

Extracellular Multi-Unit Recording from the Olfactory Nerve of Teleosts

Published on: October 6, 2020

6.8K

What is an exposure-response curve?

Louis Anthony Cox1

  • 1Cox Associates, Entanglement, University of Colorado, United States of America.

Global Epidemiology
|August 28, 2023
PubMed
Summary

This study clarifies ambiguous exposure-response curves using causal AI and machine learning. New methods improve risk assessment by precisely defining population and individual risks from exposure changes.

Keywords:
Accumulated local effects (ALE) plotCausal artificial intelligence (CAI)Exposure-response curveIndividual conditional expectation (ICE) plotPartial dependence plot (PDP)

More Related Videos

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
10:33

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

Published on: September 4, 2017

15.8K
High-throughput Analysis of Mammalian Olfactory Receptors: Measurement of Receptor Activation via Luciferase Activity
12:02

High-throughput Analysis of Mammalian Olfactory Receptors: Measurement of Receptor Activation via Luciferase Activity

Published on: June 2, 2014

12.6K

Related Experiment Videos

Last Updated: Jul 18, 2025

Extracellular Multi-Unit Recording from the Olfactory Nerve of Teleosts
07:02

Extracellular Multi-Unit Recording from the Olfactory Nerve of Teleosts

Published on: October 6, 2020

6.8K
Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation
10:33

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

Published on: September 4, 2017

15.8K
High-throughput Analysis of Mammalian Olfactory Receptors: Measurement of Receptor Activation via Luciferase Activity
12:02

High-throughput Analysis of Mammalian Olfactory Receptors: Measurement of Receptor Activation via Luciferase Activity

Published on: June 2, 2014

12.6K

Area of Science:

  • Quantitative Health Risk Assessment
  • Epidemiology
  • Causal Inference
  • Machine Learning

Background:

  • Exposure-response curves are fundamental in health risk assessment but often lack clear interpretation.
  • Ambiguity hinders accurate prediction of risk changes from exposure modifications.
  • Current methods struggle to quantify population averages and individual variability.

Purpose of the Study:

  • To enhance the conceptual clarity and computational methods for exposure-response curves.
  • To enable precise quantification of population and individual risks.
  • To improve risk management decisions through better understanding of exposure relationships.

Main Methods:

  • Application of causal artificial intelligence (CAI) concepts.
  • Integration of machine learning (ML) computational techniques.
  • Development of methods to specify fixed variables and levels in curve estimation.

Main Results:

  • Clarified the precise meaning of exposure-response curves.
  • Enabled quantification of inter-individual variability around average curves.
  • Provided tools to determine the impact of exposure reductions on population and individual risks.

Conclusions:

  • Advances in CAI and ML offer improved methods for defining and quantifying exposure-response relationships.
  • Enhanced clarity allows for more accurate risk assessment and better-informed risk management strategies.
  • Future work should focus on specifying and communicating these refined exposure-response relationships.