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Related Concept Videos

Survival Curves01:18

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
Relative Risk01:12

Relative Risk

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...
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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.
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Dose Response Curve: Conventional Versus Nonmonotonic01:21

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The correlation between a drug's dosage and its impact on a biological system is a cornerstone of pharmacology and toxicology. Conventional dose–response curves, which include graded and quantal relationships, are key to this understanding. Graded dose–response curves depict the spectrum of a biological reaction to different doses within an individual, indicating that as the drug dosage increases, so does the intensity of the response. On the other hand, quantal dose–response relationships...
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Updated: May 12, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Disease risk curves.

G Hughes, F J Burnett, N D Havis

    Phytopathology
    |March 28, 2013
    PubMed
    Summary

    Disease risk curves graphically represent the probability of needing crop treatment based on risk factors. These curves calibrate evidence on a probability scale, aiding in justified crop protection decisions.

    Area of Science:

    • Agricultural science
    • Plant pathology
    • Risk assessment

    Background:

    • Disease risk curves are graphical tools linking treatment probability to risk factors.
    • Understanding disease occurrence in crops is crucial for effective management.
    • Risk quantifies the likelihood of adverse consequences, such as disease reaching a treatment threshold.

    Purpose of the Study:

    • To describe disease risk curves derived from single and multiple risk factors.
    • To present disease risk curves as calibration tools for evidence on a probability scale.
    • To introduce a crop loss assessment model based on risk rather than yield loss.

    Main Methods:

    • Modeling disease risk as a function of multiple risk factors.
    • Modeling disease risk as a function of a single factor, specifically early disease levels.

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  • Utilizing disease risk curves as calibration curves to express evidence probability.

    • Main Results:

    • Disease risk curves can be generated using single or multiple risk factors.
    • These curves effectively calibrate accumulated risk evidence onto a probability scale.
    • Modeling risk based on early disease assessment provides a risk-denominated crop loss model.

    Conclusions:

    • Disease risk curves are versatile tools for quantifying and communicating disease risk in crops.
    • They enable evidence-based decision-making for crop protection measures.
    • The risk-based crop loss model offers an alternative to traditional yield-loss assessments.