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Related Concept Videos

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...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Cumulative Frequency Distribution01:04

Cumulative Frequency Distribution

A cumulative frequency distribution is another type of frequency distribution. Instead of reporting how many data values fall in some classes, it reports how many data values are contained in either that class or any class to its left. Technically, it means the sum of frequencies of the class and all the classes below it in a frequency distribution. A cumulative frequency is calculated by adding the frequency of each class lower than the corresponding class interval or category. In general, a...
Hazard Rate01:11

Hazard Rate

The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...

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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

Understanding cumulative risk.

Rachel McCloy1, Ruth M J Byrne, Philip N Johnson-Laird

  • 1University of Reading, Reading, UK. r.a.mccloy@reading.ac.uk

Quarterly Journal of Experimental Psychology (2006)
|July 11, 2009
PubMed
Summary

Estimating cumulative risks, like food poisoning from multiple tainted food portions, is challenging. Framing problems to focus on specific cases significantly improves risk estimation accuracy for individuals.

Area of Science:

  • Cognitive Psychology
  • Risk Perception
  • Decision Science

Background:

  • Individuals often struggle with accurately assessing cumulative risks, such as the probability of experiencing adverse events from repeated exposures.
  • Understanding the cognitive biases and heuristics that lead to inaccurate risk estimations is crucial for developing effective interventions.

Purpose of the Study:

  • To explain why naive individuals find cumulative risk estimation difficult.
  • To investigate methods for improving individuals' accuracy in estimating cumulative risks.
  • To test the impact of problem framing on risk perception.

Main Methods:

  • Theoretical explanation of difficulties in cumulative risk assessment.
  • Development of a computer program to model naive estimation methods.

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

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Last Updated: Jun 21, 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

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

  • Conducting two experiments to test hypotheses about risk estimation improvement.

    • Main Results:

    • Individuals' estimates of cumulative risks can be improved by framing problems to highlight relevant subsets of cases.
    • The accuracy of risk estimation was not reliably affected by whether problems were presented using frequencies or percentages.
    • A computer model predicted improved estimates under specific framing conditions.

    Conclusions:

    • Cognitive framing is a key factor in improving the accuracy of individual cumulative risk assessments.
    • Presenting risk information in terms of frequencies versus percentages does not inherently enhance understanding.
    • Future interventions should focus on optimizing problem presentation to aid risk perception.