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Related Concept Videos

Relative Risk01:12

Relative Risk

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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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Odds Ratio01:09

Odds Ratio

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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...
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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Hazard Ratio01:12

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The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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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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Ratio Level of Measurement00:54

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The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
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An R-Based Landscape Validation of a Competing Risk Model
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Reflection on modern methods: risk ratio regression-simple concept yet complex computation.

Murthy N Mittinty1,2, John Lynch1,2,3

  • 1School of Public Health, University of Adelaide, Adelaide, SA, Australia.

International Journal of Epidemiology
|November 23, 2022
PubMed
Summary
This summary is machine-generated.

The risk ratio (RR) offers a straightforward interpretation of outcome occurrence in exposed versus unexposed groups. Despite challenges in estimation, new computational methods improve the accuracy and reliability of RR calculations in epidemiological studies.

Keywords:
Relative riskepidemiologygeneralized linear modelsregression

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • The risk ratio (RR) is a fundamental measure for comparing outcome risks between exposed and unexposed groups.
  • Despite its conceptual simplicity, the RR has been less popular than the odds ratio (OR) in epidemiological research.
  • Estimating the OR is common via logistic regression, with its interpretation as an RR often made under the rare outcome assumption.

Purpose of the Study:

  • To highlight the interpretational advantages of the risk ratio (RR) over the odds ratio (OR).
  • To discuss the persistent challenges in accurately estimating the RR.
  • To introduce advancements in computational methods for robust RR estimation.

Main Methods:

  • Discussion of common estimation methods for the odds ratio (OR) using logistic regression.
  • Exploration of challenges associated with risk ratio (RR) estimation, including algorithmic convergence and regression specification (e.g., log-binomial, Poisson models).
  • Review of contemporary computational techniques designed to address RR estimation issues and provide doubly robust estimates.

Main Results:

  • The odds ratio (OR) is simpler to estimate but harder to interpret, while the risk ratio (RR) is challenging to estimate but straightforward to interpret.
  • Long-standing issues in RR estimation persist, impacting its widespread application.
  • Emerging computational methods offer solutions for convergence problems and enable doubly robust RR estimation.

Conclusions:

  • The risk ratio (RR) provides a more direct and interpretable measure of risk comparison than the odds ratio (OR).
  • Overcoming estimation challenges is crucial for the broader adoption of RR in epidemiological studies.
  • Recent computational advancements enhance the feasibility and reliability of obtaining accurate risk ratio estimates.