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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...
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Confidence Intervals01:21

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
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...
Confidence Coefficient01:24

Confidence Coefficient

The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under both the...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...

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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

Relative risks and confidence intervals were easily computed indirectly from multivariable logistic regression.

A Russell Localio1, David J Margolis, Jesse A Berlin

  • 1Division of Biostatistics, Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia, PA 19104-6021, USA. rlocalio@mail.med.upenn.edu

Journal of Clinical Epidemiology
|August 11, 2007
PubMed
Summary

When outcomes are common, standard methods for calculating relative risks and confidence intervals can be misleading. Logistic regression with standardization or bootstrap resampling offers more reliable estimates in these scenarios.

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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

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Last Updated: Jul 13, 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

Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Modeling

Background:

  • Estimating relative risks is crucial in medical research, especially with multivariable binary regression.
  • Common outcomes present challenges for traditional statistical methods, potentially leading to inaccurate significance assessments.

Purpose of the Study:

  • To evaluate alternative statistical approaches for estimating relative risks and their confidence intervals.
  • To address the issue of common outcomes in multivariable binary regression analysis.

Main Methods:

  • Simulations were conducted on hypothetical randomized and cohort study groups.
  • A published observational study was reanalyzed to compare methods.
  • Key metrics included bias of relative risk estimates, confidence interval coverage, and Akaike information criterion.

Main Results:

  • A common confidence interval calculation method significantly overstated statistical significance with common outcomes.
  • Generalized linear models (non-logistic) sometimes failed to converge or yielded invalid risk estimates (>1.0).
  • Conditional/marginal standardization with logistic regression and bootstrap resampling provided valid risk estimates and confidence intervals within the [0,1] bounds.

Conclusions:

  • Indirect computation of relative risks and confidence intervals from multivariable logistic regression is reliable, particularly for common outcomes.
  • Log-linear regression models demonstrate limitations and potential issues when dealing with common outcomes.