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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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Confidence Intervals01:21

Confidence Intervals

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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.
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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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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.
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Odds Ratio01:09

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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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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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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...
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Finding Critical Values for Chi-Square01:18

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Consider a curve representing sample data drawn randomly from a normally distributed population. One must construct confidence intervals to estimate or to test a claim regarding the population standard deviation. For example, a 95% confidence interval covers 95% of the area under the curve, and the remaining 5% is equally distributed on either side of the curve. To achieve such confidence intervals, one must determine the critical values. The critical values are simply the values separating the...
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An R-Based Landscape Validation of a Competing Risk Model
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Confidence intervals for the difference between two relative risks.

Joshua N Sampson1, Mitchell H Gail1

  • 1Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.

Statistical Methods in Medical Research
|April 17, 2020
PubMed
Summary
This summary is machine-generated.

New methods estimate confidence intervals for the difference between two relative risks, applicable to vaccine efficacy trials. These methods suggest one dose of the human papillomavirus vaccine may be as effective as two doses.

Keywords:
Confidence intervaldifference in relative risksrelative riskssmall samplesvaccine efficacy

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Vaccinology

Background:

  • Estimating the difference between two relative risks is crucial in clinical trials.
  • Vaccine efficacy is often assessed by comparing event rates between vaccinated and control groups.

Purpose of the Study:

  • To develop and present methods for estimating confidence intervals for the difference between two relative risks.
  • To apply these methods to vaccine trials for comparing vaccine efficacies.

Main Methods:

  • The methods estimate confidence intervals for r = p1/p0 - p2/p0, where p0, p1, and p2 are event probabilities in control, treatment 1, and treatment 2 groups.
  • The approach accommodates small sample sizes, covariates, and multi-strata populations.
  • Specifically applied to vaccine efficacy (VE) calculations: VE1 = 1 - p1/p0, VE2 = 1 - p2/p0, and r = VE2 - VE1.

Main Results:

  • The methods provide a robust way to estimate the difference in vaccine efficacy.
  • Interim data analysis from a human papillomavirus vaccine trial suggests potential equivalence in efficacy between one and two vaccine doses.

Conclusions:

  • The developed methods are suitable for analyzing vaccine trial data, even with small sample sizes or complex study designs.
  • Findings from the human papillomavirus vaccine trial indicate that a single dose might offer comparable protection to a two-dose regimen.