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Related Concept Videos

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.
A...
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Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
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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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Confidence Coefficient01:24

Confidence Coefficient

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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...
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Actuarial Approach01:20

Actuarial Approach

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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
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Determination of Expected Frequency01:08

Determination of Expected Frequency

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Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
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An R-Based Landscape Validation of a Competing Risk Model
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Estimating average attributable fractions with confidence intervals for cohort and case-control studies.

John Ferguson1, Alberto Alvarez-Iglesias1, John Newell1,2

  • 11 HRB Clinical Research Facility, National University of Ireland Galway, Ireland.

Statistical Methods in Medical Research
|June 26, 2016
PubMed
Summary

New methods estimate average attributable fractions for chronic disease risk factors in case-control and prospective studies. This helps target interventions by quantifying disease burden contributions from individual factors.

Keywords:
EpidemiologyMonte Carlo confidence intervalattributable fractionpermutationsweighted likelihood

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Chronic diseases result from complex interactions between environmental and genetic risk factors.
  • Average attributable fractions (AAFs) partition disease burden to individual risk factors, aiding intervention strategies.
  • Existing AAF estimation methods may lack applicability to various study designs.

Purpose of the Study:

  • To introduce novel estimation methods for AAFs applicable to both case-control and prospective study designs.
  • To provide methods for calculating confidence intervals for AAFs using Monte Carlo simulation.
  • To develop a computationally tractable approximation for AAFs with numerous risk factors.

Main Methods:

  • Development of new statistical methodologies for estimating AAFs.
  • Application of Monte Carlo simulation for confidence interval derivation.
  • Introduction of a novel approximation for large-scale AAF calculations.

Main Results:

  • Validated estimation methods for AAFs across different epidemiological study designs.
  • Established confidence intervals for AAF estimates, enhancing reliability.
  • Presented an efficient approximation for AAF estimation with multiple risk factors.

Conclusions:

  • The new methods provide robust tools for quantifying the impact of individual risk factors on chronic disease burden.
  • These advancements support evidence-based decision-making for public health interventions.
  • An R package is available for implementing these novel AAF estimation techniques.