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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.
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Interpretation of Confidence Intervals01:19

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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

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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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Uncertainty: Confidence Intervals00:54

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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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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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Sample Size Calculation

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
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Confidence intervals and sample size planning for optimal cutpoints.

Christian Thiele1, Gerrit Hirschfeld1

  • 1Faculty of Business and Health, University of Applied Sciences Bielefeld, Bielefeld, Germany.

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|January 3, 2023
PubMed
Summary
This summary is machine-generated.

Estimating optimal cutpoints for diagnostic tests requires careful consideration of precision and sample size. This study evaluates methods for calculating confidence intervals and provides tools for better sample size planning in diagnostic research.

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Area of Science:

  • Biostatistics
  • Diagnostic Test Evaluation

Background:

  • Optimal cutpoints for diagnostic measures are crucial but often lack reported precision.
  • Inadequate sample sizes hinder the precise estimation of these cutpoints.

Purpose of the Study:

  • To evaluate post-hoc methods for estimating the variance of cutpoint estimations using published statistics.
  • To discuss sample size planning for accurate cutpoint estimation.

Main Methods:

  • Simulation study using Youden index optimization methods (empirical, normal, transformed normal).
  • Evaluation of confidence interval methods: delta method, parametric bootstrap, nonparametric bootstrap.
  • Development of a web-application for simulating confidence interval width and coverage.

Main Results:

  • Delta method and parametric bootstrap are suitable for post-hoc confidence intervals, depending on sample size, data distribution, and model assumptions.
  • Parametric bootstrap with normal-theory cutpoint estimation offers the best average coverage.
  • Delta method is efficient for normally distributed data, but less reliable in small samples.

Conclusions:

  • The choice of method for estimating cutpoint precision depends on specific study characteristics.
  • Accurate sample size planning is essential for reliable diagnostic measure cutpoint estimation.
  • A practical web-application is provided to aid researchers in simulation-based planning.