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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Expected Value01:15

Expected Value

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The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
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Margin of Error01:27

Margin of Error

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The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
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Estimating Population Mean with Known Standard Deviation01:16

Estimating Population Mean with Known Standard Deviation

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To construct a confidence interval for a single unknown population mean μ, where the population standard deviation is known, we need sample mean as an estimate for μ and we need the margin of error. Here, the margin of error (EBM) is called the error bound for a population mean (abbreviated EBM). The sample mean is the point estimate of the unknown population mean μ.
The confidence interval estimate will have the form as follows:
(point estimate - error bound, point estimate +...
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Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

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When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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Variation01:19

Variation

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An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
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Updated: May 27, 2025

An R-Based Landscape Validation of a Competing Risk Model
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Expected Value of Sample Information Calculations for Risk Prediction Model Validation.

Mohsen Sadatsafavi1, Andrew J Vickers2, Tae Yoon Lee1

  • 1Respiratory Evaluation Sciences Program, Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, BC, Canada.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|February 18, 2025
PubMed
Summary

Value-of-information methodology quantifies the value of external validation studies for risk prediction models. This approach offers a value-based perspective for designing studies by calculating the expected gain in clinical utility from validation data.

Keywords:
Bayesian statisticsrisk predictionuncertaintyvalue of information

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Area of Science:

  • Biostatistics
  • Health Informatics
  • Clinical Epidemiology

Background:

  • External validation of risk prediction models is crucial for assessing performance in new populations before clinical adoption.
  • Current sample size calculations rely on classical inferential statistics, which may be less relevant for clinical utility metrics like net benefit (NB).
  • Value-of-information (VOI) methodology offers a framework to quantify the value of validation data based on expected gains in clinical utility.

Purpose of the Study:

  • To define and propose computational algorithms for the validation expected value of sample information (EVSI).
  • To evaluate the performance of different EVSI computation algorithms through simulation studies.
  • To apply the EVSI calculation to a real-world case study for designing external validation studies.

Main Methods:

  • Defined validation EVSI as the expected gain in NB from a validation sample of a specific size.
  • Developed and compared three algorithms for EVSI computation regarding accuracy and speed.
  • Utilized a myocardial infarction mortality risk model, validated using a non-US trial subset for a US population sample size calculation.

Main Results:

  • Simulation studies showed comparable EVSI values across algorithms, with variations in numerical accuracy and computation time.
  • For a 2% risk threshold, validating with 1,000 observations yielded an EVSI of 0.00101 (true-positive units) or 0.04938 (false-positive units).
  • Annual population EVSI in the US was estimated at 806 true positives gained or 39,500 false positives averted; diminishing returns observed beyond 4,000 observations.

Conclusions:

  • Value-of-information methodology provides a quantitative return on investment for external validation studies.
  • EVSI offers a value-based approach to complement traditional methods in designing predictive analytics validation studies.
  • This framework supports informed decision-making regarding the optimal sample size for external validation.