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Related Concept Videos

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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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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Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
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What are Estimates?01:06

What are Estimates?

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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
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Sampling Distribution01:12

Sampling Distribution

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Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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Standard Deviation of Calculated Results01:14

Standard Deviation of Calculated Results

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Standard deviation measures the spread of data around the mean value. Many large data sets follow a Gaussian distribution, also known as a normal distribution. This distribution is bell-shaped curved, with the most frequently observed value (mean or central value) in the middle. The farther away from the central value, the greater the deviation from the central value, and the lower the frequency.
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Updated: Oct 24, 2025

Design and Optimization Strategies of a High-Performance Vented Box
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Simulating Study Data to Support Expected Value of Sample Information Calculations: A Tutorial.

Anna Heath1,2,3, Mark Strong4, David Glynn5

  • 1Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, ON, Canada.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|August 14, 2021
PubMed
Summary
This summary is machine-generated.

This tutorial provides a guide to simulating study data for expected value of sample information (EVSI) calculations. It details methods for simulating data, handling correlations, missingness, and censoring to improve research prioritization.

Keywords:
R tutorialexpected value of sample informationresearch design methodssimulation methodsvalue of information

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Area of Science:

  • Health economics
  • Decision science
  • Biostatistics

Background:

  • The expected value of sample information (EVSI) aids in prioritizing research and designing cost-effective studies for medical decision-making.
  • While probabilistic modeling and uncertainty updating are well-documented for EVSI, simulating study data remains underexplored.

Purpose of the Study:

  • To provide a comprehensive, step-by-step guide for simulating study data essential for EVSI calculations.
  • To address the gap in literature regarding the simulation of study data for EVSI analysis.

Main Methods:

  • A general-purpose algorithm for simulating data is presented, demonstrating its application across three distinct outcome types.
  • Techniques for inducing correlations, managing missing data and censoring, and leveraging existing individual patient data are discussed.
  • R language code and Excel spreadsheets are provided for practical implementation.

Main Results:

  • The tutorial offers a practical framework for simulating diverse study data suitable for EVSI computations.
  • Demonstrates the simulation of data for various outcome types, including handling complexities like missingness and censoring.

Conclusions:

  • This guide facilitates practical EVSI calculations, enabling more effective research prioritization and study design.
  • Empowers researchers to utilize EVSI for optimizing resource allocation in medical research and decision-making.