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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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Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
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Calculating Expected Value of Sample Information Adjusting for Imperfect Implementation.

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|January 17, 2022
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Summary
This summary is machine-generated.

New methods calculate the true value of research by adjusting expected value of sample information (EVSI) for imperfect treatment implementation. These approaches provide accurate, efficient estimates for real-world application.

Keywords:
decision analysisexpected value of sample informationhealth economic decision modelingimplementation dynamicsresearch designvalue of information

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Area of Science:

  • Decision analysis
  • Health economics
  • Medical research methodology

Background:

  • Standard expected value of sample information (EVSI) analyses do not account for real-world implementation challenges of treatment recommendations.
  • This omission leads to an inaccurate estimation of a study's true value.
  • Existing methods for calculating research value with implementation adjustments are computationally challenging.

Purpose of the Study:

  • To develop and evaluate novel methods for calculating implementation-adjusted EVSI.
  • To provide accurate and computationally feasible estimates of research value under realistic implementation scenarios.
  • To address the limitations of standard EVSI in capturing the full impact of research findings.

Main Methods:

  • Developed two methods to calculate implementation-adjusted EVSI, assuming implementation levels correlate with evidence strength.
  • Method 1: Utilizes computationally intensive nested Monte Carlo simulations based on the EVSI definition.
  • Method 2: Adapts the efficient moment matching method for EVSI computation to incorporate implementation adjustments.

Main Results:

  • The two developed methods produced similar estimates for the implementation-adjusted EVSI, with a maximum difference of 6%.
  • The moment matching-based method was significantly faster (6-60 times) than nested simulations.
  • Both methods successfully calculated implementation-adjusted EVSI across three example cases.

Conclusions:

  • The proposed methods enable the calculation of implementation-adjusted EVSI under realistic assumptions.
  • The efficient estimation method is accurate and practical for real-world use.
  • Adapting existing EVSI methods for implementation adjustments maintains similar computational costs to standard analyses.