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Related Concept Videos

Data Validation01:03

Data Validation

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Data Validation01:15

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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
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Energy Budgets

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Organisms must balance energy intake with the energy required for growth, maintenance and reproduction. These trade-offs result in a variety of survivorship and reproductive strategies, including semelparity and iteroparity. Semelparous species, like annual plants, have only one reproductive episode in their lifetimes and consequently have short lifespans. Iteroparous species, by contrast, have many reproductive events during their lifetimes but have relatively few offspring. These two...
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Testing a Claim about Mean: Known Population SD01:11

Testing a Claim about Mean: Known Population SD

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A complete procedure of testing the hypothesis about a population mean is explained here.
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
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Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

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A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
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In Vivo Protocol of Controlled Subconcussive Head Impacts for the Validation of Field Study Data
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Validating a Budget Impact Model Using Payer Insight and Claims Data: A Framework and Case Study.

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  • 11Duke Clinical Research Institute, Durham, North Carolina.

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Summary
This summary is machine-generated.

Budget impact models for antidiabetic formulary changes were validated for accuracy. Initial overprediction of utilization was corrected, improving model transparency and reliability for payers.

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Area of Science:

  • Health economics
  • Pharmacoeconomics
  • Health services research

Background:

  • Budget impact models are crucial for formulary decision-making but lack validation studies.
  • This underuse may stem from concerns about model reliability and transparency.

Purpose of the Study:

  • To validate a budget impact model for antidiabetic formulary changes.
  • Assessed face, internal verification, and predictive validity.

Main Methods:

  • Expert review for face validity, incorporating diverse perspectives.
  • Employed multiple techniques for internal verification (e.g., walk-throughs, unit tests).
  • Evaluated predictive validity by comparing predicted vs. realized budget using mean absolute scaled error.

Main Results:

  • Face validity assessment identified areas for improvement, including external factors like insurance and policy changes.
  • Internal verification detected and corrected errors in equations and data.
  • Initial model overpredicted utilization by 13% (absolute scaled error 2.60); accuracy improved to 0.48 after adjusting utilization assumptions.

Conclusions:

  • The budget impact model initially overestimated utilization post-formulary change.
  • Validation enhanced model accuracy and transparency, increasing its utility for payers.