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Related Concept Videos

Multiple Comparison Tests01:13

Multiple Comparison Tests

4.0K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
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McNemar's Test01:23

McNemar's Test

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McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
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Midrange01:07

Midrange

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A somewhat easy to compute quantitative estimate of a data set’s central tendency is its midrange, which is defined as the mean of the minimum and maximum values of an ordered data set.
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to...
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Difference from Background: Limit of Detection01:05

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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
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Precision Implementation of Minimal Erythema Dose MED Testing to Assess Individual Variation in Human Inflammatory Response
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Deconstructing the Minimum Clinically Important Difference (MCID).

Janine Molino1,2, Joseph Harrington2, Jennifer Racine-Avila2

  • 1Lifespan Biostatistics Core, Rhode Island Hospital, Providence, RI, USA.

Orthopedic Research and Reviews
|February 25, 2022
PubMed
Summary
This summary is machine-generated.

The minimal clinically important difference (MCID) can vary significantly due to intrinsic calculation factors, not just external ones. Researchers should exercise caution when comparing MCIDs and consider calculating their own for individual studies.

Keywords:
categorical measureclinical improvementoutcome assessment

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Area of Science:

  • Orthopedics
  • Biostatistics
  • Patient-Reported Outcomes

Background:

  • The minimal clinically important difference (MCID) is crucial for interpreting patient outcomes in clinical research.
  • MCID values are often assumed to be consistent across studies, but variations exist.
  • Extrinsic factors are known to influence MCID calculations, yet intrinsic factors are less understood.

Purpose of the Study:

  • To investigate the impact of intrinsic calculation variabilities on the magnitude and precision of MCIDs.
  • To explore how different anchor questions, success thresholds, and sample sizes affect MCID calculations for knee replacement outcomes.

Main Methods:

  • Calculated MCIDs for knee replacement pain and function using an integrative anchor and distribution-based method.
  • Employed external anchor questions and receiver operator characteristic (ROC) curves.
  • Examined the effects of varying anchor questions, success/failure thresholds, and sample sizes on MCID outcomes.

Main Results:

  • Observed significant variability in both MCID magnitude and precision.
  • For pain scores, the success threshold most impacted magnitude, while sample size affected precision.
  • For function scores, sample size most influenced magnitude, and anchor questions affected precision.

Conclusions:

  • Comparing MCIDs across studies is challenging due to intrinsic calculation differences.
  • Intrinsic factors, such as sample size and anchor choice, profoundly impact MCID results.
  • Greater transparency in reporting MCID calculation methods is needed; studies should consider calculating their own MCIDs.