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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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Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
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Hypothesis: Accept or Fail to Reject?01:17

Hypothesis: Accept or Fail to Reject?

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The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null...
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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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The Availability Heuristic01:08

The Availability Heuristic

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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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All MCIDs Are Wrong, But Some May be Useful.

Christopher W Boyer, Ian E Lee, Matthew S Tenan

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    |June 1, 2022
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    Summary
    This summary is machine-generated.

    Baseline values significantly impact minimum clinically important differences (MCIDs) accuracy. MCIDs are only valid within specific baseline score ranges, questioning their utility for benchmarking treatment outcomes.

    Keywords:
    clinical measurement (clinimetrics)implementation science/quality improvementoutcome measuresstatistical analysis/research design

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    Area of Science:

    • Orthopedics
    • Health Outcomes Research
    • Biostatistics

    Background:

    • Minimum Clinically Important Differences (MCIDs) are crucial for interpreting treatment outcomes.
    • Traditional methods for calculating MCIDs may not account for baseline score variability.
    • The accuracy of MCID estimates can be influenced by patient baseline characteristics.

    Purpose of the Study:

    • To demonstrate the application of baseline-adjusted receiver operator characteristic (AROC) curve analysis for MCIDs.
    • To identify new insights regarding MCIDs using empirical data.
    • To evaluate the validity of MCID estimates across different baseline scores.

    Main Methods:

    • Retrospective study of 999 active-duty military patients.
    • Calculation of anchored MCIDs using standard receiver operator characteristic (ROC) and AROC analyses.
    • Inclusion of Patient-Reported Outcome Measure Information System (PROMIS) Pain Interference and Defense and Veterans Pain Rating Scale (DVPRS) data.

    Main Results:

    • AROC analysis revealed specific valid ranges for MCID estimation.
    • For PROMIS Pain Interference, MCID validity was observed between baseline scores of 61.0 and 72.3.
    • For DVPRS, MCID validity was found between baseline scores of 5.9 and 7.9.

    Conclusions:

    • Baseline values critically influence MCID accuracy and validity.
    • MCIDs are statistically and theoretically valid only within discrete baseline score ranges.
    • The construct of MCID may be too flawed for accurately benchmarking treatment outcomes.