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Clinical Research on Dyspnea
Author Bios
What is Dyspnea?
What Provokes Dyspnea?
The Nature of Dyspnea
Language of Dyspnea
Clinical Application
Research Application
Variability in Sensations
Challenges in Study
Mechanical Loads and Sense of Effort
Chemoreceptors
Mechanoreceptors
Neuro-Mechanical Dissociation
Phase of Respiration and Dyspnea
Physiology of Dyspnea
Respiratory System
Cardiovascular System
Measuring Dyspnea
Scaling Issues
Qualitative Aspects
Reliability and Validity Overview
Reliability and Validity
Sensitivity and Specificity
Scales
Sensation vs. Perception vs. Symptom
Treating Dyspnea
Why Measure?
Cluster Analysis
Statistical vs. Clinical Significance
Currently selected section: Standard Error of Measurement
Measuring Fatigue
Measuring Depression
Measuring Anxiety and Hyperventilation
Measuring Quality of Life
Conclusion

 

Chapter 23: Dyspnea: Standard Error of Measurement
        

The theoretical (i.e. statistical) analysis of scores depends on comparisons between obtained scores (or statistics) and expected scores (or statistics) from the population based on happenstance (chance). But in practice our comparisons are based almost without exception on scores obtained from samples, not on populations. For example, if we record systolic blood pressure in a large number of volunteers (n=100) and calculate the mean and standard deviation of our sample scores, we would know on average how far away any particular individual's score was from the (sample) average. But now if we repeat the effort (i.e. the measurement of systolic blood pressure in multiple separate samples of 100 individuals) over and over again (say, 100 times) we would know on average how far away any particular sample's average score was from the mean of all the (100) samples tested. The "standard deviation" of the mean of all the sample means (i.e. the population mean) is the standard error of measurement (SEM). Scores that fall beyond ± 1 SEMs are interpreted as unlike most (~67%) of the other scores.

In the study of dyspnea specifically, and quality of life generally, efforts to link the standard error of measurement with the minimal clinically significant difference (MCID) are accelerating. The MCID is the new metric on the block, designed to facilitate understanding changes in functional status or health-related quality of life scores. A minimal clinically significant difference is described as the smallest difference in a measurable parameter that clinicians and patients would care about it (e.g. about a 200 ml change in FEV1). The MCID is a threshold, in a way, that purportedly represents the change level in a quality of life instrument where patients begin to notice that there has been an important improvement or decline (Wyrwich et al., 2002).

The MCID is not a fixed quantity but specific for each scale under consideration. For example, for overall change in asthma-specific quality of life captured by the Asthma Quality of Life Questionnaire, the minimal important difference is 0.5 (Juniper et al., 1994); for overall change in dyspnea captured by the Transition Dyspnea Index, the minimal important difference is 1 (Witek and Mahler, 2003). The MCID is neither a simple concept nor a simple calculation and depends, in part, on physician-generated global transition ratings.

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