2002
DOI: 10.4065/77.4.371
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Methods to Explain the Clinical Significance of Health Status Measures

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Cited by 1,308 publications
(1,164 citation statements)
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References 57 publications
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“…Distribution‐based approaches, although easy to perform, are purely statistical, assuming that the MID is related to the distribution of observed scores in a relevant sample. This approach has been criticised for its arbitrariness, and because it is so heavily influenced by the heterogeneity of the population being studied 30. Furthermore, while, for example, a magnitude of change of 0.5 SD of a sample will probably be meaningful, it provides no direct information about the minimal difference that is important to patients.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Distribution‐based approaches, although easy to perform, are purely statistical, assuming that the MID is related to the distribution of observed scores in a relevant sample. This approach has been criticised for its arbitrariness, and because it is so heavily influenced by the heterogeneity of the population being studied 30. Furthermore, while, for example, a magnitude of change of 0.5 SD of a sample will probably be meaningful, it provides no direct information about the minimal difference that is important to patients.…”
Section: Discussionmentioning
confidence: 99%
“…Different researchers have suggested that specific multiplications of the standard deviation, or the standard error of the mean (SEM) may approximate the MID for patient‐reported outcome instruments 28, 30. We used the SEM approach, which calculates the MID as SD base  × √(1 – r xx’ ), where SD base is the baseline standard deviation of the sample and r xx’ is the reliability coefficient, and two standard deviation approaches: 0.5 × SD base and 0.2 × SD base .…”
Section: Methodsmentioning
confidence: 99%
“…We will illustrate the difference in the methodology by using MID(a) for the anchor‐based approach or MID(d) for the distribution‐based approach. No single approach is perfect, and multiple strategies are likely to enhance the interpretability of changes in HRQOL scales 11, 27. An anchor‐based method was used as the primary approach (as preferred by the FDA) 2, based on the average change in LupusQoL or SF‐36 scores for the subset of patients who were considered to have a small but discernible change in that particular HRQOL domain.…”
Section: Methodsmentioning
confidence: 99%
“…As with many HRQOL measures, the interpretation of the data may be problematic and should not be based solely on P values, especially if HRQOL is a secondary outcome when a trial tends not to be powered for HRQOL. To aid the interpretation of the LupusQoL, evaluation is required to assess its sensitivity to change (the ability to detect an improvement or deterioration when patients deem themselves to have improved or deteriorated) 10 as advocated by the regulatory bodies 2, and to estimate the minimal important difference (MID), the smallest difference that patients perceive as beneficial or harmful 11.…”
Section: Introductionmentioning
confidence: 99%
“…Finally, the sample size was not adequate to detect differences between the groups that corresponded to effect sizes below d = 0.63. While there is no consensus on the cutoff for clinical significance, differences between groups exceeding 1/3 or 1/2 of the standard deviation (effect sizes exceeding 0.33 or 0.5) are often deemed clinically significant 36, 37. In the present study, only one of the effect sizes (0.42 for visual‐spatial processing) was in this range, but statistical significance was not reached with the available sample size.…”
Section: Discussionmentioning
confidence: 52%