Clinical significance
In medicine and psychology, clinical significance is the practical importance of a treatment effect—whether it has a real genuine, palpable, noticeable effect on daily life.[1]
Types of significance
Statistical significance
Statistical significance is used in
Practical significance
In broad usage, the "practical clinical significance" answers the question, how effective is the intervention or treatment, or how much change does the treatment cause. In terms of testing clinical treatments, practical significance optimally yields quantified information about the importance of a finding, using metrics such as
Effect size is one type of practical significance.[4][5] It quantifies the extent to which a sample diverges from expectations.[6] Effect size can provide important information about the results of a study, and are recommended for inclusion in addition to statistical significance. Effect sizes have their own sources of bias, are subject to change based on population variability of the dependent variable, and tend to focus on group effects, not individual changes.[5][7][8]
Although clinical significance and practical significance are often used synonymously, a more technical restrictive usage denotes this as erroneous.[5] This technical use within psychology and psychotherapy not only results from a carefully drawn precision and particularity of language, but it enables a shift in perspective from group effects to the specifics of change(s) within an individual.[citation needed]
Specific usage
In contrast, when used as a technical term within psychology and psychotherapy, clinical significance yields information on whether a treatment was effective enough to change a patient’s diagnostic label. In terms of clinical treatment studies, clinical significance answers the question "Is a treatment effective enough to cause the patient to be normal [with respect to the diagnostic criteria in question]?"[citation needed]
For example, a treatment might significantly change depressive symptoms (statistical significance), the change could be a large decrease in depressive symptoms (practical significance- effect size), and 40% of the patients no longer met the diagnostic criteria for depression (clinical significance). It is very possible to have a treatment that yields a significant difference and medium or large effect sizes, but does not move a patient from dysfunctional to functional.[citation needed]
Within psychology and psychotherapy, clinical significance was first proposed by Jacobson, Follette, and Revenstorf[9] as a way to answer the question, is a therapy or treatment effective enough such that a client does not meet the criteria for a diagnosis? Jacobson and Truax later defined clinical significance as "the extent to which therapy moves someone outside the range of the dysfunctional population or within the range of the functional population."[10] They proposed two components of this index of change: the status of a patient or client after therapy has been completed, and "how much change has occurred during the course of therapy."[10]
Clinical significance is also a consideration when interpreting the results of the
Calculation of clinical significance
Just as there are many ways to calculate statistical significance and practical significance, there are a variety of ways to calculate clinical significance. Five common methods are the Jacobson-Truax method, the Gulliksen-Lord-Novick method, the Edwards-Nunnally method, the Hageman-Arrindell method, and hierarchical linear modeling.[5]
Jacobson-Truax
Jacobson-Truax is common method of calculating clinical significance. It involves calculating a Reliability Change Index (RCI).[10] The RCI equals the difference between a participant’s pre-test and post-test scores, divided by the standard error of the difference. Cutoff scores are established for placing participants into one of four categories: recovered, improved, unchanged, or deteriorated, depending on the directionality of the RCI and whether the cutoff score was met.[citation needed]
Gulliksen-Lord-Novick
The Gulliksen-Lord-Novick method
Edwards-Nunnally
The Edwards-Nunnally method[14] of calculating clinical significance is a more stringent alternative to the Jacobson-Truax method.[5] Reliability scores are used to bring the pre-test scores closer to the mean, and then a confidence interval is developed for this adjusted pre-test score. Confidence intervals are used when calculating the change from pre-test to post-test, so greater actual change in scores is necessary to show clinical significance, compared to the Jacobson-Truax method.[citation needed]
Hageman-Arrindell
The Hageman-Arrindell[15] calculation of clinical significance involves indices of group change and of individual change. The reliability of change indicates whether a patient has improved, stayed the same, or deteriorated. A second index, the clinical significance of change, indicates four categories similar to those used by Jacobson-Truax: deteriorated, not reliably changed, improved but not recovered, and recovered.[citation needed]
Hierarchical linear modeling (HLM)
HLM involves growth curve analysis instead of pre-test post-test comparisons, so three data points are needed from each patient, instead of only two data points (pre-test and post-test).[5] A computer program, such as Hierarchical Linear and Nonlinear Modeling[16] is used to calculate change estimates for each participant. HLM also allows for analysis of growth curve models of dyads and groups.[citation needed]
See also
- Cohen's h
- Medical statistics
- Minimal clinically important difference
References
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