Clinical significance

In medicine and psychology, clinical significance refers to either of two related but slightly dissimilar concepts whereby certain findings or differences, even if measurable or statistically confirmed, either may or may not have additional significance, either by (1) being of a magnitude that conveys practical relevance (a usage that conflates practical and clinical significance interchangeably), or (2) more technically and restrictively, addresses whether an intervention or treatment may or may not fully correct the finding. Commentators who utilize the second, more restrictive usage designate the broader usage as linguistically imprecise and thus erroneous.

Statistical significance
Statistical significance tends to be used in the context of null hypothesis significance testing (NHST). NHST answers the question, if a hypothesis that an effect is zero in the population is true (the null hypothesis), what is the probability of obtaining data that indicate the effect is not zero? NHST is often misunderstood in several ways: that the p-value is the probability that the null hypothesis is false; that it is related to probability of replication; and that if the null hypothesis is rejected, the proposed alternative hypothesis should be accepted. Given the nature of NHST, and its common misuse, statistical significance does not yield information about magnitude of effect, practical significance, nor clinical significance. NHST only yields information about whether results are statistically likely given some assumption about the population. In terms of testing clinical treatments, statistical significance can only indicate an answer to this question: if a treatment is actually ineffective, how likely it is that the statistical test of the treatment would erroneously indicate that the treatment is effective?

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, number needed to treat (NNT), and preventive fraction. Practical significance may also convey semi-quantitative, comparative, or feasibility assessments of utility.

Effect size is one type of practical significance. It quantifies the extent to which a sample diverges from expectations. 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.

Although clinical significance and practical significance are often used synonymously, a more technical restrictive usage denotes this as erroneous. 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.

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?

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.

Within psychology and psychotherapy, clinical significance was first proposed by Jacobson, Follette, and Revenstorf 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.” 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.”

Clinical significance is also a consideration when interpreting the results of the psychological assessment of an individual. Frequently, there will be a difference of scores or subscores that is statistically significant, unlikely to have occurred purely by chance. However, not all of those statistically significant differences are clinically significant, in that they do not either explain existing information about the client, or provide useful direction for intervention. Differences that are small in magnitude typically lack practical relevance and are unlikely to be clinically significant. Differences that are common in the population are also unlikely to be clinically significant, because they may simply reflect a level of normal human variation. Additionally, clinicians look for information in the assessment data and the client's history that corroborates the relevance of the statistical difference, to establish the connection between performance on the specific test and the individual's more general functioning.

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.

Jacobson-Truax
Jacobson-Truax is common method of calculating clinical significance. It involves calculating a Reliability Change Index (RCI). 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.

Gulliksen-Lord-Novick
The Gulliksen-Lord-Novick method is similar to Jacobson-Truax, except that it takes into account regression to the mean. This is done by subtracting the pre-test and post-test scores from a population mean, and dividing by the standard deviation of the population.

Edwards-Nunnally
The Edwards-Nunnally method of calculating clinical significance is a more stringent alternative to the Jacobson-Truax method. 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.

Hageman-Arrindel
The Hageman-Arrindel 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.

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). A computer program, such as Hierarchical Linear and Nonlinear Modeling is used to calculate change estimates for each participant. HLM also allows for analysis of growth curve models of dyads and groups.