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How to measure the success of psychotherapy? Reveal the calculation method of clinical significance!
In the field of medicine and psychology, clinical significance is the practical importance of therapeutic effects—can it have a real, perceptible impact on daily life? This is a critical issue in psychotherapy evaluation. Evaluation of treatment effectiveness is not limited to statistical analysis, but in fact, clinical significance provides a deeper understanding of treatment outcomes.
Statistical significance and actual significance
Statistical significance is widely used in hypothesis tests, and results are effectively evaluated by detecting the false hypothesis (i.e., there is no relationship between variables). If there is a statistically significant difference, it means that to some extent we can reject the hypothesis of nothingness, but this does not prove that the hypothesis of nothingness is necessarily wrong.
Statistical significance is only related to the compatibility between the observation data and the false hypothesis, and cannot prove the authenticity of the hypothesis.
On the other hand, actual significance focuses on the effectiveness of treatment or intervention and the extent of change caused by treatment. This is not just a change in numbers, but the actual impact of treatment on the patient's daily life. Actual significance uses indicators such as the size of the effect, the number of treatment required (NNT) to quantify the importance of the outcome, thereby providing specific data on the effectiveness of the treatment.
The importance of clinical significance
Clinical significance is a more technical concept focusing on whether the treatment is effective enough to change the patient's diagnostic label. If treatment can prevent 40% of patients from meeting the diagnostic criteria for depression, this means that the treatment has significant clinical results. This concept was first proposed by Jacobson, Follette and Revenstorf, who elucidated how the effectiveness of treatment could lead to a patient's transition from dysfunction to normal function.
Clinical significance shifts the focus from collective effects to individual changes, and emphasizes the evaluation of efficacy in each patient.
There are often statistically significant differences when conducting psychological assessments, but this does not mean that these differences are clinically significant. Smaller differences may lack practical significance, while common differences may only reflect normal variations. Therefore, when interpreting the assessment results, clinical experts will look for important information that can fully illustrate the statistical differences to establish a specific connection with individual functions.
Calculation method of clinical significance
Unlike statistical significance and actual significance, there are also many methods for calculating clinical significance. Common calculation methods include the Jacobson-Truax method, the Gulliksen-Lord-Novick method, the Edwards-Nunnally method, the Hageman-Arrindell method and the hierarchical linear model (HLM).
Jacobson-Truax method
This method calculates the reliability change index (RCI), by comparing participants' previous and subsequent test score differences and dividing them by the standard error of the difference. This allows participants to be classified into categories of recovery, improvement, unchange, or worsening.
Gulliksen-Lord-Novick method
Similar to the Jacobson-Truax method, but considering the effect of mean regression, the test scores before and after are subtracted from the population mean and then calculated based on the population standard deviation.
Edwards-Nunnally method
This is a more rigorous calculation method that brings the pretest score closer to the mean by reliability scores and establishes confidence intervals for the adjusted pretest scores to evaluate clinical significance.
Hageman-Arrindell method
The change index involved in this method is also divided into group changes and individual changes, so as to judge the degree of improvement of the patient.
Hydraft Linear Model (HLM)
HLM uses growth curve analysis to evaluate changes, and three data points are collected from each patient to make the analysis more comprehensive and accurate.
Ultimately, when we consider the success of psychotherapy, we cannot rely solely on statistical data, but we need to pay more attention to the real impact of clinical significance in daily life. It reflects whether the patient's quality of life has substantially improved after the treatment course, which may be the ultimate goal we pursue. So, what indicators do you think can best show the success of treatment?
