Commentary: Moving Toward Cost-Effectiveness in Using Psychophysiological Measures in Clinical Assessment: Validity, Decision Making, and Adding Value

Abstract

Psychophysiological measures offer a variety of potential advantages, including more direct assessment of certain processes, as well as provision of information that may contrast with other sources. The role of psychophysiological measures in clinical practice will be best defined when researchers (a) switch to research designs and statistical models that better approximate how clinicians administer assessments and make clinical decisions in practice, (b) systematically compare the validity of psychophysiological measures to incumbent methods for assessing similar criteria, (c) test whether psychophysiological measures show either greater validity or clinically meaningful incremental validity, and (d) factor in fiscal costs as well as the utilities that the client attaches to different assessment outcomes. The statistical methods are now readily available, along with the interpretive models for integrating assessment results into client-centered decision making. These, combined with technology reducing the cost of psychophysiological measurement and improving ease of interpretation, poise the field for a rapid transformation of assessment practice, but only if we let go of old habits of research.

Notes

Note. AUC = Area Under the Curve; CDI = Child Depression Inventory; FN = Feedback Negativity; RSA = respiratory sinus arrhythmia; ANOVA = analysis of variance; YSR = Youth Self Report.

^a Eta-squared ranged from .03 to .31; all reported as 3 df so cannot estimate AUC.

^b Coefficients from multiple regression cannot be converted into other effect size metrics, because they are contingent on the other predictors included in the equation (Lipsey & Wilson, 2001). Note that Franklin, Glenn, Jamieson, and Nock (2014) was a review paper that did not report any statistical analyses.