BACKGROUND: Patient expectancy is an important source of placebo effects in antidepressant clinical trials, but all prior studies measured expectancy prior to the initiation of medication treatment. Little is known about how expectancy changes during the course of treatment and how such changes influence clinical outcome. Consequently, we undertook the first analysis to date of in-treatment expectancy during antidepressant treatment to identify its clinical and demographic correlates, typical trajectories, and associations with treatment outcome. METHODS: Data were combined from two randomized controlled trials of antidepressant medication for major depressive disorder in which baseline and in-treatment expectancy assessments were available. Machine learning methods were used to identify pre-treatment clinical and demographic predictors of expectancy. Multilevel models were implemented to test the effects of expectancy on subsequent treatment outcome, disentangling within- and between-patient effects. RESULTS: Random forest analyses demonstrated that whereas more severe depressive symptoms predicted lower pre-treatment expectancy, in-treatment expectancy was unrelated to symptom severity. At each measurement point, increased in-treatment patient expectancy significantly predicted decreased depressive symptoms at the following measurement (B = -0.45, t = -3.04, p = 0.003). The greater the gap between expected treatment outcomes and actual depressive severity, the greater the subsequent symptom reductions were (B = 0.49, t = 2.33, p = 0.02). CONCLUSIONS: Greater in-treatment patient expectancy is associated with greater subsequent depressive symptom reduction. These findings suggest that clinicians may benefit from monitoring and optimizing patient expectancy during antidepressant treatment. Expectancy may represent another treatment parameter, similar to medication compliance and side effects, to be regularly monitored during antidepressant clinical management.
BACKGROUND:Patient expectancy is an important source of placebo effects in antidepressant clinical trials, but all prior studies measured expectancy prior to the initiation of medication treatment. Little is known about how expectancy changes during the course of treatment and how such changes influence clinical outcome. Consequently, we undertook the first analysis to date of in-treatment expectancy during antidepressant treatment to identify its clinical and demographic correlates, typical trajectories, and associations with treatment outcome. METHODS: Data were combined from two randomized controlled trials of antidepressant medication for major depressive disorder in which baseline and in-treatment expectancy assessments were available. Machine learning methods were used to identify pre-treatment clinical and demographic predictors of expectancy. Multilevel models were implemented to test the effects of expectancy on subsequent treatment outcome, disentangling within- and between-patient effects. RESULTS: Random forest analyses demonstrated that whereas more severe depressive symptoms predicted lower pre-treatment expectancy, in-treatment expectancy was unrelated to symptom severity. At each measurement point, increased in-treatment patient expectancy significantly predicted decreased depressive symptoms at the following measurement (B = -0.45, t = -3.04, p = 0.003). The greater the gap between expected treatment outcomes and actual depressive severity, the greater the subsequent symptom reductions were (B = 0.49, t = 2.33, p = 0.02). CONCLUSIONS: Greater in-treatment patient expectancy is associated with greater subsequent depressive symptom reduction. These findings suggest that clinicians may benefit from monitoring and optimizing patient expectancy during antidepressant treatment. Expectancy may represent another treatment parameter, similar to medication compliance and side effects, to be regularly monitored during antidepressant clinical management.
Entities:
Keywords:
expectancy; major depressive disorder; placebo effects
Authors: Erica A Smith; William P Horan; Dominique Demolle; Peter Schueler; Dong-Jing Fu; Ariana E Anderson; Joseph Geraci; Florence Butlen-Ducuing; Jasmine Link; Ni A Khin; Robert Morlock; Larry D Alphs Journal: Innov Clin Neurosci Date: 2022 Jan-Mar
Authors: Sigal Zilcha-Mano; Meredith L Wallace; Patrick J Brown; Joel Sneed; Steven P Roose; Bret R Rutherford Journal: Transl Psychiatry Date: 2021-09-15 Impact factor: 7.989
Authors: Bradley S Peterson; Amy E West; John R Weisz; Wendy J Mack; Michele D Kipke; Robert L Findling; Brian S Mittman; Ravi Bansal; Steven Piantadosi; Glenn Takata; Corinna Koebnick; Ceth Ashen; Christopher Snowdy; Marie Poulsen; Bhavana Kumar Arora; Courtney M Allem; Marisa Perez; Stephanie N Marcy; Bradley O Hudson; Stephanie H Chan; Robin Weersing Journal: BMC Psychiatry Date: 2021-06-30 Impact factor: 3.630