Hostname: page-component-65b85459fc-2g6tz Total loading time: 0 Render date: 2025-10-15T05:36:03.492Z Has data issue: false hasContentIssue false
  • English
  • Français

Optimizing patient expectancy in the pharmacologic treatment of major depressive disorder

Published online by Cambridge University Press:  13 November 2018

Sigal Zilcha-Mano ,
Patrick J. Brown ,
Steven P. Roose ,
Kiley Cappetta  and
Bret R. Rutherford
Sigal Zilcha-Mano*
Affiliation:
Department of Psychology, University of Haifa, Mount Carmel, Haifa 31905, Israel
Patrick J. Brown
Affiliation:
Columbia University College of Physicians and Surgeons, New York State Psychiatric Institute, New York, NY, USA
Steven P. Roose
Affiliation:
Columbia University College of Physicians and Surgeons, New York State Psychiatric Institute, New York, NY, USA
Kiley Cappetta
Affiliation:
Columbia University College of Physicians and Surgeons, New York State Psychiatric Institute, New York, NY, USA
Bret R. Rutherford
Affiliation:
Columbia University College of Physicians and Surgeons, New York State Psychiatric Institute, New York, NY, USA
*
Author for correspondence: Sigal Zilcha-Mano, E-mail: sigalzil@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

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.

Keywords

expectancymajor depressive disorderplacebo effects

Information

Type
Original Articles
Information
Psychological Medicine , Volume 49 , Issue 14 , October 2019 , pp. 2414 - 2420
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Bolger, N and Laurenceau, JP (2013) Intensive Longitudinal Methods. New York, NY: Guilford.Google Scholar
Borkovec, TD (1972) Effects of expectancy on the outcome of systematic desensitization and implosive treatments for analogue anxiety. Behavior Therapy 3, 29–40.Google Scholar
Borkovec, TD and Nau, SD (1972) Credibility of analogue therapy rationales. Journal of Behavior Therapy and Experimental Psychiatry 3, 257–260.Google Scholar
Cohen, ZD and DeRubeis, RJ (2018) Treatment selection in depression. Annual Review of Clinical Psychology 14, 209–236.Google Scholar
Constantino, MJ, Vîslă, A, Coyne, AE and Boswell, JF (2018) A meta-analysis of the association between patients' early treatment outcome expectation and their posttreatment outcomes. Psychotherapy 55, 473–485.Google Scholar
Curran, PJ and Bauer, DJ (2011) The disaggregation of within-person and between-person effects in longitudinal models of change. Annual Review of Psychology 62, 583–619.Google Scholar
Faria, V, Gingnell, M, Hoppe, JM, Hjorth, O, Alaie, I, Frick, A, Hultberg, S, Wahlstedt, K, Engman, J and Månsson, KNT (2017) Do you believe it? Verbal suggestions influence the clinical and neural effects of escitalopram in social anxiety disorder: a randomized trial. EBioMedicine 24, 179–188.Google Scholar
Fawcett, J, Epstein, P, Fiester, SJ, Elkin, I and Autry, JH (1987) Clinical management-imipramine/placebo administration manual. Psychopharmacology Bulletin 23, 309–324.Google Scholar
Greenberg, RP, Bornstein, RF, Greenberg, MD and Fisher, S (1992) A meta-analysis of antidepressant outcome under ‘blinder’ conditions. Journal of Consulting and Clinical Psychology 60, 664.Google Scholar
Hothorn, T, Hornik, K, Van De Wiel, MA and Zeileis, A (2006a). A Lego system for conditional inference. The American Statistician 60, 257–263.Google Scholar
Hothorn, T, Hornik, K and Zeileis, A (2006b). Unbiased recursive partitioning: a conditional inference framework. Journal of Computational and Graphical Statistics 15, 651–674.Google Scholar
Iida, M, Seidman, G and Shrout, PE (2018) Models of interdependent individuals versus dyadic processes in relationship research. Journal of Social and Personal Relationships 35, 59–88.Google Scholar
Khan, A, Kolts, RL, Thase, ME, Krishnan, KRR and Brown, W (2004) Research design features and patient characteristics associated with the outcome of antidepressant clinical trials. American Journal of Psychiatry 161, 2045–2049.Google Scholar
Khan, A, Redding, N and Brown, WA (2008) The persistence of the placebo response in antidepressant clinical trials. Journal of Psychiatric Research 42, 791–796.Google Scholar
Kirsch, I and Sapirstein, G (1998) Listening to Prozac but hearing placebo: a meta-analysis of antidepressant medication. Prevention & Treatment 1, 2a.Google Scholar
Kirsch, I, Deacon, BJ, Huedo-Medina, TB, Scoboria, A, Moore, TJ and Johnson, BT (2008) Initial severity and antidepressant benefits: a meta-analysis of data submitted to the Food and Drug Administration. PLoS Medicine 5, e45.Google Scholar
Krull, JL and MacKinnon, DP (2001) Multilevel modeling of individual and group level mediated effects. Multivariate Behavioral Research 36, 249–277.Google Scholar
Laurenceau, J-P and Bolger, N (2012) Analyzing diary and intensive longitudinal data from dyads. In Mehl M and Conner T (eds), Handbook of Research Methods for Studying Daily Life. New York, NY: Guilford, pp. 407–422.Google Scholar
Rutherford, BR and Roose, SP (2013) A model of placebo response in antidepressant clinical trials. American Journal of Psychiatry 170, 723–733.Google Scholar
Rutherford, BR, Sneed, JR and Roose, SP (2009) Does study design influence outcome? Psychotherapy and Psychosomatics 78, 172–181.Google Scholar
Rutherford, BR, Wall, MM, Brown, PJ, Choo, TH, Wager, TD, Peterson, BS, Chung, S, Kirsch, I and Roose, SP (2017) Patient expectancy as a mediator of placebo effects in antidepressant clinical trials. American Journal of Psychiatry 174, 135–142.Google Scholar
SAS SA (2003) Guide SU Version 9.1. SAS Institute Inc., Cary, NC.Google Scholar
Sinyor, M, Levitt, AJ, Cheung, AH, Schaffer, A, Kiss, A, Dowlati, Y and Lanctôt, KL (2010) Does inclusion of a placebo arm influence response to active antidepressant treatment in randomized controlled trials? Results from pooled and meta-analyses.Google Scholar
Sneed, JR, Rutherford, BR, Rindskopf, D, Lane, DT, Sackeim, HA and Roose, SP (2008) Design makes a difference: a meta-analysis of antidepressant response rates in placebo-controlled versus comparator trials in late-life depression. The American Journal of Geriatric Psychiatry 16, 65–73.Google Scholar
SPSS, I (2007) SPSS version 16.0. Chicago, IL: SPSS Incorporated.Google Scholar
Stewart-Williams, S and Podd, J (2004) The placebo effect: dissolving the expectancy versus conditioning debate. Psychological Bulletin 130, 324.Google Scholar
Strasser, H and Weber, C (1999) On the asymptotic theory of permutation statistics. SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business.Google Scholar
Strobl, C, Malley, J and Tutz, G (2009) An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological Methods 14, 323.Google Scholar
Walsh, BT, Seidman, SN, Sysko, R and Gould, M (2002) Placebo response in studies of major depression: variable, substantial, and growing. JAMA 287, 1840–1847.Google Scholar
Wang, LP and Maxwell, SE (2015) On disaggregating between-person and within-person effects with longitudinal data using multilevel models. Psychological Methods 20, 63.Google Scholar
Zilcha-Mano, S (2016) New analytic strategies help answer the controversial question of whether alliance is therapeutic in itself. World Psychiatry 15, 84–85.Google Scholar
Zilcha-Mano, S (2017) Is the alliance really therapeutic? Revisiting this question in light of recent methodological advances. American Psychologist 72, 311.Google Scholar
Zilcha-Mano, S, Roose, SP, Brown, PJ and Rutherford, BR (2018) A machine learning approach to identifying placebo responders in late-life depression trials. The American Journal of Geriatric Psychiatry 26, 669–677.Google Scholar