Hostname: page-component-7c8c6479df-fqc5m Total loading time: 0 Render date: 2024-03-28T20:31:56.168Z Has data issue: false hasContentIssue false

The different trajectories of antipsychotic response: antipsychotics versus placebo

Published online by Cambridge University Press:  20 October 2010

T. R. Marques
Affiliation:
Institute of Psychiatry, King's College London, London, UK
T. Arenovich
Affiliation:
Centre for Addiction and Mental Health, Toronto, Canada
O. Agid
Affiliation:
Centre for Addiction and Mental Health, Toronto, Canada
G. Sajeev
Affiliation:
Centre for Addiction and Mental Health, Toronto, Canada
B. Muthén
Affiliation:
Graduate School of Education and Information, UCLA, Los Angeles, CA, USA
L. Chen
Affiliation:
Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA
B. J. Kinon
Affiliation:
Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN, USA
S. Kapur*
Affiliation:
Institute of Psychiatry, King's College London, London, UK
*
*Address for correspondence: S. Kapur, M.B.B.S., Ph.D., F.R.C.P.C., Dean and Professor, PO Box 053, Institute of Psychiatry, King's College London, De Crespigny Park, London SE5 8AF, UK. (Email: shitij.kapur@kcl.ac.uk)

Abstract

Background

It is generally accepted that antipsychotics are more effective than placebo. However, it remains unclear whether antipsychotics induce a pattern or trajectory of response that is distinct from placebo. We used a data-driven technique, called growth mixture modelling (GMM), to identify the different patterns of response observed in antipsychotic trials and to determine whether drug-treated and placebo-treated subjects show similar or distinct patterns of response.

Method

We examined data on 420 patients with schizophrenia treated for 6 weeks in two double-blind placebo-controlled trials using haloperidol and olanzapine. We used GMM to identify the optimal number of response trajectories; to compare the trajectories in drug-treated versus placebo-treated patients; and to determine whether the trajectories for the different dimensions (positive versus negative symptoms) were identical or different.

Results

Positive symptoms were found to respond along four distinct trajectories, with the two most common trajectories (‘Partial responder’ and ‘Responder’) accounting for 70% of the patients and seen proportionally in both drug- and placebo-treated. The most striking drug–placebo difference was in the ‘Dramatic responders’, seen only among the drug-treated. The response of negative symptoms was more modest and did not show such distinct trajectories.

Conclusions

Trajectory models of response, rather than the simple responder/non-responder dichotomy, provide a better statistical account of how antipsychotics work. The ‘Dramatic responders’ (those showing >70% response) were seen only among the drug-treated and make a significant contribution to the overall drug–placebo difference. Identifying and studying this subset may provide specific insight into antipsychotic action.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2010

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.)

References

Agid, O, Kapur, S, Arenovich, T, Zipursky, RB (2003). Delayed-onset hypothesis of antipsychotic action: a hypothesis tested and rejected. Archives of General Psychiatry 60, 12281235.CrossRefGoogle Scholar
Agid, O, Potkin, S, Remington, G, Kapur, S, Watsky, E, Vanderburg, D, Siu, C (2010). Meta-analysis of placebo response in antipsychotic trials. APA Annual Meeting, New Orleans, May 2010.Google Scholar
Beasley, CM Jr., Sanger, T, Satterlee, W, Tollefson, G, Tran, P, Hamilton, S (1996 a). Olanzapine versus placebo: results of a double-blind, fixed-dose olanzapine trial. Psychopharmacology (Berlin) 124, 159167.Google Scholar
Beasley, CM Jr., Tollefson, G, Tran, P, Satterlee, W, Sanger, T, Hamilton, S (1996 b). Olanzapine versus placebo and haloperidol: acute phase results of the North American double-blind olanzapine trial. Neuropsychopharmacology 14, 111123.CrossRefGoogle ScholarPubMed
Garver, DL, Holcomb, JA, Christensen, JD (2000). Heterogeneity of response to antipsychotics from multiple disorders in the schizophrenia spectrum. Journal of Clinical Psychiatry 61, 964972; quiz 973.CrossRefGoogle ScholarPubMed
Kapur, S, Arenovich, T, Agid, O, Zipursky, R, Lindborg, S, Jones, B (2005). Evidence for onset of antipsychotic effects within the first 24 hours of treatment. American Journal of Psychiatry 162, 939946.Google Scholar
Kinon, BJ, Chen, L, Ascher-Svanum, H, Stauffer, VL, Kollack-Walker, S, Sniadecki, JL, Kane, JM (2008). Predicting response to atypical antipsychotics based on early response in the treatment of schizophrenia. Schizophrenia Research 102, 230240.Google Scholar
Leucht, S, Busch, R, Hamann, J, Kissling, W, Kane, JM (2005). Early-onset hypothesis of antipsychotic drug action: a hypothesis tested, confirmed and extended. Biological Psychiatry 57, 15431549.Google Scholar
Leucht, S, Kane, JM, Etschel, E, Kissling, W, Hamann, J, Engel, RR (2006). Linking the PANSS, BPRS, and CGI: clinical implications. Neuropsychopharmacology 31, 23182325.Google Scholar
Levine, SZ, Rabinowitz, J (2008). Trajectories and antecedents of treatment response over time in early-episode psychosis. Schizophrenia Bulletin 36, 624632.CrossRefGoogle ScholarPubMed
Muthén, B, Brown, HC (2009). Estimating drug effects in the presence of placebo response: causal inference using growth mixture modeling. Statistics in Medicine 28, 33633385.Google Scholar
Muthén, B, Brown, CH, Leuchter, A, Hunter, A (in press). General approaches to analysis of course: applying growth mixture modeling to randomized trials of depression medication. In Causality and Psychopathology: Finding the Determinants of Disorders and their Cures (ed. Shrout, P. E.). American Psychiatric Publishing: Washington, DC.Google Scholar
Muthén, B, Brown, CH, Masyn, K, Jo, B, Khoo, ST, Yang, CC, Wang, CP, Kellam, S, Carlin, J, Liao, J (2002). General growth mixture modeling for randomized preventive interventions. Biostatistics (Oxford, England) 3, 459475.CrossRefGoogle ScholarPubMed
Muthén, B, Muthén, L (2007). Mplus: Statistical Analysis with Latent Variables: User's Guide, 5th edn. Muthén and Muthén: Los Angeles, CA.Google Scholar
Muthén, B, Shedden, K (1999). Finite mixture modeling with mixture outcomes using the EM algorithm. Biometrics 55, 463469.CrossRefGoogle ScholarPubMed
Overall, JE, Gorham, DR (1962). The Brief Psychiatric Rating Scale. Psychological Reports 10, 799812.CrossRefGoogle Scholar
Quitkin, FM, Rabkin, JG, Ross, D, Stewart, JW (1984). Identification of true drug response to antidepressants. Use of pattern analysis. Archives of General Psychiatry 41, 782786.Google Scholar
Rothschild, R, Quitkin, FM (1992). Review of the use of pattern analysis to differentiate true drug and placebo responses. Psychotherapy and Psychosomatics 58, 170177.CrossRefGoogle ScholarPubMed
Stassen, HH, Angst, J, Hell, D, Scharfetter, C, Szegedi, A (2007). Is there a common resilience mechanism underlying antidepressant drug response? Evidence from 2848 patients. Journal of Clinical Psychiatry 68, 11951205.CrossRefGoogle Scholar
Stassen, HH, Delini-Stula, A, Angst, J (1993). Time course of improvement under antidepressant treatment: a survival-analytical approach. European Neuropsychopharmacology 3, 127135.CrossRefGoogle ScholarPubMed
Stewart, JW, Quitkin, FM, McGrath, PJ, Amsterdam, J, Fava, M, Fawcett, J, Reimherr, F, Rosenbaum, J, Beasley, C, Roback, P (1998). Use of pattern analysis to predict differential relapse of remitted patients with major depression during 1 year of treatment with fluoxetine or placebo. Archives General Psychiatry 55, 334343.Google Scholar