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-blindplacebo-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.
RCT Entities:
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.
Authors: Rebecca Schennach; Hans-Jürgen Möller; Michael Obermeier; Florian Seemüller; Markus Jäger; Max Schmauss; Gerd Laux; Herbert Pfeiffer; Dieter Naber; Lutz G Schmidt; Wolfgang Gaebel; Joachim Klosterkötter; Isabella Heuser; Wolfgang Maier; Matthias R Lemke; Eckart Rüther; Stefan Klingberg; Markus Gastpar; Richard Musil; Ilja Spellmann; Michael Riedel Journal: Int J Methods Psychiatr Res Date: 2015-07-14 Impact factor: 4.035
Authors: E Sacchetti; C Magri; A Minelli; P Valsecchi; M Traversa; S Calza; A Vita; M Gennarelli Journal: Pharmacogenomics J Date: 2016-02-09 Impact factor: 3.550
Authors: William G Honer; Andrea A Jones; Allen E Thornton; Alasdair M Barr; Ric M Procyshyn; Fidel Vila-Rodriguez Journal: Can J Psychiatry Date: 2015-03 Impact factor: 4.356
Authors: Richard A Van Dorn; Sarah L Desmarais; Stephen J Tueller; Jennifer M Jolley; Kiersten L Johnson; Marvin S Swartz Journal: Schizophr Res Date: 2013-05-29 Impact factor: 4.939
Authors: M Case; V L Stauffer; H Ascher-Svanum; R Conley; S Kapur; J M Kane; S Kollack-Walker; J Jacob; B J Kinon Journal: Psychol Med Date: 2010-10-07 Impact factor: 7.723