a1 Health Sciences Research Group, School of Community-Based Medicine, University of Manchester, UK
a2 School of Medicine, Health Policy and Practice, University of East Anglia, Norfolk, UK
a3 Norfolk and Waveney Mental Health Partnership Trust, UK
a4 Department of Psychology, Institute of Psychiatry, King's College London, UK
a5 Department of Mental Health Sciences, UCL, London, UK
a6 Department of Psychology, University of Reading, UK
Background Meta-analyses show that cognitive behaviour therapy for psychosis (CBT-P) improves distressing positive symptoms. However, it is a complex intervention involving a range of techniques. No previous study has assessed the delivery of the different elements of treatment and their effect on outcome. Our aim was to assess the differential effect of type of treatment delivered on the effectiveness of CBT-P, using novel statistical methodology.
Method The Psychological Prevention of Relapse in Psychosis (PRP) trial was a multi-centre randomized controlled trial (RCT) that compared CBT-P with treatment as usual (TAU). Therapy was manualized, and detailed evaluations of therapy delivery and client engagement were made. Follow-up assessments were made at 12 and 24 months. In a planned analysis, we applied principal stratification (involving structural equation modelling with finite mixtures) to estimate intention-to-treat (ITT) effects for subgroups of participants, defined by qualitative and quantitative differences in receipt of therapy, while maintaining the constraints of randomization.
Results Consistent delivery of full therapy, including specific cognitive and behavioural techniques, was associated with clinically and statistically significant increases in months in remission, and decreases in psychotic and affective symptoms. Delivery of partial therapy involving engagement and assessment was not effective.
Conclusions Our analyses suggest that CBT-P is of significant benefit on multiple outcomes to patients able to engage in the full range of therapy procedures. The novel statistical methods illustrated in this report have general application to the evaluation of heterogeneity in the effects of treatment.
(Received January 31 2011)
(Revised August 15 2011)
(Accepted August 30 2011)
(Online publication September 23 2011)
c1 Address for correspondence: Professor G. Dunn, Health Sciences Methodology, 1st Floor, Jean McFarlane Building, University Place, Oxford Road, Manchester M13 9PL, UK. (Email: firstname.lastname@example.org)
† These authors contributed equally to this work.