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ABSTRACT: Background: Cannabis use has been identified as a potent predictor of the earlier onset of psychosis, but meta-analysis has not indicated that it has a clear effect in established psychosis. Aim: To assess the association between cannabis and outcomes, including whether change in cannabis use affects symptoms and functioning, in a large sample of people with established nonaffective psychosis and comorbid substance misuse. Methods: One hundred and sixty participants whose substance use included cannabis were compared with other substance users (n = 167) on baseline demographic, clinical, and substance use variables. The cannabis using subgroup was examined prospectively with repeated measures of substance use and psychopathology at baseline, 12 months, and 24 months. We used generalized estimating equation models to estimate the effects of cannabis dose on subsequent clinical outcomes and whether change in cannabis use was associated with change in outcomes. Results: Cannabis users showed cross-sectional differences from other substances users but not in terms of positive symptoms. Second, cannabis dose was not associated with subsequent severity of positive symptoms and change in cannabis dose did not predict change in positive symptom severity, even when patients became abstinent. However, greater cannabis exposure was associated with worse functioning, albeit with a small effect size. Conclusions: We did not find evidence of an association between cannabis dose and psychotic symptoms, although greater cannabis dose was associated with worse psychosocial functioning, albeit with small effect size. It would seem that within this population, not everyone will demonstrate durable symptomatic improvements from reducing cannabis.
Schizophrenia Bulletin 10/2011; · 8.80 Impact Factor
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ABSTRACT: The importance of therapeutic alliance in predicting treatment outcomes is well established, but less is known about client characteristics that predict alliance. Clients with co-occurring psychosis and substance misuse (n = 116) who received integrated motivational interviewing and cognitive behavior therapy in the context of a large randomized controlled trial completed the Working Alliance Inventory. Their trial therapists also completed Working Alliance Inventories. Rating perspectives were compared, and in a cross-sectional study, client predictors of therapeutic alliance were examined. As hypothesized, clients' negative attitudes to treatment, including lack of insight, were predictive of poorer alliance. Therapist-rated alliance was also predicted by the client's attitude to medication, self-reported depression, and living situation. Symptom severity and substance use measures were unrelated to alliance. Consistent with previous studies, rating perspectives differed, with clients rating alliance more positive than therapists.
The Journal of nervous and mental disease 05/2010; 198(5):373-7. · 1.77 Impact Factor
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ABSTRACT: Complex intervention trials should be able to answer both pragmatic and explanatory questions in order to test the theories motivating the intervention and help understand the underlying nature of the clinical problem being tested. Key to this is the estimation of direct effects of treatment and indirect effects acting through intermediate variables which are measured post-randomisation. Using psychological treatment trials as an example of complex interventions, we review statistical methods which crucially evaluate both direct and indirect effects in the presence of hidden confounding between mediator and outcome. We review the historical literature on mediation and moderation of treatment effects. We introduce two methods from within the existing causal inference literature, principal stratification and structural mean models, and demonstrate how these can be applied in a mediation context before discussing approaches and assumptions necessary for attaining identifiability of key parameters of the basic causal model. Assuming that there is modification by baseline covariates of the effect of treatment (i.e. randomisation) on the mediator (i.e. covariate by treatment interactions), but no direct effect on the outcome of these treatment by covariate interactions leads to the use of instrumental variable methods. We describe how moderation can occur through post-randomisation variables, and extend the principal stratification approach to multiple group methods with explanatory models nested within the principal strata. We illustrate the new methodology with motivating examples of randomised trials from the mental health literature.
Statistical Methods in Medical Research 08/2009; 19(3):237-70. · 2.44 Impact Factor
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ABSTRACT: This article describes the implementation of a double-robust estimator for pretest-posttest studies (Lunceford and Davidian, 2004, Statistics in Medicine 23: 2937-2960) and presents a new Stata command (dr) that carries out the procedure. A double-robust estimator gives the analyst two opportunities for obtaining unbiased inference when adjusting for selection effects such as confounding by allowing for different forms of model misspecification; a double-robust estimator also can offer increased efficiency when all the models are correctly specified. We demonstrate the results with a Monte Carlo simulation study, and we show how to implement the double-robust estimator on a single simulated dataset, both manually and by using the dr command. Copyright 2008 by StataCorp LP.
Stata Journal 01/2008; 8(3):334-353. · 2.22 Impact Factor
