Estimating transitions between symptom severity states over time in schizophrenia: a Bayesian meta-analytic approach

We obtain the posterior predictive distribution of transition probabilities between symptom severity states over time for patients with schizophrenia by (i) employing a Bayesian meta-analysis of published clinical trials and observational studies to estimate the posterior distribution of parameters that guide changes in Positive and Negative Syndrome Scale (PANSS) scores over time and under the influence of various drugs and (ii) by propagating the variability from the posterior distributions of the parameters through a micro-simulation model that is formulated based on schizophrenia progression. Results show detailed differences among haloperidol, risperidone and olanzapine in controlling various levels of severities of positive, negative and joint symptoms over time. For example, risperidone seems best in controlling severe positive symptoms while olanzapine is the worst in that during the first quarter of drug treatment; however, olanzapine seems to be best in controlling severe negative symptoms across all four quarters of treatment while haloperidol is the worst in this regard. These details may further serve to better estimate quality of life of patients and aid in resource utilization decisions in treating schizophrenic patients. In addition, consistent estimation of uncertainty in the time-profile parameters also has important implications for the practice of cost-effectiveness analysis and for future resource allocation policies in schizophrenia treatment.