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Although randomised controlled trials are regarded as the gold standard for treatments efficacy, evidence from observational studies remains relevant. To address the problem of possible confounding in these studies, investigators must employ analysis methods that adjust for confounders and lead to an unbiased estimation of the treatment effect. In this paper, the authors describe two relevant statistical methods. The first method represents the classical approach consisting of a multiple regression model including the effects of treatment and covariates. This approach considers the relation between prognostic factors and the outcome variable as a relevant criterion for adjustment. The second method is based on the propensity score, and focuses on the relation between prognostic factors and treatment assignment. These approaches were applied to a cohort of 183 French schizophrenic patients who were followed for a 2-year period (from 1998 to 2000). The probability of relapse according to antipsychotic treatment exposure was modelled using Cox regression models with the two statistical methods. Goodness-of-fit criteria were used to compare the modelling approaches. This study demonstrates that the propensity score, a predicted probability, has an important balancing property that underscores its value in strengthening the results of nonrandomised observational studies.

Objective: To compare the rate of relapse as a function of antipsychotic treatment (monotherapy vs. polypharmacy) in schizophrenic patients over a 2-year period., Methods: Using data from a multicenter cohort study conducted in France, we performed a propensity-adjusted analysis to examine the association between the rate of relapse over a 2-year period and antipsychotic treatment (monotherapy vs. polypharmacy)., Results: Our sample consisted in 183 patients; 50 patients (27.3%) had at least one period of relapse and 133 had no relapse (72.7%). Thirty-eight (37.7) percent of the patients received polypharmacy. The most severely ill patients were given polypharmacy: the age at onset of illness was lower in the polypharmacy group (p = 0.03). Patients that received polypharmacy also presented a higher general psychopathology PANSS subscore (p = 0.04) but no statistically significant difference was found in the PANSS total score or the PANSS positive or negative subscales. These patients were more likely to be given prescriptions for sedative drugs (p < 0.01) and antidepressant medications (p = 0.03). Relapse was found in 23.7% of patients given monotherapy and 33.3% given polypharmacy (p = 0.16). After stratification according to quintiles of the propensity score, which eliminated all significant differences for baseline characteristics, antipsychotic polypharmacy was not statistically associated with an increase of relapse: HR = 1.686 (0.812; 2.505)., Conclusion: After propensity score adjustment, antipsychotic polypharmacy is not statistically associated to an increase of relapse. Future randomised studies are needed to assess the impact of antipsychotic polypharmacy in schizophrenia.