Dose-effect meta-analysis for psychopharmacological interventions using randomised data.

Objective: The current practice in meta-analysis of the effects of psychopharmacological interventions ignors the administered dose or restricts the analysis in a dose range. This may introduce unnecessary uncertainty and heterogeneity. Methods have been developed to integrate the dose-effect models in meta-analysis.

Methods: We describe the two-stage and the one-stage models to conduct a dose-effect meta-analysis using common or random effects methods. We illustrate the methods on a dataset of selective serotonin reuptake inhibitor antidepressants. The dataset comprises 60 randomised controlled trials. The dose-effect is measured on an odds ratio scale and is modelled using restricted cubic splines to detect departure from linearity.

Results: The estimated summary curve indicates that the probability of response increases up to 30 mg/day of fluoxetine-equivalent which results in reaching 50% probability to respond. Beyond 40 mg/day, no further increase in the response is observed. The one-stage model includes all studies, resulting in slightly less uncertainty than the two-stage model where only part of the data is analysed.

Conclusions: The dose-effect meta-analysis enables clinicians to understand how the effect of a drug changes as a function of its dose. Such analysis should be conducted in practice using the one-stage model that incorporates evidence from all available studies.