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, 3 (2), 73-90; discussion 91-8

Do Antidepressants Cause Suicidality in Children? A Bayesian Meta-Analysis


Do Antidepressants Cause Suicidality in Children? A Bayesian Meta-Analysis

Eloise E Kaizar et al. Clin Trials.


Background: To quantify the risk of suicidal behavior/ideation (suicidality) for children who use antidepressants, the FDA collected randomized placebo-controlled trials of antidepressant efficacy in children. Although none of the 4487 children completed suicide, 1.7% exhibited suicidality. The FDA meta-analyzed these studies and found sufficient evidence of an increased risk to require a black-box warning on antidepressants for children.

Purpose: The FDA considered different drug formulations and psychiatric diagnoses to be equivalent in their effect on suicidality. If this assumption does not hold, the FDA analysis may have underestimated the variance of the risk estimate. We investigate the consequences of relaxing these assumptions.

Methods: We extend the FDA analysis using a Bayesian hierarchical model that allows for a study-level component of variability and facilitates extensive sensitivity analyses.

Results: We found an association between antidepressant use and an increased risk of suicidality in studies where the diagnosis was major depressive disorder (odds ratio 2.3 [1.3, 3.8]), and where the antidepressant was an SSRI (odds ratio 2.2 [1.3, 3.6]). We did not find evidence for such an association in the complement sets of trials. Although the results based on the hierarchical model are insensitive to model perturbations, the robustness of the FDA's meta-analysis to model assumptions is less clear. These data have limited generalizability due to exclusion of patients with baseline risk of suicide and the use of relatively short duration trials.

Conclusions: Because of model specification and interpretation issues raised in this paper, we conclude that the evidence supporting a causal link between antidepressant use and suicidality in children is weak. The use of Bayesian hierarchical models for meta-analysis has facilitated the incorporation of potentially important sources of variability and the use of sensitivity analysis to assess the consequences of model specifications and their impact on important regulatory decisions.

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