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Journal Article


Hayes JF, Lewis G, Lewis G. Psychother. Psychosom. 2019; ePub(ePub): ePub.


Division of Psychiatry, University College London, London, United Kingdom.


(Copyright © 2019, Karger Publishers)






We were interested in the letter by Hengartner and Plöderl [1] which was covered by the press in the United Kingdom. The issue of potential suicide risk in relation to antidepressant treatment is very important and we agree that the evidence for this is somewhat mixed [2, 3] and limited by low statistical power. Current guidelines recommend that clinicians should closely monitor patients for suicidal behaviour on initiation of treatment [4], particularly those under the age of 25 [2]. We agree with Hengartner and Plöderl [1] that the analysis by Khan et al. [5] is probably flawed because of the longer follow-up time in the antidepressant-exposed group, and the time-varying hazards of suicide and suicide attempts. There is some evidence that the possible increased risk for suicidality is still present after 2 weeks of SSRI use, but the main hypothesis suggests that the earlier effects of antidepressants are those that might lead to suicidal behaviour [6].

However, the analysis conducted by Hengartner and Plöderl [1] pooled data across all studies and this can potentially introduce bias and produce a misleading result [7]. A more statistically robust approach to aggregating data from different studies is to conduct a meta-analysis which produces an average of the results of each trial rather than simply pooling the results. We carried out meta-analyses of the data presented in table 1 of Hengartner and Plöderl’s [1] letter using the admetan and bayesmh commands in Stata 15. We took a number of different analytical approaches: the DerSimonian and Laird model with 0.5 continuity correction, the inverse variance heterogeneity model with 0.5 continuity correction (both methods potentially lead to excess bias in the estimated effects and spuriously narrow CIs when outcomes are rare [8]), the Peto one-step OR model (the method recommended by Cochrane for rare events ...

Language: en


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