PURPOSE: In the 2004, FDA placed a black box warning on antidepressants for risk of suicidal thoughts and behavior in children and adolescents. The purpose of this paper is to examine the risk of suicide attempt and self-inflicted injury in depressed children ages 5-17 treated with antidepressants in two large observational datasets taking account time-varying confounding. METHODS: We analyzed two large US medical claims databases (MarketScan and LifeLink) containing 221,028 youth (ages 5-17) with new episodes of depression, with and without antidepressant treatment during the period of 2004-2009. Subjects were followed for up to 180 days. Marginal structural models were used to adjust for time-dependent confounding. RESULTS: For both datasets, significantly increased risk of suicide attempts and self-inflicted injury were seen during antidepressant treatment episodes in the unadjusted and simple covariate adjusted analyses. Marginal structural models revealed that the majority of the association is produced by dynamic confounding in the treatment selection process; estimated odds ratios were close to 1.0 consistent with the unadjusted and simple covariate adjusted association being a product of chance alone. CONCLUSIONS: Our analysis suggests antidepressant treatment selection is a product of both static and dynamic patient characteristics. Lack of adjustment for treatment selection based on dynamic patient characteristics can lead to the appearance of an association between antidepressant treatment and suicide attempts and self-inflicted injury among youths in unadjusted and simple covariate adjusted analyses. Marginal structural models can be used to adjust for static and dynamic treatment selection processes such as that likely encountered in observational studies of associations between antidepressant treatment selection, suicide and related behaviors in youth.
PURPOSE: In the 2004, FDA placed a black box warning on antidepressants for risk of suicidal thoughts and behavior in children and adolescents. The purpose of this paper is to examine the risk of suicide attempt and self-inflicted injury in depressedchildren ages 5-17 treated with antidepressants in two large observational datasets taking account time-varying confounding. METHODS: We analyzed two large US medical claims databases (MarketScan and LifeLink) containing 221,028 youth (ages 5-17) with new episodes of depression, with and without antidepressant treatment during the period of 2004-2009. Subjects were followed for up to 180 days. Marginal structural models were used to adjust for time-dependent confounding. RESULTS: For both datasets, significantly increased risk of suicide attempts and self-inflicted injury were seen during antidepressant treatment episodes in the unadjusted and simple covariate adjusted analyses. Marginal structural models revealed that the majority of the association is produced by dynamic confounding in the treatment selection process; estimated odds ratios were close to 1.0 consistent with the unadjusted and simple covariate adjusted association being a product of chance alone. CONCLUSIONS: Our analysis suggests antidepressant treatment selection is a product of both static and dynamic patient characteristics. Lack of adjustment for treatment selection based on dynamic patient characteristics can lead to the appearance of an association between antidepressant treatment and suicide attempts and self-inflicted injury among youths in unadjusted and simple covariate adjusted analyses. Marginal structural models can be used to adjust for static and dynamic treatment selection processes such as that likely encountered in observational studies of associations between antidepressant treatment selection, suicide and related behaviors in youth.
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Authors: G M Goodwin; P M Haddad; I N Ferrier; J K Aronson; Trh Barnes; A Cipriani; D R Coghill; S Fazel; J R Geddes; H Grunze; E A Holmes; O Howes; S Hudson; N Hunt; I Jones; I C Macmillan; H McAllister-Williams; D R Miklowitz; R Morriss; M Munafò; C Paton; B J Saharkian; Kea Saunders; Jma Sinclair; D Taylor; E Vieta; A H Young Journal: J Psychopharmacol Date: 2016-03-15 Impact factor: 4.153