Initial Severity and Antidepressant Benefits: A Meta-Analysis of Data Submitted to the Food and Drug Administration

By Irving Kirsch, B. Deacon, Tania B. Huedo-Medina, A. Scoboria, T. Moore, Blair T. Johnson. PLoS Medicine, 2008

2,421 citations recorded by the source corpus.

Background Meta-analyses of antidepressant medications have reported only modest benefits over placebo treatment, and when unpublished trial data are included, the benefit falls below accepted criteria for clinical significance. Yet, the efficacy of the antidepressants may also depend on the severity of initial depression scores. The purpose of this analysis is to establish the relation of baseline severity and antidepressant efficacy using a relevant dataset of published and unpublished clinical trials. Methods and Findings We obtained data on all clinical trials submitted to the US Food and Drug Administration (FDA) for the licensing of the four new-generation antidepressants for which full datasets were available. We then used meta-analytic techniques to assess linear and quadratic effects of initial severity on improvement scores for drug and placebo groups and on drug–placebo difference scores. Drug–placebo differences increased as a function of initial severity, rising from virtually no difference at moderate levels of initial depression to a relatively small difference for patients with very severe depression, reaching conventional criteria for clinical significance only for patients at the upper end of the very severely depressed category. Meta-regression analyses indicated that the relation of baseline severity and improvement was curvilinear in drug groups and showed a strong, negative linear component in placebo groups. Conclusions Drug–placebo differences in antidepressant efficacy increase as a function of baseline severity, but are relatively small even for severely depressed patients. The relationship between initial severity and antidepressant efficacy is attributable to decreased responsiveness to placebo among very severely depressed patients, rather than to increased responsiveness to medication.

Subfields: Cognitive Neuroscience, Pharmacology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1d1f0a4ec8337f818f1a1183da816ac19693fc16

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Citations in the corpus

Suggestion, Cognition, and Behavior (2012, background): “At other times, suggestion’s effects are surprisingly strong, rivaling the effects of active medication and surgical procedures (Kaptchuk et al., 2008; Kirsch et al., 2008; Moseley et al., 2002).”
Depression sum-scores don’t add up: why analyzing specific depression symptoms is essential (2015, background): “Several large meta-analyses of clinical trials have demonstrated that antidepressants outperform placebos in less than half of the trials, and that clinically relevant improvements can be documented only for a minority of severely depressed patients [32-34].”
Evidence map of crop diversification strategies at the global scale (2019, methodology): “Low-quality meta-analyses are strongly criticized (Kirsch et al 2008, Pullin and Knight 2012, Ioannidis 2016) and negatively impact the image of usefulness of such method (Whittaker 2010).”
Depression is not a consistent syndrome: an investigation of unique symptom patterns in the STAR*D study (2014, neutral): “, 2012), and the low efficacy of antidepressants in clinical trials (Kirsch et al., 2008; Pigott et al., 2010).”
Handbook of cognitive-behavioral therapies (2009, neutral): “This question may become moot, however, because a recent meta- analysis found virtually no difference in treatment outcome between patients with moderate levels of initial depression who received a pill- placebo and those who received antidepressants, and relatively small effects for initially sever”
Transparency, Reproducibility, and the Credibility of Economics Research (2018, positive): “…et al. (1991) reviewed the universe of protocols submitted to the Central Oxford Research Ethics Committee, and both Turner et al. (2008) and Kirsch et al. (2008) employ the universe of tests of certain anti-depressant drugs submitted to the FDA, and all found significantly higher…”
Optimizing randomized trial designs to distinguish which subpopulations benefit from treatment. (2011, positive): “In scenarios 1a and 1b, we use data generating distributions for each subpopulation constructed to reflect what was seen in the meta-analysis of Kirsch et al. (2008), i.e. no effect of the treatment compared to placebo in those with moderate initial depression, but a mean improvement r > 0…”
Acceptance‐based interventions for the treatment of chronic pain: A systematic review and meta‐analysis (2011, neutral): “In several recent, large meta-analyses on the effects of psychotherapy and pharmacotherapy, it has been found that lower effects were found in higher-quality studies [13,14,23,37].”
The Personalized Advantage Index: Translating Research on Prediction into Individualized Treatment Recommendations. A Demonstration (2014, neutral): “4 [20].”
The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration (2009, methodology): “Therefore, we present analyses only on data for medications for which complete clinical trials’ change was reported” (147).”
Causal Effects In Psychotherapy: Counterfactuals Counteract Overgeneralization (2010, methodology): “( 2008 ) used metaregression, they only considered one moderator of treatment effect, namely the initial severity (with higher effects for the more severe cases).”