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Background: Response to antidepressants in major depressive disorder is variable and determinants are not well understood or used to design clinical trials. We aimed to understand these determinants., Methods: Supported by Innovative Medicines Initiative, as part of a large public-private collaboration (NEWMEDS), we assembled the largest dataset of individual patient level information from industry sponsored randomized placebo-controlled trials of antidepressant drugs in adults with MDD. We examined patient and trial-design-related determinants of outcome as measured by change on Hamilton Depression Scale or Montgomery-Asberg Depression Rating Scale in 34 placebo-controlled trials (drug, n = 8260; placebo, n = 3957)., Results: While it is conventional for trials to be 6-8 weeks long, drug-placebo differences were nearly the same at week 4 as at week 6 and with lower dropout rates. At the multivariate level, having any of these attributes was significantly associated with greater drug vs. placebo differences on symptom improvement: female, increasing proportion of patients on placebo, centers located outside of North America, centers with low placebo response (regardless of active treatment response) and using randomized withdrawal designs., Limitations: Data on compounds that failed were not available to us. Findings may not be relevant for new mechanisms of action., Conclusions: Proof of concept trials can be shorter and efficiency improved by selecting enriched populations based on clinical and demographic variables, ensuring adequate balance of placebo patients, and carefully selecting and monitoring centers. In addition to improving drug discovery, patient exposure to placebo and experimental treatments can be reduced., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Several often-cited meta-analyses have reported that the efficacy of antidepressant medications depends on the severity of depression. They found that drug-placebo differences increased as a function of initial severity, which was attributed to decreased responsiveness to placebo among patients with severe depression rather than to increased responsiveness to medication. We retested this using patient-level data and also undertaking a meta-analysis of trial-level data from 34 randomised placebo controlled trials (n = 10 737) from the NEWMEDS registry. Although our trial-level data support prevous findings, patient-level data did not show any significant effect of initial depression severity on drug v. placebo difference., (© The Royal College of Psychiatrists 2016.)

Outcomes in RCT's of antipsychotic medications are often examined using last observation carried forward (LOCF) and mixed effect models (MMRM), these ignore meaning of non-completion and thus rely on questionable assumptions. We tested an approach that combines into a single statistic, the drug effect in those who complete trial and proportion of patients in each treatment group who complete trial. This approach offers a conceptually and clinically meaningful endpoint. Composite approach was compared to LOCF (ANCOVA) and MMRM in 59 industry sponsored RCT's. For within study comparisons we computed effect size (z-score) and p values for (a) rates of completion, (b) symptom change for complete cases, which were combined into composite statistic, and (c) symptom change for all cases using last observation forward (LOCF). In the 30 active comparator studies, composite approach detected larger differences in effect size than LOCF (ES=.05) and MMRM (ES=.076). In 10 of the 49 comparisons composite lead to significant differences (p ≤ .05) where LOCF and MMRM did not. In 3 comparisons LOCF was significant, in 2 MMRM lead to significant differences whereas composite did not. In placebo controlled trials, there was no meaningful difference in effect size between composite and LOCF and MMRM when comparing placebo to active treatment, however composite detected greater differences than other approaches when comparing between active treatments. Composite was more sensitive to effects of experimental treatment vs. active controls (but not placebo) than LOCF and MMRM thereby increasing study power while answering a more relevant question., (© 2013 Published by Elsevier B.V. and ECNP.)

Background: Negative symptoms are an important target for intervention in schizophrenia. There is lack of clarity in defining appropriate patients for negative symptom trials. While regulators, drug developers and academics have expressed positions in this regard, the implications of these definitions are not yet tested in large-scale trials and there is no consensus., Objectives: We examined the extent to which various operational criteria for inclusion in negative symptoms in schizophrenia clinical trials can impact patient selection and examined the effectiveness of second generation antipsychotics (SGAs) in patients with various degrees of negative symptoms., Method: Using anonymized patient data from AstraZeneca, Janssen Pharmaceuticals, Eli Lilly, Lundbeck, and Pfizer from 20 placebo-controlled trials of SGAs in schizophrenia from the NewMeds repository, we applied different criteria for negative symptoms: prominent, predominant, and EMA criteria, which require predominant and core negative symptoms to be present and examined the impact of these on inclusion and outcome., Results: Operational criteria for negative symptoms in trials vary greatly in their inclusion of patients from "typical" trial samples. Of the patients in our studies, 8.1% and 62.3% met criteria for prominent negative symptoms, 10.2% to 50.2% met criteria for predominant negative symptoms and 7.6% to 40.0% met EMA criteria at baseline. After 6weeks of active treatment, 8% and 33.1% of patients met criteria for prominent residual negative symptoms and 14.9% to 65% met criteria for prominent and 12.2% to 45.5% met EMA criteria. Patients with predominant or prominent negative symptoms showed marked improvement on second generation antipsychotics., Conclusions: Applying various operational criteria for selecting patients for negative symptoms trials provides a great variability in percentage of suitable patients calling into question the extent to which some definitions may be overly narrow., (Copyright © 2013 Elsevier B.V. All rights reserved.)