Abstract
A major problem in mental health clinical trials, such as depression, is low assay sensitivity in primary outcome measures. This has contributed to clinical trial failures, resulting in the exodus of the pharmaceutical industry from the Central Nervous System space. This reduced assay sensitivity in psychiatry outcome measures stems from inappropriately broad measures, recall bias, and poor interrater reliability. Limitations in the ability of traditional measures to differentiate between the trait versus state-like nature of individual depressive symptoms also contributes to measurement error in clinical trials. In this viewpoint, we argue that ecological momentary assessment (EMA)—frequent, real time, in-the-moment assessments of outcomes, delivered via smartphone—can both overcome these psychometric challenges and reduce clinical trial failures by increasing assay sensitivity and minimizing recall and rater bias. Used in this manner, EMA has the potential to further our understanding of treatment response by allowing for the assessment of dynamic interactions between treatment and distinct symptom response.
Highlights
Mental health treatment development and testing has been at an impasse for the past several decades; our clinical trials increasingly fail more often than in other fields [1]
The central nervous system (CNS) drug development pipeline has become increasingly burdened with late-phase failures [4], contributing to a well-publicized exodus of the pharmaceutical industry from the CNS space
A recent study by Checkroud et al [27] of over 7000 patients with major depression demonstrates why this approach, as well as any other that relies on indiscriminate use all of the items in a scale to assess primary efficacy outcomes, may be a problem
Summary
Mental health treatment development and testing has been at an impasse for the past several decades; our clinical trials increasingly fail more often than in other fields [1]. A recent study by Checkroud et al [27] of over 7000 patients with major depression demonstrates why this approach, as well as any other that relies on indiscriminate use all of the items in a scale to assess primary efficacy outcomes (eg, the HAM-D), may be a problem In their study, they illustrate how this indiscriminate approach to measurement can jeopardize a potential treatment in late-phase clinical trials. The buprenorphine/samidorphan combination treatment, which failed to separate from placebo on the primary outcome measure of change from baseline on the MADRS-10 item scale, fared better in separating from placebo using the MADRS-6 item scale [34] These examples suggest a data reduction approach to symptom assessment focusing on core symptoms is more likely to accurately detect meaningful clinical response. We can confidently say that our current approach is suboptimal, fixing it will not be so easy
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