“Objective: Generate evidence for the comparative effectiveness for each pairwise comparison of depression treatments for a set of outcomes of interest.”

Mark Tuttle points us to this project by Martijn Schuemie and Patrick Ryan:

Large-Scale Population-Level Evidence Generation

Objective: Generate evidence for the comparative effectiveness for each pairwise comparison of depression treatments for a set of outcomes of interest.

Rationale: In current practice, most comparative effectiveness questions are answered individually in a study per question. This is problematic because the slow pace at which evidence is generated, but also invites reporting and publishing only those studies where the result is ‘statistically significant’, leading to an underestimation of the true number of tests performed when correcting for multiple testing. This process is known as publication bias. Moreover, these studies typically do not include the evidence needed to interpret the study results, such as empirical estimates of residual bias inherent to the study design and data used. A solution to these problems is to perform a large set of comparative effectiveness analyses in one study, where each analysis adheres to current best practices. One of these best practices that we’ll follow is to use large scale propensity models to adjust for confounding. Another best practice that this study will follow is that each analysis will include a large set of negative and positive control outcomes (outcomes that are respectively not known or known to be cause by one exposure more than the other). In this study we would like to demonstrate the feasibility of generating population-level estimates at scale by focusing on on disease: depression. We perform every possible pairwise comparison between depression treatments for a large set of outcomes of interest. Most of these outcomes are generic safety outcomes, but some outcomes are related more specifically to the effectiveness of antidepressant treatment.

I don’t know anything about depression treatments, so all I have to offer is the general suggestion of analyzing all these comparisons together using a multilevel model, as discussed in this 2011 paper with Hill and Yajima. Then you get all the comparisons automatically.