Combining data sources to identify effect moderation for personalized mental health treatment

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Project Summary Determining “what works for whom” is a key goal in prevention and treatment across a variety of areas, including mental health. By understanding which individuals benefit most from which treatments we have the possibility of directing scarce resources to those who will most benefit, and of reducing the “churn” of individuals attempting multiple treatments before finding the one that works for them. Identifying effect moderators—factors that relate to the size of treatment effects--is crucial for delivery of treatment and prevention interventions, but doing so is incredibly difficult using standard study designs. Randomized trials, the gold standard for estimating average effects, are typically under-powered to detect moderation. Large-scale non- experimental studies may provide another way to examine effect moderation, but can suffer from confounding. New methods are needed to best harness the data available to learn how to personalize mental health treatments. This work will synthesize, extend, and apply methods for identifying effect moderators when multiple studies are available, with a particular focus on the complexities in mental health research. The methods will apply broadly and will be illustrated in an example estimating the effects of medication treatment for schizophrenia, using data from 11 randomized controlled trials and non-experimental data from the Duke University Health System electronic health record. The work will: 1) Extend moderation methods for scenarios with multiple randomized experiments, 2) Develop methods for using data from combined datasets with both experimental and non-experimental designs to identify effect moderation, and 3) Disseminate the methods to mental health researchers. By developing methods to take full advantage of both experimental and non-experimental data this work has the potential to move towards personalized mental health, thus improving how we prevent and treat mental health challenges in the population.