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

Efficiently identifying the right treatment for the right patient can improve quality of healthcare for individuals and populations. Treatments for depression and schizophrenia are well documented to exhibit variable effectiveness because “effect moderators,” defined as known and unknown individual, disease-specific, genetic, environmental, and other characteristics that impact the effectiveness of treatments or exposures. Finding ways to identify and leverage effect moderators at the point of care to facilitate clinical decision-making would improve efficiency, quality, and outcomes of healthcare, including behavioral healthcare. Without cut-and-dried tests to provide definitive diagnoses, and in the setting of illnesses with heterogeneous presentations and response to treatments, the field of mental health faces unique challenges in the quest to determine “what works for whom” – and why. Identifying such effect moderators is crucial for personalized delivery of treatment and prevention interventions, but doing so is incredibly difficult using standard study designs. 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 and major depressive disorder, using data from 7 randomized controlled trials and non-experimental data from the Duke University and Johns Hopkins Health System electronic health record. The work will: 1) Extend moderation methods for scenarios with multiple randomized experiments and 2) Develop methods for using data from combined datasets with both experimental and non-experimental designs to identify effect moderation. 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.