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Abstract
Mental health researchers and clinicians have long sought answers to the question
"What works for whom?" The goal of precision medicine is to provide evidence-based
answers to this question. Treatment selection in depression aims to help each individual
receive the treatment, among the available options, that is most likely to lead to
a positive outcome for them. Although patient variables that are predictive of response
to treatment have been identified, this knowledge has not yet translated into real-world
treatment recommendations. The Personalized Advantage Index (PAI) and related approaches
combine information obtained prior to the initiation of treatment into multivariable
prediction models that can generate individualized predictions to help clinicians
and patients select the right treatment. With increasing availability of advanced
statistical modeling approaches, as well as novel predictive variables and big data,
treatment selection models promise to contribute to improved outcomes in depression.