ABSTRACT
Depression is a highly heterogeneous syndrome. Homogeneous subtypes according to symptomatology of illness may contribute to development of individualized treatment, assessment on outcomes and prognosis. Latent class analysis is a flexible statistical approach to determine classes with similar symptom profiles in a heterogeneous group, which has been widely used in data-driven subtyping of depression to increase accuracy of subtyping. This article reviewed existing symptom-based subtypes of depression and findings of researches on latent class analysis based illness subtyping.
ABSTRACT
Depression is a highly heterogeneous syndrome. Homogeneous subtypes according to symptomatology of illness may contribute to development of individualized treatment, assessment on outcomes and prognosis. Latent class analysis is a flexible statistical approach to determine classes with similar symptom profiles in a heterogeneous group, which has been widely used in data-driven subtyping of depression to increase accuracy of subtyping. This article reviewed existing symptom-based subtypes of depression and findings of researches on latent class analysis based illness subtyping.