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Abstract:
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The individualization of treatment of chronic disorders requires accounting for variations in external conditions, state of patient, and his/her response to treatment over time. Typically, the changes are not identical across the members of the treated population. The approach towards analysis of the factors influencing the changes in the treatment response derives from the principles described in our works (2011, 2012, 2015, 2018, 2019). Assumptions, logic, algorithm and computational aspects of identifying and analysis of subgroups and time segments are discussed. The treatment process is modeled as a sequence of time increments (cycles). Each cycle designates a momentary state described with a set of variables including treatment, outcome, internal and external environment (conditions). The method operates with a set of categories including a “treatment-outcome complex,” “sensitivity to treatment,” “spontaneous recovery,” “aggregation,” etc. It can apply to analysis of longitudinal data to explore heterogeneity of the population and diversity of the factors affecting treatment response during the treatment process.
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