Trajectory Design and Analytic Methods for the Study of Human Response to Chronic Illness and Care Systems

Purpose: People with chronic illnesses and their families experience multiple symptoms and/or disabilities and their responses cross multiple measurement domains, from genetic, biologic, through psychosocial, behavioral, and environmental.  The Adaptive Leadership (AL) framework provides a compelling and effective conceptual lens through which to view the challenges that individuals face as it allows examination of phenomena that are multilevel, dynamic, unpredictable, and highly context dependent.  Research approaches that best address the vital questions in this field also require dynamic designs and analytic methods that allow us to identify patterns across levels of functioning and place a high value on the role and timing of dynamic interactions. The purpose of this paper is to present a range of trajectory design methods that can be used in synergy with the AL framework for the study of chronic illness and care systems.

Methods: We will describe key trajectory design methods that cross levels of analysis capturing interactions between genetic, physiologic, behavioral, and environmental factors

Results: These methods, including person-oriented, case-based, longitudinal mixed-method designs capture stability, change, and development. They are useful for complex, inhomogeneous, and multifaceted datasets that are often generated in studies of humans in chronic illness situations. Data inhomogeneity in this field is created because the phenomena of interest often requires various temporal and spatial scales, which cross a range of responses, and include both textual and numerical data that may be either continuous or discrete. In contrast to variable-oriented approaches, these analysis methods search for patterns, trends, or typologies in trajectories of response. Visualization is a key component of these novel methods in which patterns are identified across trajectories of data around a case (individual, dyad, system).

Conclusion: Trajectory design methods allow for the exploration of data, create portraits of response, and enhance insights to generate hypotheses.