Establishing the Need for Anticipatory Symptom Guidance and Networked Models of Disease in Adaptive Family Management Among Children With Medical Complexity: Qualitative Study

BACKGROUND

Caregivers of children with medical complexity navigate complex family management tasks for their child both in the hospital and home-based setting. The roles and relationships of members of their social network and the dynamic evolution of these family management tasks have been underexamined.

OBJECTIVE

The purpose of this study was to explore the structures and processes of family management among caregivers of children with medical complexity, with a focus on the underlying dynamic nature of family management practices and the role of members of their social network.

METHODS

This study used a qualitative approach to interview caregivers of children with medical complexity and members of their social network. Caregivers of children with medical complexity were recruited through an academic Children's Hospital Complex Care Clinic in the mid-Atlantic region and interviewed over a period of 1 to 3 days. Responses were analyzed using constructivist grounded theory and situational analysis to construct a new conceptual model. Only caregiver responses are reported here.

RESULTS

In total, 20 caregivers were included in this analysis. Caregiver perspectives revealed the contextual processes that allowed for practices of family management within the setting of rapidly evolving symptoms and health concerns. The dynamic and adaptive nature of this process is a key underlying action supporting this novel conceptual model. The central themes underpinning the adaptive family management model include symptom cues, ongoing surveillance, information gathering, and acute on chronic health concerns. The model also highlights facilitators and threats to successful family management among children with medical complexity and the networked relationship among the structures and processes.

CONCLUSIONS

The adaptive family management model provides a basis for further quantitative operationalization and study. Previously described self- or family management frameworks do not account for the underlying dynamic nature of the disease trajectory and the developmental stage progression of the child or adolescent, and our work extends existing work. For future work, there is a defined role for technology-enhanced personalized approaches to home-based monitoring. Due to the disparities caregivers and the children in this population already experience, technology-enhanced approaches must be built alongside key stakeholders with an equity orientation to technology co-development.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/14810.

Natural cubic splines for the analysis of Alzheimer's clinical trials

Mixed model repeated measures (MMRM) is the most common analysis approach used in clinical trials for Alzheimer's disease and other progressive diseases measured with continuous outcomes over time. The model treats time as a categorical variable, which allows an unconstrained estimate of the mean for each study visit in each randomized group. Categorizing time in this way can be problematic when assessments occur off-schedule, as including off-schedule visits can induce bias, and excluding them ignores valuable information and violates the intention to treat principle. This problem has been exacerbated by clinical trial visits which have been delayed due to the COVID19 pandemic. As an alternative to MMRM, we propose a constrained longitudinal data analysis with natural cubic splines that treats time as continuous and uses test version effects to model the mean over time. Compared to categorical-time models like MMRM and models that assume a proportional treatment effect, the spline model is shown to be more parsimonious and precise in real clinical trial datasets, and has better power and Type I error in a variety of simulation scenarios.