Identifying treatment effects of an informal caregiver education intervention to increase days in the community and decrease caregiver distress: a machine-learning secondary analysis of subgroup effects in the HI-FIVES randomized clinical trial.
Journal:
Trials
PMID:
32059687
Abstract
BACKGROUND: Informal caregivers report substantial burden and depressive symptoms which predict higher rates of patient institutionalization. While caregiver education interventions may reduce caregiver distress and decrease the use of long-term institutional care, evidence is mixed. Inconsistent findings across studies may be the result of reporting average treatment effects which do not account for how effects differ by participant characteristics. We apply a machine-learning approach to randomized clinical trial (RCT) data of the Helping Invested Family Members Improve Veteran's Experiences Study (HI-FIVES) intervention to explore how intervention effects vary by caregiver and patient characteristics.
Authors
Keywords
Age Factors
Aged
Aged, 80 and over
Caregivers
Cognitive Dysfunction
Data Interpretation, Statistical
Depression
Family
Female
Follow-Up Studies
Humans
Institutionalization
Long-Term Care
Machine Learning
Male
Middle Aged
Quality of Life
Randomized Controlled Trials as Topic
Stress, Psychological
Treatment Outcome
Veterans