Exploring treatment effect heterogeneity of a PROMs alert intervention in knee and hip arthroplasty patients: A causal forest application

Abstract

Patient reported outcome measures (PROMs) experience an uptake in use for hip (HA) and knee arthroplasty (KA) patients. As they may be used for patient monitoring interventions, it remains unclear whether their use in HA/KA patients is effective, and which patient groups benefit the most. Nonetheless, knowledge about treatment effect heterogeneity is crucial for decision makers to target interventions towards specific subgroups that benefit to a greater extend. Therefore, we evaluate the treatment effect heterogeneity of a remote PROM monitoring intervention that includes ∼8000 HA/KA patients from a randomized controlled trial conducted in nine German hospitals. The study setting gave us the unique opportunity to apply a causal forest, a recently developed machine learning method, to explore treatment effect heterogeneity of the intervention. We found that among both HA and KA patients, the intervention was especially effective for patients that were female, >65 years of age, had a blood pressure disease, were not working, reported no backpain and were adherent. When transferring the study design into standard care, policy makers should make use of the knowledge obtained in this study and allocate the treatment towards subgroups for which the treatment is especially effective.