Exploring Heterogeneity in Cost-Effectiveness Using Machine Learning Methods: A Case Study Using the FIRST-ABC Trial.

OBJECTIVE: The aim of this study was to explore heterogeneity in the cost-effectiveness of high-flow nasal cannula (HFNC) therapy compared with continuous positive airway pressure (CPAP) in children following extubation. DESIGN: Using data from the FIRST-line support for Assistance in Breathing in Children (FIRST-ABC) trial, we explore heterogeneity at the individual and subgroup levels using a causal forest approach, alongside a seemingly unrelated regression (SUR) approach for comparison. SETTINGS: FIRST-ABC is a noninferiority randomized controlled trial (ISRCTN60048867) including children in UK paediatric intensive care units, which compared HFNC with CPAP as the first-line mode of noninvasive respiratory support. PATIENTS: In the step-down FIRST-ABC, 600 children clinically assessed to require noninvasive respiratory support were randomly assigned to HFNC and CPAP groups with 1:1 treatment allocation ratio. In this analysis, 118 patients were excluded because they did not consent to accessing their medical records, did not consent to follow-up questionnaire or did not receive respiratory support. MEASUREMENTS AND MAIN RESULTS: The primary outcome of this study is the incremental net monetary benefit (INB) of HFNC compared with CPAP using a willingness-to-pay threshold of £20,000 per QALY gain. INB is calculated based on total costs and quality adjusted life years (QALYs) at 6 months. The findings suggest modest heterogeneity in cost-effectiveness of HFNC compared with CPAP at the subgroup level, while greater heterogeneity is detected at the individual level. CONCLUSIONS: The estimated overall INB of HFNC is smaller than the INB for patients with better baseline status suggesting that HFNC can be more cost-effective among less severely ill patients.

Heterogeneity within the Oregon Health Insurance Experiment: An application of causal forests.

Existing evidence regarding the effects of Medicaid expansion, largely focused on aggregate effects, suggests health insurance impacts some health, healthcare utilization, and financial hardship outcomes. In this study we apply causal forest and instrumental forest methods to data from the Oregon Health Insurance Experiment (OHIE), to explore heterogeneity in the uptake of health insurance, and in the effects of (a) lottery selection and (b) health insurance on a range of health-related outcomes. The findings of this study suggest that the impact of winning the lottery on the health insurance uptake varies among different subgroups based on age and race. In addition, the results generally coincide with findings in the literature regarding the overall effects: lottery selection (and insurance) reduces out-of-pocket spending, increases physician visits and drug prescriptions, with little (short-term) impact on the number of emergency department visits and hospital admissions. Despite this, we detect quite weak evidence of heterogeneity in the effects of the lottery and of health insurance across the outcomes considered.