OBECTIVE: We propose a data-matching approach to estimate intervention efficacy for randomised controlled trials (RCTs) when there is non-compliance to the allocated treatment with induced selection bias. STUDY DESIGN: and Settings: We considered a large RCT to compare healthcare costs and hospital length-of-stay 12 months post randomisation. Participants allocated to the intervention group were eligible to receive health-coaching and disease-management services. An opt-out approach was adopted for recruitment. Control-group participants received usual care but were allowed to opt-in to receive the intervention. Using "nearest-neighbour"-matched data, we identified compliant participants in both arms to estimate intervention efficacy. Results were compared with intention-to-treat (ITT), instrumental-variable (IV)-adjusted ITT, per-protocol (PP), and as-treated (AT) analyses. RESULTS: The ITT estimated an intervention effect of a 1.5% reduction in cost, but 56.7% of intervention-group participants did not receive health-coaching. The PP and AT found an increase in cost of 9.4% and 17.1%, respectively. The matching method estimated a 12.3% reduction in cost. After adjustment for baseline covariates, the intervention group had lower same-day admission cost (13.6%; 95% CI: 7.3%-20.0%; p<0.001) and shorter hospital stay (11.2%; 95% CI: 2.6%-19.9%; p=0.021). CONCLUSION: Opt-in/opt-out strategies in RCTs misled intervention comparisons and the matching approach improved estimation of intervention efficacy.