Combining causal inference and within-trial economic evaluation methods to assess comparative cost-effectiveness using real-world data: a tutorial with recommendations based on the quasi-experimental ADAPT study of a redesigned mental health service

Abstract

Abstract OBJECTIVES. Real-world evidence is playing an increasingly important role in health technology assessment, but is prone to selection and confounding bias. We demonstrate how to conduct a real-world within-study cost per quality-adjusted life-year (QALY) analysis. We combined traditional within-trial bootstrapped regression-baseline-adjustment with causal inference methods, using a Target Trial framework, inverse probability weights (IPWs), marginal structural models (MSMs), and g-computation, applied to England’s Talking Therapies for anxiety and depression services (TTad) mental-health e-records. METHODS.The ‘Assessing a Distinct IAPT service’ (ADAPT) quasi-experimental-study evaluated an Enhanced-TTad-service Vs. TTad-services’ treatment-as-usual. TTad-services collect patient-reported PHQ-9-depression and GAD-7-anxiety scores at index-assessment and each treatment session, from which we predicted EQ-5D utilities using a mapping function. Our primary estimands were incremental costs and QALYs for Enhanced-TTad Vs. treatment-as-usual at 16-weeks post-TTad-service-index-assessment. We prespecified our target trial including eligibility, treatment strategies, assignment procedure, follow-up, outcomes, estimands, and analysis plan. We used stabilised treatment-related and censoring-related IPWs within MSMs to reduce selection and confounding bias due to non-randomised treatment allocation and informative censoring, respectively. Our doubly-robust approach involved MSM-adjusted baseline confounders and g-computation to estimate incremental utilities, costs, and QALYs, with bootstrapped bias-corrected 95% confidence-intervals (95%bCIs) and cost-effectiveness acceptability curves. RESULTS. Primary analysis sample: Enhanced, N=5,441; treatment-as-usual, N=2,149. Naïve regression-baseline-adjustment and doubly-robust approaches suggested Enhanced-TTad-service dominated treatment-as-usual, with average per-person (95%bCIs) cost-savings of £30.64 (£22.26 to £38.90) or £29.64 (£20.69 to £37.99) and QALYs-gained of 0.00035 (-0.00075 to 0.00152) or 0.00052 (-0.00105 to 0.00277), respectively; probability of cost-effectiveness at £30,000 per QALY was 99% or 95%, respectively. The doubly-robust and naïve results concurred; albeit, the doubly-robust results suggested average QALY gains were higher but less certain. The cost-effectiveness results were driven by potential cost-savings. CONCLUSION. When treatment allocation is non-randomised, the Target Trial framework alongside doubly-robust analyses should be used to reduce selection and confounding bias.