Estimating the effects of treatment regimes over the course of chronic disease: A multi-state causal framework with baseline confounding

Abstract

The development of chronic disease is a long-term process involving multiple endpoints. Although multi-state Cox models can estimate state-specific survival risks over time, they are not well suited for comparing the effectiveness of treatment regimes. A discrete-time split-state framework has been proposed, which divides disease states into substates by conditioning on past history. As this framework is both “memoryless” and “memorable,” the transition rates can be synthesized into summary measures, multimorbidity-adjusted life year (MALY) and substate-specific life year (SSLY). Building on this framework, we propose to investigate the causal effects of static and dynamic treatment regimes over the disease course under the assumptions of constant baseline confounders and instantaneous effects of interventions on transition rates. We identify the optimal treatment regime as the one that maximizes MALY and use SSLY to elucidate the mechanisms of how treatments influence disease progression. In the application, we identified the optimal weight targets in the ARIC study by modeling the disease course in healthy, at-metabolic-risk, coronary heart disease, heart failure, and mortality states. The estimated MALY was 1.80 years higher (95% CI: 0.62, 2.78) under regime “Normal weight initially and change to overweight if age >65 y” compared to regime “Normal weight across all states.” SSLY decomposition indicates that this gain arises from increased life year in all substates except the healthy state. In summary, our method provides a framework to evaluate the health benefits of treatment regimes over the disease course and has the potential to improve the precision prevention of chronic diseases.