Assessment of the Impact of antihypertensive Medications on vascular and renal outcomes in Chronic Kidney Disease (AIM-CKD) - Title: Assessment of the Impact of antihypertensive Medications on vascular and renal outcomes in Chronic Kidney Disease (AIM-CKD) Abstract Chronic kidney disease (CKD), characterized by enduring damage to kidney function, poses a significant public health concern, impacting around 15% of the adult population in the United States. CKD patients commonly experience heightened adverse cardiovascular effects, which are associated with an elevated risk of mortality. Foundational treatment strategies for slowing CKD progression involve blood pressure-lowering agents, including renin-angiotensin system inhibitors and calcium channel blockers. These medications also exhibit potential vasodilatory and anti-inflammatory properties. Nevertheless, the impact of these therapies on cardiovascular disease outcomes in CKD patients is still in debate, and the tradeoff between risks and benefits remains unclear due to limited clinical trials conducted thus far. To address this knowledge gap, we intend to leverage data from two extensive studies: the Chronic Renal Insufficiency Cohort (CRIC) and the Systolic Blood Pressure Intervention Trial (SPRINT). Our goal is to estimate and compare the effects of medications on cardiovascular risks in CKD patients using novel and advanced causal inference methods. We recognize that analyzing real-world data poses statistical challenges (e.g., time-varying treatment administration, competing risk of death, treatment combination). However, these complexities cannot be directly addressed by current causal inference techniques or may be addressed inefficiently. Our proposal focuses on time-to-event outcomes, which accommodate detailed information, including the timing and duration of disease outcomes that are crucial for understanding the natural progression of the disease and assessing the potential effectiveness of interventions. Preliminary work has demonstrated the promise of our novel dynamic propensity trajectory matching (DPTM) techniques, which guarantee further evaluation through theoretical and extensive simulation studies conducted under various scenarios. Ultimately, we aim to develop user-friendly R software packages and a Shiny app to facilitate the broader use of our research findings for the benefit of the public.
