🤖 AI Summary
This study addresses the challenges in estimating the causal effect of antihypertensive strategies on recurrent acute kidney injury (AKI)—a recurrent event outcome—amid time-varying treatments, time-dependent confounding, and potential model misspecification. Leveraging data from the SPRINT trial, the authors propose a causal inference framework that integrates doubly robust estimation with adjustment for time-varying confounders, while explicitly accounting for medication adherence and death as a semi-competing risk. The approach effectively identifies the average causal effect of standard versus intensive blood pressure lowering on AKI recurrence. By combining model flexibility with robustness to misspecification, the method substantially enhances the reliability of causal conclusions and offers a novel paradigm for analyzing recurrent events in complex longitudinal settings.
📝 Abstract
Evaluating the average causal effects of treatment strategies on recurrent event outcomes, such as heart attacks or renal failure, is important in clinical and medical research. However, the analysis becomes increasingly complex as multiple interacting factors are considered within a longitudinal setting. In this paper, we use advanced methodologies to estimate the average causal effects of standard versus intensive blood pressure-lowering therapies on acute kidney injury recurrences. We address time-varying treatment and confounding, and model misspecification during the identification and estimation processes for the effect estimands. We analyze the Systolic Blood Pressure Intervention Trial data set using our proposed method, accounting for medication adherence and the semi-competing risk of death observed in the data.