Cecilia Cotton | Searcharxiv

arXiv2026

Doubly robust Methods for Recurrent Event Outcomes: Causal Effects of Blood Pressure Medications on Acute Kidney Injuries

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.

stat.ME↗

arXiv2019

Causal inference with recurrent data via inverse probability treatment weighting method (IPTW)

Propensity score methods are increasingly being used to reduce estimation bias of treatment effects for observational studies. Previous research has shown that propensity score methods consistently estimate the marginal hazard ratio for time to event data. However, recurrent data frequently arise in the biomedical literature and there is a paucity of research into the use of propensity score methods when data are recurrent in nature. The objective of this paper is to extend the existing propensity score methods to recurrent data setting. We illustrate our methods through a series of Monte Carlo simulations. The simulation results indicate that without the presence of censoring, the IPTW estimators allow us to consistently estimate the marginal hazard ratio for each event. Under administrative censoring regime, the stabilized IPTW estimator yields biased estimate of the marginal hazard ratio, and the degree of bias depends on the proportion of subjects being censored. For variance estimation, the naïve variance estimator often tends to substantially underestimate the variance of the IPTW estimator, while the robust variance estimator significantly reduces the estimation bias of the variance.

stat.ME↗