Continuous‐Time Causal Inference With Marked Point Process Weights: An Example on Sodium‐Glucose Co‐Transporters 2 Inhibitor Medications and Urinary Tract Infection

ABSTRACT Treatment‐confounder feedback is present in time‐to‐recurrent or longitudinal event analysis when time‐dependent confounders are themselves influenced by previous treatments. Conventional models produce misleading statistical inference of causal effects due to conditioning on these factors on the causal pathway. Marginal structural models are often applied to quantify the causal treatment effect, estimated using longitudinal weights that mimic the randomization procedure by balancing the covariate distributions across the treatment groups. The weights are usually constructed in discrete time intervals, which is appropriate if the follow‐up visits are scheduled and regular. However, in primary care, visit times can be irregular and informative, and the treatment history consists of duration and doses. This can be modeled through a continuous‐time marked point process. We constructed a continuous‐time marginal structural model to estimate the effect of cumulative exposure to Sodium‐Glucose co‐Transporters 2 Inhibitor (SGLT‐2i) medications on time‐to‐recurrent urinary tract infection (UTI). We used a cohort of type II diabetes patients with chronic kidney disease and constructed a marked point process that characterized the recurrent flare episodes of primary care visits (i.e., point process) with marks for the multinominal dose (none, low, high) of SGLT‐2i medications and recurrent episodes of UTI. We applied the stabilized and optimal treatment weights to estimate the hypothesized causal effect. Our results are concordant with earlier findings in which the recurrent episodes of UTI did not increase when patients were prescribed low dose or high dose of SGLT‐2i medications.