BACKGROUND: Although the role of the vascular component in the pathophysiology of Parkinson\'s disease is widely accepted and retinal vascular abnormalities are commonly observed in Parkinson\'s disease patients, evidence connecting retinal vascular disorders with the risk of developing Parkinson\'s disease is limited. We aimed to investigate the association between retinal vascular occlusion (RVO) and the risk of developing Parkinson\'s disease in people over 60 years using a nationwide cohort.
METHODS: From the 14-year South Korean National Health Insurance Service-Senior cohort, 11 210 incident RVO patients and 11 210 propensity scores, risk-matched controls were included. The incidence of Parkinson\'s disease was estimated with a Poisson regression. A Cox proportional hazards regression model was used to investigate the associations between RVO and the risk of Parkinson\'s disease.
RESULTS: The incidence of Parkinson\'s disease was 664.4 cases per 100 000 person-years (95% confidence interval [CI], 599.7-736.0) in the RVO cohort. Individuals with RVO had an increased incidence of Parkinson\'s disease (hazard ratio [HR], 1.28; 95% CI: 1.10-1.49). Increased PD risk was predominantly observed in retinal artery occlusion patients (HR, 1.53; 95% CI: 1.11-2.12), male patients (HR, 1.67; 95% CI: 1.29-2.17), and 5 years after diagnosis (HR, 1.46; 95% CI: 1.10-1.93).
CONCLUSIONS: Our findings suggest that a common pathophysiological pathway, such as vasculature changes, may exist between RVO and Parkinson\'s disease. RVO may be one of the risk factors associated with future development of Parkinson\'s disease. The nature of this association warrants further investigation.

In observational studies with a survival outcome, treatment initiation may be time dependent, which is likely to be affected by both time-invariant and time-varying covariates. In situations where the treatment is necessary for the study population, all or most subjects may be exposed to the treatment sooner or later. In this scenario, the causal effect of interest is the delay in treatment reception. A simple comparison of those receiving treatment early vs. those receiving treatment late might not be appropriate, as the timing of the treatment reception is not randomized. Extending Lu\'s matching design with time-varying covariates, we propose a propensity score matching strategy to estimate the treatment delay effect. The goal is to balance the covariate distribution between on-time treatment and delayed treatment groups at each time point using risk set matching. Our simulation study shows that, in the presence of treatment delay effects, the matching-based analyses clearly outperform the conventional regression analysis using the naive Cox proportional hazards model. We apply this method to study the treatment delay effect of 17 alpha-hydroxyprogesterone caproate (17P) for patients with recurrent preterm birth.