IMPACT OF MODEL CHOICE WHEN STUDYING THE RELATIONSHIP BETWEEN BLOOD PRESSURE VARIABILITY AND RISK OF STROKE

Objective: Long-term blood pressure variability (BPV), an increasingly recognized vascular risk factor, is challenging to define and to analyze. In order to take into account two major methodological issues - conditioning on the future and uncertainty around BPV value - we compared different models on the risk of stroke in a large dataset Design and method: We used data from the PROGRESS trial, a secondary stroke prevention trial which included 6105 subjects followed-up during 4 years. The median number of BP measurements was 12 and 727 strokes occurred. We compared a naive Cox model including BPV as a fixed covariate calculated on the entire follow-up, to other models aimed at avoiding conditioning on the future and taking into account uncertainty around BPV value. Results: We found that BPV was associated with an increased risk of stroke when using a naive Cox Model (HR = 1.19, 95% CI: 1.10 – 1.30) but not when using other models dealing more appropriately with the issue of conditioning on the future. Conclusions: These results illustrate that the methods used to estimate the association between BPV and stroke may affect the estimates and that more appropriate models tend to reduce this association.