Objective: Visit-to-visit blood pressure variability (BPV) has been identified in several recent studies as a risk factor for stroke, independently of the level of BP. If true, this finding could help to identify patients at higher risk of stroke and to develop new preventive strategies. However, studies on BPV are exposed to important methodological challenges including conditioning on the future, which violates the assumption of the Cox model, using endogenous variables like the expected BP and its variance, and not handling properly the measurement error. Our objective was to address these issues by designing a joint model with heterogeneous variance for the repeated measures of BP, allowing us to analyze the association between BPV and the risk of stroke and the competing risk of death. Design and Method: We used data from PROGRESS (Perindopril Protection against Stroke Study), a placebo-controlled BP-lowering randomized trial for the prevention of stroke recurrence in 6105 patients with a previous history of stroke or TIA. During 5 years of follow-up, BP was measured 12 times, and 727 strokes and 438 non-stroke deaths were observed. We designed an innovative and dedicated joint model combining a linear mixed model, including a subject-specific random effect for the residual variance and two proportional hazard models for the two competing risks of stroke and death. In this model, the risk of each event may depend on the current value and current slope of the individual expected trajectory of BP as well as the intra-subject residual variance. Results: In separate analysis of each arm of the trial, we found that the risk of stroke increased with the current level of BP (HR = 1.13 per 5 mmHg, 95%CI = [1.08; 1.17], p-value < 0.001) but was not associated with its intra-individual variability (HR = 0.89, 95%CI = [0.71; 1.12], p-value = 0.317). Conversely, the risk of non-stroke death increased with the intra-individual variability of BP (HR = 1.43, 95%CI = [1.08; 1.88], p-value = 0.011) but not with its current level (HR = 1.03, 95%CI = [0.97; 1.09], p-value = 0.317). Results were similar on the complete sample adjusting for the randomized treatment. Conclusion: Using appropriate modeling accounting for measurement error and limiting selection and other potential biases, we found a differential effect of expected BP and BPV on the risk of stroke and of death. These results, if confirmed and enlarged to other populations, would have important consequences to design efficient and individualized preventive strategies.< Leer menos