Deep vein thrombosis (DVT) of the lower limb is characterised by the formation of abnormal blood clots in deep veins of lower extremity. Changes in blood flow have been associated with an increased risk of thrombus development. Understanding the relationship between variable venous anatomy and haemodynamics can reveal insights to support clinical decision-making processes. The purpose of this study was to combine statistical shape modelling (SSM) - to analyse venous shape - and computational fluid dynamics (CFD) - to estimate blood flow - in the common iliac vein to demonstrate the feasibility of a combined framework to support the treatment of DVT. Principal geodesic analysis was used to extract dominant shape modes from a set of 24 venous shapes in 2D: 8 patient-specific extracted from standard angiograms and 16 synthetic complementing the set. Steady-state CFD simulations were run on the associated 3D geometries. Low wall shear stress distributions below three thresholds ( \(<0.15, <0.10, <0.05 Pa\) ) were the haemodynamic risk metrics of choice. The distribution of CFD output metrics was evaluated using the three most dominant shape modes from PGA and compared to the three modes that showed the strongest correlation with the CFD metrics, illustrating that they are not the same. The study demonstrated the feasibility of combining SSM and CFD to examine the importance of shape variability and inflow in a local region of the venous circulation. It will serve as a basis for extended work on a larger set of venous shapes extracted from standard medical images.